tag:blogger.com,1999:blog-84579902007667390162024-03-17T23:03:53.124-04:00Engineering Computation LaboratoryArtificial intelligence, computational design, cyber-physical systems, personalized learningCharles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.comBlogger260125tag:blogger.com,1999:blog-8457990200766739016.post-941040388067800952021-05-09T11:28:00.000-04:002021-05-09T11:28:12.321-04:00Osmosis: We can simulate it, but do we really get it?<div dir="ltr" style="text-align: left;" trbidi="on">
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Computer simulations are useful for developing conceptual understanding of science ideas that are otherwise obscure. However, there are circumstances that simulations just raise more questions than what they answer. <a href="http://en.wikipedia.org/wiki/Osmosis">Osmosis</a> is one of those deceptively simple phenomena that turn out to be quite challenging to understand, even if it can be simulated with reasonable clarity.</div>
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<tr><td class="tr-caption" style="text-align: center;">Figure 1. Osmotic pressure.<br />
(Image from <a href="http://en.wikipedia.org/wiki/Osmosis">Wikipedia</a>)</td></tr>
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Osmosis is a process in which solvent molecules move -- <b>without input of energy</b> -- across a semipermeable membrane (which let only the solvent molecules pass) separating two solutions of different concentrations. A typical explanation is that the solvent molecules "want" to equalize the solute concentrations (or equivalently, the solvent concentrations) on the two sides. As a result, the solutions must build up persistent pressure that causes the liquid level in the left side of the U-shape tube shown in Figure 1 to rise remarkably higher against gravity.<br />
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Most people just walk away with this theory. But where exactly does the energy that elevates the liquid come from?<br />
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In the U-shape tube, no one exerts any force on the liquid in either side, while the force of gravity keeps the liquid level as low as possible. Somehow, by simply making the membrane in the middle partially permeable to only the solvent, some energy is extracted to do the heavy lifting. This process can be simulated using molecular dynamics as is shown in the YouTube video posted above. The simulation shows that, on average, the middle column eventually maintained a noticeably higher level. Removing the solute (the green particles) returned the liquid levels in the three columns to about the same.<br />
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In this simulation, the green particles and the blue particles have comparable chemical affinities, meaning that the blue particles do not particularly favor their kins around. Neither do the green particles. The white particles that represent the membrane molecules have very weak interactions with both blue and green particles.<br />
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Here is <a href="http://www.concord.org/~qxie/mwapplet/osmosis1.html">the link to the simulation</a> (which is a Java applet). Happy New Year!</div>
Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com0tag:blogger.com,1999:blog-8457990200766739016.post-75306734703814695592018-11-21T13:10:00.001-05:002018-11-22T15:17:22.593-05:00The SmartIR Gallery<div dir="ltr" style="text-align: left;" trbidi="on">
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<tr><td class="tr-caption" style="text-align: center;">Fig. 1: Thermal images in the Gallery</td></tr>
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Gallery is a tool within the SmartIR app for users to manage their own thermal images, temperature graphs, and other data within the app. Unlike the default gallery in the operating system, SmartIR Gallery shows only things related to thermal imaging and provides image processing functions for analyzing thermal images and videos.<br />
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<tr><td class="tr-caption" style="text-align: center;">Fig. 2: Image processing in the Gallery</td></tr>
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The gallery can be opened from the action bar of the SmartIR app. Once launched, it displays all the files that the user has collected through the app and stored on the device in a grid view (Figure 1). Tapping an image within the grid will display an enlarged view of the image in a popup window (by the way, the enlarged image in Figure 1 shows the liquid level of a propane tank revealed by the effect of cooling due to evaporation that feeds the gaseous fuel to the pipeline). Within the window, the user can swipe to the left or right, or tap the left or right arrow buttons, to browse the images.
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<tr><td class="tr-caption" style="text-align: center;">Fig. 3: Temperature graphs in the Gallery</td></tr>
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Based on OpenCV, SmartIR Gallery provides a number of image analysis tools for the user to quickly process thermal images. For instance, the user can blur, sharpen, invert, posterize, and adjust the brightness and contrast of an image (Figure 2). In the left image shown in Figure 2, sharpening a thermal image makes the view of the house more pronounced than the default view rendered by the FLIR ONE thermal camera. Inverting the thermal image, as shown in the middle image of Figure 2, may be a quick way to show how a heated house in the winter might look like in the opposite thermal condition such as an air-conditioned house in a hot summer day. In the right image shown in Figure 2, posterizing the thermal image creates an artistic view. More OpenCV tools will be added in the future to extend SmartIR's thermal vision capacity.<br />
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SmartIR Gallery also allows the user to filter the image files. For instance, the user can choose to show only the graphs, which are screenshot images taken from the temperature chart of SmartIR (Figure 3). Using the Share button, the user can easily send the displayed image to other apps such as an email app or a social network app.<br />
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Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com2tag:blogger.com,1999:blog-8457990200766739016.post-13402786108241680112018-09-19T19:09:00.001-04:002018-09-22T10:19:10.364-04:00A Small Step towards a Big Dream of Infrared Street View<div dir="ltr" style="text-align: left;" trbidi="on">
The Infrared Street View is an award-winning project <a href="https://www.nrel.gov/news/press/2016/nrel-announces-innovation-challenge-winner-for-crowdsourced-residential-buildings-energy-efficiency-ideas.html">recognized</a> by the U.S. Department of Energy and subsequently <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1712676">funded</a> by the National Science Foundation. The idea is to create a thermal equivalent of Google's Street View that would serve as the starting point to develop a thermographic information system (i.e., the "Map of Temperature"). This is an ambitious goal that is normally only attainable through big investments from the Wall Street or by big companies like Google. However, as a single developer who doesn't have a lot of resources, I decided to give it a shot on my own. Being a non-Googler, I am counting on the citizen scientists out there to help me build the Infrared Street View. The first step is to create a free app so that they have a way to contribute.<br />
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My journey started in the mid of July 2018. In two months I have learned how to develop a powerful app from scratch. At the end, the Infrared Street View is coming into sight! This blog article shows some of the (imperfect but promising) results, as demonstrated in Figures 1 and 2.<br />
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<tr><td class="tr-caption" style="text-align: center;">Fig. 1: Panoramas in visible light and infrared light generated by SmartIR</td></tr>
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This milestone is about developing the functionality in the SmartIR app for creating infrared panoramas so that <i>anyone</i> who has a smartphone with an infrared camera attachment such as FLIR ONE could produce a panoramic image and contribute it to the Infrared Street View, much like what you can do with Google's Street View app. Although this sounds easy at first glance, it has turned out to be quite challenging as we must work under the constraint of a very slow infrared thermal camera that can only take less than ten pictures per second. As our app targets average people who may be interested in science and technology, we must provide an easy-to-do user interface so that the majority of people can do the job without being overwhelmed. Lastly, to create virtual reality in infrared light, we must overcome the challenge of stitching the imperfect thermal images together to produce a seamless panoramic picture. Although they are many image stitchers out there, no one can be sure that they would be applicable to thermal images as those stitchers may have been optimized for only visible light images.<br />
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<tr><td class="tr-caption" style="text-align: center;">Fig. 2: Panoramas in visible light and infrared light (two coloring styles) generated by SmartIR</td></tr>
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To support users to make 360<i style="-webkit-text-stroke-width: 0px; background-color: white; color: #6a6a6a; font-family: Roboto, arial, sans-serif; font-size: small; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: bold; letter-spacing: normal; orphans: 2; text-align: left; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">°</i> panoramas, SmartIR guides them to aim at the right angles so that the resulting image set can be used for stitching. These images should be evenly distributed in the azimuthal dimension and they should overlap considerably for the stitcher to have a clue about how to knit them together. SmartIR uses the on-board sensors of the smartphone to detect the orientation of the infrared camera. A number of circles are shown on the screen to indicate the orientations which the user should aim the cursor of the camera at. When the cursor is within the circle, an image is automatically taken and stored in a 360<i style="-webkit-text-stroke-width: 0px; background-color: white; color: #6a6a6a; font-family: Roboto, arial, sans-serif; font-size: small; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: bold; letter-spacing: normal; orphans: 2; text-align: left; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">°</i> scroller. By turning at a fixed position and aiming at the circles, the user can capture a series of images for stitching. The following YouTube videos show how this image collector works.<br />
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Although this is a very primitive prototype, it nonetheless represents the first concrete step towards the realization of the Infrared Street View. Down the road, stitchers for infrared thermal images still need significant improvements to truly achieve seamless effects similar to those for visible light images. Tremendous challenges for weaving the Map of Temperature still lie ahead. I will keep folks posted as I inch towards the goal and I am quite optimistic that I can get somewhere, even though I am not a Googler.</div>
Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com4tag:blogger.com,1999:blog-8457990200766739016.post-32666534768341802922018-09-09T12:43:00.004-04:002018-09-10T08:22:52.863-04:00Creating Augmented Reality Experiences in the Thermal World with SmartIR<div dir="ltr" style="text-align: left;" trbidi="on">
Location-based augmented reality (AR) games such as Pokemon Go have become popular in recent years. What can we learn from them in order to make SmartIR into a fun science app? In the past week, I have been experimenting with AR in SmartIR using the location and orientation sensors of the smartphone. This article shows the limited progress I have achieved thus far. Although there are still tons of challenges ahead, AR appears to be a promising direction to explore further in the world of infrared thermal imaging.<br />
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According to Wikipedia, augmented reality is an interactive experience of a real-world environment whereby the objects in the real world are "augmented" by computed information. Typically, AR is implemented with a mobile device that has a camera and a display. In a broad sense, an image from a FLIR ONE thermal camera using the so-called MSX technology is automatically AR by default as it meshes a photo of the real world with false colors generated from the thermal radiation of the objects in the real world measured by the camera's microbolometer array. Similarly, the object-tracking work I have recently done for <a href="http://molecularworkbench.blogspot.com/2018/08/project-snake-eyes-automatic-feature.html">Project Snake Eyes</a> can augment thermal images with information related to the recognized objects computed from their infrared radiation data, such as their silhouettes and their average temperatures.<br />
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<tr><td class="tr-caption" style="text-align: center;">Fig. 1: Very simple AR demos in SmartIR</td></tr>
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But these are not the AR applications that I want to talk about in this article. The AR experience I hope to create is more similar to games like Pokemon Go, which is based on information geotagged by users and discovered by others. The involvement of users in creating AR content is critical to our app, as it aims to promote location-based observation, exploration, and sharing using thermal imaging around the world and aggregate large-scale thermal data for citizen science applications. Figure 1 shows the augmentation of the thermal image view with geotagged information for a house. If you are wondering about the usefulness of this feature other than its coolness (the so-what question), you can imagine <b>tagging the weak points of a thermal envelope of a building during a home energy assessment</b>. The following YouTube videos show how geotagging works in the current version of SmartIR and how users can later discover those geotags.<br />
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At this point of the development, I envision SmartIR to provide the following AR experiences. For users who have a thermal camera, a geotag can obviously guide them to find and observe something previously marked by others<b>.</b> What if you don't have a thermal camera but would still like to see how a place would look like through the lens of a thermal camera? In that case, a geotag allows you to see a plain thermal image or virtual reality (VR) view stored in the geotag, taken previously by someone else who had a thermal camera and overlaid in the direction of the current view on the screen of your phone. If VR is provided for the tagged site, the thermal image can also change when you turn your phone. Although nothing beats using a thermal camera to explore on your own, this is a "better than nothing" solution that mimics the experience of using one. In fact, this is the vision of our Infrared Street View project that aims at providing a thermal view of our world. In addition to the Web-based approach to exploring the Infrared Street View, AR provides a location-based approach that may be more intuitive and exciting.</div>
Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com1tag:blogger.com,1999:blog-8457990200766739016.post-5125097119143469042018-08-30T22:03:00.000-04:002018-08-30T23:03:48.768-04:00Adding Instructional Support in SmartIR<div dir="ltr" style="text-align: left;" trbidi="on">
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A goal of the SmartIR app is to provide basic instructional support directly in the app so that students, citizen scientists, professionals, and other users can learn what they can do with the incredible power of thermal vision beyond its conventional applications. This requires a lot of development work, such as inventing the artificial intelligence that can guide them through making their own scientific discoveries and engineering decisions. While that kind of instructional power poses an enormous challenge to us, adding some instructional materials in the app so that users can get started is a smaller step that we can take right now.<br />
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As of August 30, 2018, I have added 17 experiments in physical sciences that people can do with thermal vision in SmartIR. These experiments, ranging from heat transfer to physical chemistry, are based on my own work in the field of infrared imaging in the past eight years and are all very easy to do (to the point that I call them "kitchen science"). I now finally have a way to deliver these experiments through a powerful app. Figure 1 shows the list of these experiments in SmartIR. Users can click each card to open the corresponding instructional unit (which is a sequence of steps that guide users through the selected set of experiments).<br />
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To do better than just putting some HTML pages into the app, I have also built critical features that allow users to switch back and forth between the thermal view and the document view (Figure 2). When users jump to the thermal view from a document, a thumbnail view of that document is shown on top of a floating button in the thermal view window (see the left image in Figure 2), allowing users to click it and go back to the document at any time. The thumbnail also serves to remind them which experiment they are supposed to conduct. When they go back to the document, a thumbnail view of the thermal camera is shown on top of a floating button in the document view window (see the right image in Figure 2), allowing users to click it and go back to the thermal view at any time. This thumbnail view also connects to the image stream from the thermal camera so that users can see current picture the camera is displaying without leaving the document.<br />
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These instructional features will be further enhanced in the future. For instance, users will be able to insert a thermal image into a container, or even create a slide show, in an HTML page to document a finding. At the end, they will be able to use SmartIR to automatically generate a lab report.<br />
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These new features, along with what I have built in the past few weeks, mark the milestone of Version 0.0.2 of SmartIR (i.e., only 2% of the work has been done towards maturity). The following video offers a sneak peek of this humble version.<br />
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Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com0tag:blogger.com,1999:blog-8457990200766739016.post-81471401123571031532018-08-22T10:31:00.000-04:002018-08-22T19:05:48.322-04:00Project Snake Eyes: Automatic Feature Extraction Based on Thermal Vision<div dir="ltr" style="text-align: left;" trbidi="on">
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I am pleased to announce <b>Project Snake Eyes</b>. This ambitious project aims to combine image analysis and infrared imaging to create <b>biomimetic thermal vision</b> -- computer vision that simulates the ability of some animals such as snakes to see in darkness. One of the goals of Project Snake Eyes is to create probably the world's first robotic snake that can hunt through thermal sensing. Project Snake Eyes not only can detect heat, but it can also estimate
the size and proximity of the source, giving the robotic snake the artificial
intelligence to figure out whether or not it should strike
the target. Through two weeks of intense research and development, I have come up with original algorithms that allow <b>robust automatic feature extraction in real time from thermal images</b> through a FLIR ONE camera. This article reports my progress thus far. The algorithms will be published in a journal in the field of image processing and computer vision later.<br />
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<a href="https://4.bp.blogspot.com/-SA0ZbJGMK5U/W31teT7uReI/AAAAAAAADUw/STuRmv8s_VkXGoDQLJ6ITjw5dbw3q_6QQCLcBGAs/s1600/Untitled-1.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="316" data-original-width="949" src="https://4.bp.blogspot.com/-SA0ZbJGMK5U/W31teT7uReI/AAAAAAAADUw/STuRmv8s_VkXGoDQLJ6ITjw5dbw3q_6QQCLcBGAs/s400/Untitled-1.png" width="550" /></a></div>
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Figure 1 shows the detection of windows at night from outside a house. Half of the window was open at the time of observation. The lower temperature of the upper pane was partially due to the reflection of infrared light from the cooler environment such as the sky by the glass. The lower pane, which was the open part of the window, was due to the warmer inside of the house. As you can see, Project Snake Eyes approximately identified the shapes and sizes of the two panes, which stood out from the background because of their distinct temperatures. This ability will be used to <b>develop advanced algorithms to automatically analyze the thermal signature of a building, paving the road to large-scale building thermal analyses through automation.</b><br />
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Figure 2 shows a more complex scenario using my computer desk as an example. As you can see, Project Snake Eyes automatically detected most of the hot spots (or cold spots, but I use hot spots to represent points of interest at either high or low temperature). <b>Feature extraction such as blob detection through thermal vision could result in enhanced computer vision for technologies such as unmanned vehicles.</b><br />
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Figure 3 shows object reconstruction based on residue heat using my hand as an example. The residue heat that my hand left on the wall revealed its shape as six separate polygons (the largest one corresponds to the palm and the five smaller ones to the fingertips). <b>Object reconstruction through residue heat could find its applications in certain industry monitoring and control.</b><br />
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<tr><td style="text-align: center;"><a href="https://2.bp.blogspot.com/-AeTN6PZ4nRo/W32B9Ky4hoI/AAAAAAAADU8/8rhzG7spnKg0d5aWfVijh3yg8HZpwjNxgCLcBGAs/s1600/smartir-20180821222826.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1440" data-original-width="1080" height="400" src="https://2.bp.blogspot.com/-AeTN6PZ4nRo/W32B9Ky4hoI/AAAAAAAADU8/8rhzG7spnKg0d5aWfVijh3yg8HZpwjNxgCLcBGAs/s400/smartir-20180821222826.png" width="300" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Project Snake Eyes aims to identify and track objects.</td></tr>
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Figure 4 shows object tracking using a colleague as an example. As you can see, Project Snake Eyes basically captured his body shape. The video clip that follows shows how this works in real time. The algorithms still need optimization to reduce the lag, but you get the basic idea of how object tracking using Project Snake Eyes works. Object tracking will be used to <b>realize animal and human tracking in dark conditions for many science and engineering applications.</b> For instance, we are collaborating with biologists in Texas to develop a system that can track bats at night in order to monitor the health of their colonies.<br />
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With all these excitements, I expect to carry on the research and development of Project Snake Eyes in the coming years. As a landmark step, we are working tirelessly towards the unveiling of the first prototype of the robotic snake with thermal vision, collaborating with two robotics teams led by Prof. Yan Gu at the University of Massachusetts Lowell and Prof. Zhenghui Sha at the University of Arkansas, respectively.<br />
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Stay tuned!</div>
Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com1tag:blogger.com,1999:blog-8457990200766739016.post-16334027512557186962018-08-02T14:04:00.000-04:002018-08-02T15:10:39.427-04:00Using SmartIR in Science Experiments<div dir="ltr" style="text-align: left;" trbidi="on">
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<tr><td style="text-align: center;"><a href="https://4.bp.blogspot.com/-ttADzlSZp4A/W2M9-4OrsXI/AAAAAAAADTs/-8oY7rgOX_YQ1SQM5qmVR8VmKmJYaut7gCLcBGAs/s1600/Untitled-1.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="666" data-original-width="668" height="319" src="https://4.bp.blogspot.com/-ttADzlSZp4A/W2M9-4OrsXI/AAAAAAAADTs/-8oY7rgOX_YQ1SQM5qmVR8VmKmJYaut7gCLcBGAs/s320/Untitled-1.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 1: The paper-on-cup experiment with SmartIR</td></tr>
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SmartIR is a smartphone app that I am developing to support infrared (IR) thermal imaging applications, primarily in the field of science and engineering, based on the FLIR ONE SDK. The development officially kicked off in July 2018. By the end of the month, a rudimentary version, to which I assigned V 0.0.1 (representing approximately 1% of the work that needs to be done for a mature release), has been completed.<br />
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<tr><td style="text-align: center;"><a href="https://4.bp.blogspot.com/-yLz6HOulkv4/W2NAiAOS6RI/AAAAAAAADT4/7hDOrVAHT2AOJFqU2cDJ26gLIR3Xw3gSQCLcBGAs/s1600/Untitled-1.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="668" data-original-width="673" height="317" src="https://4.bp.blogspot.com/-yLz6HOulkv4/W2NAiAOS6RI/AAAAAAAADT4/7hDOrVAHT2AOJFqU2cDJ26gLIR3Xw3gSQCLcBGAs/s320/Untitled-1.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 2: Time graphs of temperatures in SmartIR</td></tr>
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Although a very early version, SmartIR V0.0.1 can already support some scientific exploration. In this article, I share the results from doing the can't-be-simpler experiment that <a href="http://molecularworkbench.blogspot.com/2012/10/think-molecularly-infrared-imaging.html">I did back in 2011</a> with a FLIR I5. This experiment needs only a cup of water, a piece of paper, and, of course, an IR camera (which is <a href="https://www.amazon.com/FLIR-Thermal-Imaging-Camera-Android/dp/B07232C9RB">FLIR ONE Pro Generation 3</a> in my case). When a piece of paper is placed on top of an open cup of tap water that has sit in the room for a few hours, it warms up -- instead of cooling down -- as a result of the adsorption of water molecules onto the underside of the paper and the condensation of more water molecules to form a layer of liquid water, as shown in Figure 1.<br />
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While the user can observe this effect with any thermal camera, it is sometimes useful to also record the change of temperatures as time goes by. To do this, SmartIR allows the user to add any number of thermometers to the view (and move or delete them as needed) and show their temperature readings in a time graph on top of the thermal image view (this is sort of like the translucent sensor graph in my Energy2D computational fluid dynamics simulation program). Figure 2 shows the time graph of temperatures. To study the effect, I added three thermometers: one for measuring the ambient temperature (T3), one for measuring the temperature of water (T2), and one for measuring the temperature of the paper (T1). Note that, before the paper was placed, T1 and T2 both measured the temperature of the water in the cup. As today is pretty hot, T3 registered higher than 35 <i style="-webkit-text-stroke-width: 0px; background-color: white; color: #6a6a6a; font-family: Roboto, arial, sans-serif; font-size: small; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: bold; letter-spacing: normal; orphans: 2; text-align: left; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">°</i><span style="background-color: white; color: #545454; display: inline; float: none; font-family: "roboto" , "arial" , sans-serif; font-size: x-small; font-style: normal; font-weight: 400; letter-spacing: normal; text-align: left; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;"></span>C. Due to the effect of evaporative cooling, T2 registered about 33 <i style="-webkit-text-stroke-width: 0px; background-color: white; color: #6a6a6a; font-family: Roboto, arial, sans-serif; font-size: small; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: bold; letter-spacing: normal; orphans: 2; text-align: left; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">°</i><span style="background-color: white; color: #545454; display: inline; float: none; font-family: "roboto" , "arial" , sans-serif; font-size: x-small; font-style: normal; font-weight: 400; letter-spacing: normal; text-align: left; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;"></span>C. When a piece of paper was put on top of the cup, T1 rose to nearly 37 <i style="-webkit-text-stroke-width: 0px; background-color: white; color: #6a6a6a; font-family: Roboto, arial, sans-serif; font-size: small; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: bold; letter-spacing: normal; orphans: 2; text-align: left; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">°</i><span style="background-color: white; color: #545454; display: inline; float: none; font-family: "roboto" , "arial" , sans-serif; font-size: x-small; font-style: normal; font-weight: 400; letter-spacing: normal; text-align: left; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;"></span>C in a few seconds!<br />
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SmartIR is currently only available in Android. It hasn't been released in Google Play as intense development is expected to be under way in the next six months. A public release may be available next year.<br />
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Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com0tag:blogger.com,1999:blog-8457990200766739016.post-3997848728819343802018-07-01T21:40:00.001-04:002018-07-03T10:51:57.781-04:00Artificial Intelligence Research: The Octopus Algorithm for Generating Goal-Directed Feedback<div dir="ltr" style="text-align: left;" trbidi="on">
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During the 2010 FIFA World Cup eight years ago, a common octopus named <a href="https://en.wikipedia.org/wiki/Paul_the_Octopus" rel="nofollow noopener" target="_blank">Paul the Octopus</a>
gained worldwide attention because it accurately "predicted" all the
results of the most important soccer matches in the world (sadly it died by natural courses
shortly after that). Perhaps Paul the Octopus just got extraordinarily lucky. Eight
years later, as reported by the MIT Technology Review, <a href="https://www.technologyreview.com/s/611397/machine-learning-predicts-world-cup-winner/" rel="nofollow noopener" target="_blank">artificial intelligence has been used in its stead to predict the World Cup</a> (which I doubt would achieve the 100% success rate as the famous octopus did marvelously).<br />
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<tr><td style="text-align: center;"><a href="https://4.bp.blogspot.com/-MzcJ-6i7hvU/WzqzAEeiP7I/AAAAAAAADS0/NpAkQEjoZhQ0drCnOlesWSfGb_wkL9BhACLcBGAs/s1600/figure001.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="364" data-original-width="716" height="202" src="https://4.bp.blogspot.com/-MzcJ-6i7hvU/WzqzAEeiP7I/AAAAAAAADS0/NpAkQEjoZhQ0drCnOlesWSfGb_wkL9BhACLcBGAs/s400/figure001.png" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i>Fig. 1: An illustration of the Octopus Algorithm</i></td></tr>
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While my research on artificial intelligence (AI) has nothing to do
with predicting which team would win the World Cup this year, octopuses
have become one of my inspirations in the past few days. My work is about developing
AI techniques that support learning and teaching through solving vastly
open-ended problems such as scientific inquiry and engineering design.
One of the greatest challenges in such problem-solving tasks is about
how to automatically generate formative feedback based on automatically
assessing student work. Typically, the purpose of this kind of feedback
is to gradually direct students to some kind of goals, for example, to
achieve the most energy-efficient design of a building that meets all
the specs. Formative feedback is critically important to ensuring the
success of <a href="https://en.wikipedia.org/wiki/Project-based_learning">project-based learning</a>. Based on my own experiences, however, many
students have great difficulties making progress towards the goal in the short amount of
time typically available in the classroom. Given the time constraints, they need help on an
ongoing basis. But it is not realistic to expect the teacher to
simultaneously monitor a few dozen students while they are working on
their projects and provide timely feedback to each and every one of them. This is where AI can help.
This is why we are developing new pedagogical principles and
instructional strategies, hoping to harness the power of AI to spur
students to think more deeply, explore more widely, and even design more
creatively.<br />
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<tr><td style="text-align: center;"><a href="https://2.bp.blogspot.com/-CUhxiHi46LM/WzuI2UBgEMI/AAAAAAAADTA/zxjs2w0Du7EzYH0_xaxfFPZvE6gisqvHgCLcBGAs/s1600/figure002.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="493" data-original-width="788" height="250" src="https://2.bp.blogspot.com/-CUhxiHi46LM/WzuI2UBgEMI/AAAAAAAADTA/zxjs2w0Du7EzYH0_xaxfFPZvE6gisqvHgCLcBGAs/s400/figure002.png" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i>Fig. 2: Learning from AI through a competition-based strategy</i></td></tr>
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Although this general idea of using AI in education makes sense,
developing reliable algorithms that can automatically guide
students to solve massively open-ended problems such as engineering
design is by no means a small job. Through three months of intense work
in this field, I have developed <a href="https://en.wikipedia.org/wiki/Genetic_algorithm" rel="nofollow noopener" target="_blank">genetic algorithms</a> that can be used to find optimal solutions in complex design environments such as the <a href="http://energy3d.concord.org/" rel="nofollow noopener" target="_blank">Energy3D</a> CAD software, which you can find in earlier articles published through <a href="http://molecularworkbench.blogspot.com/" rel="nofollow noopener" target="_blank">my blog</a>.
These algorithms were proven to be effective for optimizing certain
engineering problems, but to call them AI, we will need to identify what
kind of human intelligence they are able to augment or
replace. In my current point of view, an apparent class of AI
applications is about mimicking certain instructional capacities of
peers and teachers. In order to create an <b>artificial peer</b> or even an <b>artificial instructor</b>,
we would have to figure out the algorithms that simulate the constructive
interactions between a student and a peer or between a student and an
instructor.<br />
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Hence, an optimization algorithm that yields the best solution in a
long single run is not very useful to developers and educators as it
doesn't provide sufficient opportunities for engaging students. You can
imagine that type of algorithm as someone who does something very fast
but doesn't pause to explain to the learner how he or she does the job.
To create a developmentally appropriate tool, we will need to slow down
the process a bit -- sort of like the creeping of an octopus -- so that
the learner can have a chance to observe, reflect, internalize, and
catch up when AI is solving the problem step by step. This kind of
algorithm is known as <a href="https://en.wikipedia.org/wiki/Local_search_(optimization)" rel="nofollow noopener" target="_blank">local search</a>,
a technique for finding an optimal solution in the vicinity of a starting point that represents the learner's current state (as opposed to
global search that casts a wide net across the entire solution space, representing
equally all possibilities regardless of the learner's current state). <a href="https://en.wikipedia.org/wiki/Random_optimization" rel="nofollow noopener" target="_blank">Random optimization</a>
is one of the local search methods proposed in 1965, which
stochastically generates a set of candidate solutions distributed around
the initial solution in accordance with the <a href="https://en.wikipedia.org/wiki/Normal_distribution" rel="nofollow noopener" target="_blank">normal distribution</a>.
The graphical representation of a normal distribution is a bell curve
that somewhat resembles the shape of an octopus (Figure 1). When using a genetic
algorithm to implement the local search, the two red edge areas in Figure 1
can be imagined as the "tentacles" for the "octopus" to sense "food"
(optima), while the green bulk area in the middle can be imagined as the "body" for it to "digest the catches" (i.e., to concentrate on local search). Once an optimum is "felt" (i.e., one or more solution
points close to the optimum is included in the randomly generated population of the genetic
algorithm), the "octopus" will move towards it (i.e., the best
solution from the population will converge to the optimum) as driven by
the genetic algorithm. The length of the "tentacles," characterized by the standard
deviation of the normal distribution, dictates the pace in which the
algorithm will find an optimum. The smaller the standard deviation, the
slower the algorithm will locate an optimum. <b>I call this
particular combination of random optimization and genetic algorithm the
Octopus Algorithm as it intuitively mimics how an octopus hunts on the sea floor</b> (and, in part, to honor Paul the Octopus and to celebrate the 2018 World Cup Tournament).
With a controlled drift speed, the Octopus Algorithm can be applied to
incrementally correct the learner's work in a way that goes back and forth between human and computer, making it
possible for us to devise a competition-based learning strategy as
illustrated in Figure 2.<br />
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Although there are still a lot of technical details that need to be ironed
out as we make progress, this competition-based strategy essentially
represents an idea to turn a design process into some kind of
adversarial gaming (e.g., chess or Go), which challenges students to
race against a computer towards an agreed goal but with an unpredictable
outcome (either the computer wins or the human wins). It is our hope that AI would ultimately serve as a tool to train students to design effectively just like what it has
already done for training chess or Go players.<br />
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<tr><td style="text-align: center;"><a href="https://2.bp.blogspot.com/-wQhH1s1oVo4/Wzoh5nztMqI/AAAAAAAADSo/Oi8a1kXai9csbTlwJtO7OfGOg1Iye-QaQCLcBGAs/s1600/single-peak.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="733" data-original-width="585" height="400" src="https://2.bp.blogspot.com/-wQhH1s1oVo4/Wzoh5nztMqI/AAAAAAAADSo/Oi8a1kXai9csbTlwJtO7OfGOg1Iye-QaQCLcBGAs/s400/single-peak.png" width="318" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i>Fig. 3: Evolution of population in the Octopus Algorithm</i></td></tr>
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How does the Octopus Algorithm work, I hear you are curious? I have tested it
with some simple test functions such as certain sinusoidal
functions (e.g., |sin(<i>nx</i>)|) and found that it worked for those
test cases. But since I have the Energy3D platform, I can readily test
my algorithms with real-world problems instead of some toy problems. As the first real-world example,
let's check how it finds the optimal tilt angle of a single row of solar
panels for a selected day at a given location (we can do it for the
entire year, but it takes much longer to run the simulation with not
much more to add in terms of testing the algorithm). Let's assume that
the initial guess for the tilt angle is zero degree (if you have no idea which way and how much the solar panels should be
tilted, you may just lay them flat as a reasonable starting point). Figure 3 shows the
results of four consecutive runs. The graphs in the left column show the
normal distributions around the initial guess and the best emerged after each round (which was used as the initial guess for the next round). The graphs in the right column show the final distribution of the population at the end of each round. The first and second runs show that the "octopus" gradually drifted left. At the end of the third run, it had converged to the final
solution. It just stayed there at the end of the fourth run.<br />
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<tr><td style="text-align: center;"><a href="https://1.bp.blogspot.com/-5zno6og-7IQ/WzmJdj_o0RI/AAAAAAAADRg/sxrHmsCRAy8WMlFAe6mtDw8qsRjz68LuQCLcBGAs/s1600/figure003.png" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="315" data-original-width="1267" height="156" src="https://1.bp.blogspot.com/-5zno6og-7IQ/WzmJdj_o0RI/AAAAAAAADRg/sxrHmsCRAy8WMlFAe6mtDw8qsRjz68LuQCLcBGAs/s640/figure003.png" width="560" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i>Fig. 4: Using four "octopuses" to locate </i><i><i>four optimal orientations for the energy efficiency of a house.</i></i></td></tr>
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<tr><td style="text-align: center;"><a href="https://3.bp.blogspot.com/-kQyszBu4u2M/WzoUEQ09JoI/AAAAAAAADSQ/VDKshSKrx2YwTOeafmRC8W3Qihh4zX0ygCLcBGAs/s1600/four-peaks.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="738" data-original-width="586" height="400" src="https://3.bp.blogspot.com/-kQyszBu4u2M/WzoUEQ09JoI/AAAAAAAADSQ/VDKshSKrx2YwTOeafmRC8W3Qihh4zX0ygCLcBGAs/s400/four-peaks.png" width="317" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i>Fig. 5: Locating the nearest optimum</i></td></tr>
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When there are multiple optima in the solution space (a problem known as <a href="https://en.wikipedia.org/wiki/Evolutionary_multimodal_optimization" rel="nofollow noopener" target="_blank">multimodal optimization</a>),
it may be appropriate to expect that AI would guide students to the
nearest optimum. This may also be a recommendation by learning theories
such as the <a href="https://en.wikipedia.org/wiki/Zone_of_proximal_development" rel="nofollow noopener" target="_blank">Zone of Proximal Development </a>introduced
by Russian psychologist Lev Vygotsky. If a student is working in a
certain area of the design space, guiding him or her to find the best
option within that niche seems to be the most sensible instructional
strategy. With a conventional genetic algorithm that performs global
search with uniform initial selection across the solution space, there
is simply no guarantee that the suggested solution would take the
student's current solution into consideration, even though his/her
current solution can be included as part of the first generation (which,
by the way, may be quickly discarded if the solution turns out to be a
bad one). The Octopus Algorithm, on the other hand, respects the
student's current state and tries to walk him/her through the process
stepwisely. In theory, it is a better technique to support <b>advanced personalized learning</b>, which is <a href="http://www.engineeringchallenges.org/challenges/learning.aspx">the number one in the 14 grand challenges</a> for engineering in the 21st century posed by the National Academy of Engineering of the United States.<br />
<br />
Let's see how the Octopus Algorithm finds multiple optima. Again, I
have tested the algorithm with simple sinusoidal functions and found that it
worked in those test cases. But I want to use a real-world example from
Energy3D to illustrate my points. This example is concerned with
determining the optimal orientation of a house, given that everything
else has been fixed. By manual search, I found that there are basically four different orientations
that could result in comparable energy efficiency, as shown in Figure 4. <br />
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<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://4.bp.blogspot.com/--rJUpJLHDeo/WzmKFxD8oxI/AAAAAAAADR4/B8_JX7_7DFIM_IzQxGJbLzN8bTEjkD-EwCLcBGAs/s1600/figure004.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="436" data-original-width="769" height="226" src="https://4.bp.blogspot.com/--rJUpJLHDeo/WzmKFxD8oxI/AAAAAAAADR4/B8_JX7_7DFIM_IzQxGJbLzN8bTEjkD-EwCLcBGAs/s400/figure004.png" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i>Fig. 6: "A fat octopus" vs. "a slim octopus."</i></td></tr>
</tbody></table>
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</div>
Now let's pick four different initial guesses and see which optimum
each "octopus" finds. Figure 5 shows the results. The graphs in the left
column show the normal distributions around the four initial guesses.
The graphs in the right column show the final solutions to which the Octopus
Algorithm converged. In this test case, the algorithm succeeded in
ensuring nearest guidance within the zone of proximal development. Why
is this important? Imagine if the student is experimenting with a
southwest orientation but hasn't quite figured out the optimal angle. An
algorithm that suggests that he or she should abandon the current line
of thinking and consider another orientation (say, southeast) could
misguide the student and is unacceptable. Once the student arrives at an optimal solution nearby, it may be
desirable to prompt him/her to explore alternative solutions by choosing
a different area to focus and repeat this process as needed. The
ability for the algorithm to detect the three other optimal solutions
simultaneously, known as multi-niche optimization, may not be essential.
<br />
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<tr><td style="text-align: center;"><a href="https://1.bp.blogspot.com/-G7PyXnkOoos/WzoULVwteXI/AAAAAAAADSY/-8qG8YDG-dcQM3VOm5-LTPq2U9DVVh1BgCLcBGAs/s1600/four-peaks-larger-radius.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="738" data-original-width="586" height="400" src="https://1.bp.blogspot.com/-G7PyXnkOoos/WzoULVwteXI/AAAAAAAADSY/-8qG8YDG-dcQM3VOm5-LTPq2U9DVVh1BgCLcBGAs/s400/four-peaks-larger-radius.png" width="317" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i>Fig. 7. A "fatter octopus" may be problematic. </i></td></tr>
</tbody></table>
There is a practical problem, though. When we generate the normal
distribution of solution points around the initial guess, we have to
specify the standard deviation that represents the reach of the "tentacles"
(Figure 6). As illustrated by Figure 7, the larger the standard
deviation ("a fatter octopus"), the more likely the algorithm will find
more than one optima and may lose the nearest one as a result. In most cases, finding a
solution that is close enough may be good enough in terms of guidance. But if this weakness
becomes an issue, we can always reduce the standard deviation to search the neighborhood more carefully. The
downside is that it will slow down the optimization process, though.<br />
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<i></i><br />
In summary, the Octopus Algorithm that I have invented seems to be
able to accurately guide a designer to the nearest optimal solution in an
engineering design process. Unlike Paul the Octopus that relied on
supernatural forces (or did it?), the Octopus Algorithm is an AI technique that we create, control, and leverage. On a separate note, since some genetic algorithms also employ tournament selection like the
World Cup, perhaps Paul the Octopus was thinking like a genetic
algorithm (joke)? For the computer
scientists who happen to be reading this article, it may also add a new
method for multi-niche optimization besides fitness sharing and
probabilistic crowding.</div>
</div>
Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com3tag:blogger.com,1999:blog-8457990200766739016.post-49361006599797557992018-06-28T20:03:00.001-04:002018-06-28T20:03:24.755-04:00Computer Applications in Engineering Education Published Our Paper on CAD Research<div dir="ltr" style="text-align: left;" trbidi="on">
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<tr><td style="text-align: center;"><a href="https://4.bp.blogspot.com/-CuzfxREL1wA/WzV29tA4OkI/AAAAAAAADRE/lYWlIivTCxsxdHn5z0Bhb-cjqGQd0DuRwCLcBGAs/s1600/Untitled-3.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="805" data-original-width="644" height="400" src="https://4.bp.blogspot.com/-CuzfxREL1wA/WzV29tA4OkI/AAAAAAAADRE/lYWlIivTCxsxdHn5z0Bhb-cjqGQd0DuRwCLcBGAs/s400/Untitled-3.png" width="318" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 1: Integrated design and simulation in Energy3D</td></tr>
</tbody></table>
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In workplaces, engineering design is supported by contemporary computer-aided design (CAD) tools capable of <b>virtual prototyping</b> — a full-cycle process to explore the structure, function, and cost of a complete product on the computer using modeling and simulation techniques before it is actually built. In classrooms, such software tools allow students to take on a design task without regard to the expense, hazard, and scale of the challenge. Whether it is a test that takes too long to run, a process that happens too fast to follow, a structure that no classroom can fit, or a field that no naked eye can see, students can always design a computer model to simulate, explore, and imagine how it may work in the real world. Modeling and simulation can thereby push the envelope of engineering education to cover much broader fields and engage many more students, especially for underserved communities that are not privileged to have access to expensive hardware in advanced engineering laboratories. CAD tools that are equipped with such modeling and simulation capabilities provide viable platforms for teaching and learning engineering design, because a significant part of design thinking is abstract and generic, can be learned through designing computer models that work in cyberspace, and is transferable to real-world situations. <br />
<br />
Some researchers, however, cautioned that using CAD tools in engineering practices and education could result in negative side effects, such as circumscribed thinking, premature fixation, and bounded ideation, which undermine design creativity and erode existing culture. To put the issues in a perspective, these downsides probably exist in any type of tools — computer-based or not — to various extents, as all tools inevitably have their own strengths and weaknesses. As a matter of fact, the development history of CAD tools can be viewed as a progress of breaking through their own limitations and engendering new possibilities that could not have been achieved before. To do justice to the innovative community of CAD developers and researchers at large, we believe it is time to revisit these issues and investigate how modern CAD tools can address previously identified weaknesses. <b>This was the reason that motivated us to <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/cae.21920">publish a paper</a> in Computer Applications in Engineering Education to expound our points of view and supporting them with research findings.</b><br />
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<tr><td style="text-align: center;"><a href="https://2.bp.blogspot.com/-wibpSgudZHA/WzV0EOL3AJI/AAAAAAAADQ0/iX_xzzBkkO0oFhwhjEaTAT7By6ZIIx98ACLcBGAs/s1600/Untitled-1.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="904" data-original-width="692" height="400" src="https://2.bp.blogspot.com/-wibpSgudZHA/WzV0EOL3AJI/AAAAAAAADQ0/iX_xzzBkkO0oFhwhjEaTAT7By6ZIIx98ACLcBGAs/s400/Untitled-1.png" width="305" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 2: Sample student work presented in the paper</td></tr>
</tbody></table>
The view that CAD is “great for execution, not for learning” might be true for the kind of CAD tools that were developed primarily for creating 2D/3D computer drawings for manufacturing or construction. That view, however, largely overlooks three advancements of CAD technologies:<br />
<br />
<b>1) System integration that facilitates formative feedback</b>: Based on fundamental principles in science, the modeling and simulation capabilities seamlessly integrated within CAD tools can be used to analyze the function of a structure being designed and evaluate the quality of a design choice within a single piece of software (Figure 1). This differs dramatically from the conventional workflow through complicated tool chaining of solid modeling tools, pre-processors, solvers, and post-processors that requires users to master quite a variety of tools for performing different tasks or tackling different problems in order to design a virtual prototype successfully. Although the needs for many tools and even collaborators with different specialties can be addressed in the workplace using sophisticated methodologies such as 4D CAD that incorporate time or schedule-related information into a design process, it is hardly possible to orchestrate such complex operations in schools. In education, cumbersome tool switching ought to be eliminated — whenever and wherever possible and appropriate — to simplify the design process, reduce cognitive load, and shorten the time for getting formative feedback about a design idea. Being able to get rapid feedback about an idea enables students to learn about the meaning of a design parameter and its connections to others quickly by making frequent inquiries about it within the software. The accelerated feedback loop can spur iterative cycles at all levels of engineering design, which are fundamental to design ideation, exploration, and optimization. We have reported strong classroom evidence that this kind of integrated design environment can narrow the so-called “design-science gap,” empowering students to learn science through design and, in turn, apply science to design.<b> </b><br />
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<b>2) Machine learning that generates designer information</b>: For engineering education research, a major advantage of moving a design project to a CAD platform is that fine-grained process data (e.g., actions and artifacts), can be logged continuously and sorted automatically behind the scenes while students are trying to solve design challenges. This data mining technique can be used to monitor, characterize, or predict an individual student’s behavior and performance and even collaborative behavior in a team. The mined results can then be used to compile adaptive feedback to students, create infographic dashboards for teachers, or develop intelligent agents to assist design. The development of this kind of intelligence for a piece of CAD software to “get to know the user” is not only increasingly feasible, but also increasingly necessary if the software is to become future-proof. It is clear that deep learning from big data is largely responsible for many exciting recent advancements in science and technology and has continued to draw extensive research interest. Science ran a special issue on artificial intelligence (AI) in July 2015 and, only two years later, the magazine found itself in the position of having to catch up with another special issue. For the engineering discipline, CAD tools represent a possible means to gather user data of comparable magnitudes for developing AI of similar significance. In an earlier paper, we have explained why the process data logged by CAD software possess all the 4V characteristic features — volume, velocity, variety, and veracity — of big data as defined by IBM.<b> </b><br />
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<b>3) Computational design that mitigates design fixation</b>: In trying to solve a new problem, people tend to resort to their existing knowledge and experiences. While prior knowledge and experiences are important to learning according to theories such as constructivism and knowledge integration, they could also blind designers to new possibilities, a phenomenon known as design fixation. In the context of engineering education, design fixation can be caused by the perception or preconception of design subjects, the examples given to illustrate design principles, and students’ own previous designs. As it may adversely affect engineering learning to a similar degree as “cookbook labs” underrepresent science learning, design fixation may pose a central challenge to engineering education (though it has not been thoroughly evaluated among young learners in secondary schools). Emerging computational design capabilities of innovative CAD tools based on algorithmic generation and parametric modeling can suggest design permutations and variations interactively and evolutionarily, equivalent to teaming students up with virtual teammates capable of helping them explore new territories in the solution space.<br />
<br />
To read more about this paper, click <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/cae.21920">here</a> to go to the publisher's website. <br />
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Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com0tag:blogger.com,1999:blog-8457990200766739016.post-31367504336878327982018-06-15T09:15:00.000-04:002018-06-20T09:29:26.786-04:00Maine Teacher Workshop on Artificial Intelligence in Engineering Education<div dir="ltr" style="text-align: left;" trbidi="on">
In June 10-12, we hosted a successful teacher professional development workshop
in York, Maine for 29 teachers from seven states. The theme was around
the application of artificial intelligence (AI) in engineering
education to assist teaching and foster learning. The workshop was supported by generous funding from General
Motors and the National Science Foundation.<br />
<br />
<a href="https://2.bp.blogspot.com/-SQGAsA6D_18/WyO1eZF_yBI/AAAAAAAADQM/DEI6q5ql5NUngxwdJUlfXeAOJ-J6gHyggCLcBGAs/s1600/IMG_8655.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" data-original-height="852" data-original-width="1136" src="https://2.bp.blogspot.com/-SQGAsA6D_18/WyO1eZF_yBI/AAAAAAAADQM/DEI6q5ql5NUngxwdJUlfXeAOJ-J6gHyggCLcBGAs/s640/IMG_8655.jpg" width="560" /></a>The teachers explored how the AI tools built in Energy3D could help
students learn STEM concepts and skills required by the Next Generation
Science Standards (NGSS), especially engineering design. Together we brainstormed how AI applications
such as generative design might change their teaching. We believed that
AI could transform STEM education from the following four aspects: (1) <b>augment students with tools that accelerate problem solving</b><b>, thereby supporting them to explore more broadly</b>; (2) <b>identify cognitive gaps between students' current knowledge and the learning goals</b><b>, thereby enabling them to learn more deeply</b>; (3) <b><b>suggest alternative solutions beyond students' current work, thereby spurring them to think more creatively</b></b>; and (4) <b>assess students' performance by computing the distances between their solutions and the optimal ones, thereby providing formative feedback during the design process</b>. The activities that the teachers tried were situated in the context of building science and
solar engineering, facilitated by our Solarize Your World Curriculum. We presented examples that demonstrated the affordances of AI for supporting
learning and teaching along the above four directions, especially in
engineering design (which is highly open-ended). Teachers first learned how to design a solar farm in
the conventional way and then learned how to accomplish the same task
in the AI way, which -- in theory -- can lead to <b>broader exploration</b>, <b>deeper understanding</b>, <b>better solutions</b>, and <b>faster feedback</b>.<br />
<br />
View <a href="http://energy.concord.org/~xie/papers/aied-york-me.pdf" rel="nofollow noopener" target="_blank">my PowerPoint slides</a> for more information.<br />
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Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com0tag:blogger.com,1999:blog-8457990200766739016.post-65173331856495523932018-06-01T14:25:00.001-04:002018-06-01T21:00:26.396-04:00Generative Design of Concentrated Solar Power Towers<div dir="ltr" style="text-align: left;" trbidi="on">
<div class="reader-article-content">
In a sense, design is about
choosing parameters. All the parameters available for adjustment form
the basis of the multi-dimensional <a href="https://en.wikipedia.org/wiki/Feasible_region" rel="nofollow noopener" target="_blank">solution space</a>.
The ranges within which the parameters are allowed to change, often due
to constraints, sets the volume of the feasible region of the solution
space where the designer is supposed to work. <a href="https://en.wikipedia.org/wiki/Parametric_design" rel="nofollow noopener" target="_blank">Parametric design</a>
is, to some extent, a way to convert design processes or subprocesses
into algorithms for varying the parameters in order to automatically
generate a variety of designs. Once such algorithms are established,
users can easily create new designs by tweaking parameters without
having to repeat the entire process manually. The reliance on computer
algorithms to manipulate design elements is called <a href="https://en.wikipedia.org/wiki/Parametricism" rel="nofollow noopener" target="_blank">parametricism</a> in modern architecture.<br />
<br />
Parametricism allows people to use a computer to generate a lot of
designs for evaluation, comparison, and selection. If the choice of the
parameters is driven by a <a href="https://en.wikipedia.org/wiki/Genetic_algorithm" rel="nofollow noopener" target="_blank">genetic algorithm</a>, then the computer will also be able to spontaneously evolve the designs towards one or more objectives. In this article, I use the design of the <a href="https://en.wikipedia.org/wiki/Heliostat" rel="nofollow noopener" target="_blank">heliostat</a> field of a <a href="https://en.wikipedia.org/wiki/Solar_power_tower" rel="nofollow noopener" target="_blank">concentrated solar power tower</a> as an example to illustrate how this type of <a href="https://en.wikipedia.org/wiki/Generative_design" rel="nofollow noopener" target="_blank">generative design</a>
may be used to search for optimal designs in engineering practice. As
always, I recorded a screencast video that used the daily total output
of such a power plant on June 22 as the <a href="https://en.wikipedia.org/wiki/Loss_function" rel="nofollow noopener" target="_blank">objective function</a>
to speed up the calculation. The evaluation and ranking of different solutions in the real world must use
the annual output or profit as the objective function. For the purpose
of demonstration, the simulations that I have run for writing this
article were all based on a rather coarse grid (only four points per
heliostat) and a pretty large time step (only once per hour for solar
radiation calculation). In real-world applications, a much more
fine-grained grid and a much smaller time step should be used to
increase the accuracy of the calculation of the objective function.<br />
<br />
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<iframe allowfullscreen="" class="YOUTUBE-iframe-video" data-thumbnail-src="https://i.ytimg.com/vi/ung8kG8FCqs/0.jpg" frameborder="0" height="300" src="https://www.youtube.com/embed/ung8kG8FCqs?feature=player_embedded" width="520"></iframe></div>
<br />
<span style="font-size: x-small;"><i>Video: The animation of a generative design process of a
heliostat field on an area of 75m×75m for a hypothetical solar power
tower in Phoenix, AZ.</i></span><br />
<br />
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<tr><td style="text-align: center;"><a href="https://media.licdn.com/dms/image/C5612AQF5kkLBtSoGpQ/article-inline_image-shrink_1500_2232/0?e=2127081600&v=beta&t=7kSDdhgJuSW5qirHwC5Qqq7LReEqoAeybH16BVrzq4M" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-li-src="https://media.licdn.com/dms/image/C5612AQF5kkLBtSoGpQ/article-inline_image-shrink_1500_2232/0?e=2127081600&v=beta&t=7kSDdhgJuSW5qirHwC5Qqq7LReEqoAeybH16BVrzq4M" data-media-urn="urn:li:digitalmediaAsset:C5612AQF5kkLBtSoGpQ" height="160" src="https://media.licdn.com/dms/image/C5612AQF5kkLBtSoGpQ/article-inline_image-shrink_1500_2232/0?e=2127081600&v=beta&t=7kSDdhgJuSW5qirHwC5Qqq7LReEqoAeybH16BVrzq4M" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i>Figure 1: A parametric model of the sunflower.</i></td></tr>
</tbody></table>
Heliostat fields can take many forms (the <a href="http://www.powerfromthesun.net/Book/chapter10/chapter10.html" rel="nofollow noopener" target="_blank">radial stagger layout</a>
with different heliostat packing density in multiple zones seems to be the
dominant one). One of my earlier (and naïve) attempts was to treat the
coordinates of every heliostat as parameters and use genetic algorithms
to find optimal coordinates. In principle, there is nothing wrong with
this approach. In reality, however, the algorithm tends to generate a
lot of heliostat layouts that appear to be random distributions (later on, I realized that the problem is as challenging as <a href="https://en.wikipedia.org/wiki/Protein_folding" rel="noopener nofollow" target="_blank">protein folding</a>
if you know what it is -- when there are a lot of heliostats, there are
just too many local optima that can easily trap a genetic algorithm to
the extent that it would probably never find the global optimum within
the computational time frame that we can imagine). While a
"messy" layout might in fact generate more electricity than a "neat"
one, it is highly unlikely that a serious engineer would recommend such a
solution and a serious manager would approve it, especially for large
projects that cost hundreds of million of dollars to construct. For one thing, a
seemingly stochastic distribution would not present the beauty of the Ivanpah Solar Power Facility through the lens of <a href="http://www.forwardthinkingmuseum.com/exhibitions/solo_stillings2_01.php">the famed photographers like Jamey Stillings</a>.<br />
<br />
In this article, I chose a biomimetic pattern <a href="https://www.sciencedirect.com/science/article/pii/S0038092X11004373?via%3Dihub">proposed by Noone, Torrilhon, and Mitsos</a> in 2012 based on <a href="https://en.wikipedia.org/wiki/Fermat%27s_spiral" rel="nofollow noopener" target="_blank">Fermat's spiral</a>
as the template. The Fermat spiral can be expressed as a simple parametric
equation, which in its discrete form has two
parameters: a divergence parameter <i>β</i><b> </b>that specifies the angle the next point should rotate and a radial parameter <i>b</i> that specifies how far the point should be away from the origin, as shown in Figure 1.<br />
<br />
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</div>
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; text-align: center;"><tbody>
<tr><td style="text-align: center;"><img data-li-src="https://media.licdn.com/dms/image/C5612AQGCU0ksa5-Clg/article-inline_image-shrink_1500_2232/0?e=2127081600&v=beta&t=G8FOfy4qX3r5rAlQ1O4cw_-unfBEvFnHac_Lx1MkNNc" data-media-urn="urn:li:digitalmediaAsset:C5612AQGCU0ksa5-Clg" height="300" src="https://media.licdn.com/dms/image/C5612AQGCU0ksa5-Clg/article-inline_image-shrink_1500_2232/0?e=2127081600&v=beta&t=G8FOfy4qX3r5rAlQ1O4cw_-unfBEvFnHac_Lx1MkNNc" style="margin-left: auto; margin-right: auto;" width="400" /></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i>Figure 2: Possible heliostat field patterns based on Fermat's spiral.</i></td></tr>
</tbody></table>
When <i>β </i>= 137.508° (the so-called <a href="https://en.wikipedia.org/wiki/Golden_angle" rel="nofollow noopener" target="_blank">golden angle</a>), we arrive at Vogel's model that shows the pattern of florets like the ones we see in sunflowers and daisies (Figure 1).
Before using a genetic algorithm, I first explored the design
possibilities manually by using the spiral layout manager I wrote for Energy3D. Figure 2 shows some of the interesting patterns I came up
with that appear to be sufficiently distinct. These patterns may give us some ideas about the solution space.<br />
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</div>
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; text-align: center;"><tbody>
<tr><td style="text-align: center;"><img data-li-src="https://media.licdn.com/dms/image/C5612AQGjq76nCJM-EA/article-inline_image-shrink_1500_2232/0?e=2127081600&v=beta&t=FeetQZ2q7fB779EBxDVIhCmuXW9l18_oy0jLAGSrfYA" data-media-urn="urn:li:digitalmediaAsset:C5612AQGjq76nCJM-EA" height="238" src="https://media.licdn.com/dms/image/C5612AQGjq76nCJM-EA/article-inline_image-shrink_1500_2232/0?e=2127081600&v=beta&t=FeetQZ2q7fB779EBxDVIhCmuXW9l18_oy0jLAGSrfYA" style="margin-left: auto; margin-right: auto;" width="400" /></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i>Figure 3: Standard genetic algorithm result.</i></td></tr>
</tbody></table>
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; text-align: center;"><tbody>
<tr><td style="text-align: center;"><img data-li-src="https://media.licdn.com/dms/image/C5612AQF2e1GVJ1PNVg/article-inline_image-shrink_1500_2232/0?e=2127081600&v=beta&t=9dP-4IyL_7Z6L12bSPn6LiqlymBd8TVyxWzWVi4asAI" data-media-urn="urn:li:digitalmediaAsset:C5612AQF2e1GVJ1PNVg" height="238" src="https://media.licdn.com/dms/image/C5612AQF2e1GVJ1PNVg/article-inline_image-shrink_1500_2232/0?e=2127081600&v=beta&t=9dP-4IyL_7Z6L12bSPn6LiqlymBd8TVyxWzWVi4asAI" style="margin-left: auto; margin-right: auto;" width="400" /></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i>Figure 4: Micro genetic algorithm result.</i></td></tr>
</tbody></table>
<br />
Then I used the standard genetic algorithm to find a viable solution.
In this study, I allowed only four parameters to change: the divergence
parameter <i>β</i>, the width and height of the heliostats (which affect the radial parameter <i>b</i>),
and the radial expansion ratio (the degree to which the radial distance
of the next heliostat should be relative to that of the current one in
order to evaluate how much the packing density of the heliostats should
decrease with respect to the distance from the tower). Figure 3 shows
the result after evaluating 200 different patterns, which seems to have
converged to the sunflower pattern. The corresponding divergence parameter <i>β</i>
was found to be 139.215°, the size of the heliostats to be 4.63m×3.16m, and the radial expansion ratio to be 0.0003. Note that the
difference between <i>β </i>and the golden angle cannot be used alone
as the criterion to judge the resemblance of the pattern to the
sunflower pattern as the distribution also depends on the size of the
heliostat, which affects the parameter <i>b</i>.<br />
<div class="slate-resizable-image-embed slate-image-embed__resize-full-width">
</div>
<i></i><br />
I also tried the micro genetic algorithm. Figure 4 shows the best
result after evaluating 200 patterns, which looks quite similar to
Figure 3 but performs slightly less. The corresponding divergence parameter
<i>β</i> was found to be 132.600°, the size of the heliostats to be 4.56m×3.17m, and the radial expansion ratio to be 0.00033.<br />
<div class="slate-resizable-image-embed slate-image-embed__resize-full-width">
</div>
<i></i><br />
<i></i>
In conclusion, genetic algorithms seem to be able to generate Fermat
spiral patterns that resemble the sunflower pattern, judged from the
looks of the final patterns.</div>
</div>
Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com1tag:blogger.com,1999:blog-8457990200766739016.post-19950107345827744002018-05-24T18:20:00.000-04:002018-06-05T21:14:11.057-04:00Using Artificial Intelligence to Design a Solar Farm<div dir="ltr" style="text-align: left;" trbidi="on">
<div class="reader-article-content">
Everyone loves to maximize the return of investment (ROI). If you can
effortlessly find a solution that pays a higher profit -- even only a few dollars more handsomely,
why not? The problem is that, in many complicated engineering cases in
the real world, such as designing a solar farm, we often don't know
exactly what the optimal solutions are. We may know how to get some good
solutions based on what textbooks or experts say, but no one in the world can be
100% sure that there aren't any better ones waiting to be discovered beyond
the solution space that we have explored. As humans, we can easily get
complacent and settled with the solutions that we feel good about,
leaving the job (and the reward) of finding better solutions to another
time or someone else.<br />
<br />
<b>Artificial intelligence (AI) is about to change all that</b>. As design
is essentially an evolution of solutions, AI techniques such as genetic
algorithms (GA) are an excellent fit to the nature of many design
problems and can generate a rich variety of competitive designs in the same way genetics does for biology (no two leaves are the same but they both work). These powerful tools have the potential to help people learn,
design, and discover new things. In this article, I demonstrate how GA
can be used to design a photovoltaic (PV) solar farm. As always, I first
provide a short screencast video in which I used the daily output or
profit as the objective function to speed up the animation so that you
can see the evolution driven by GA. The actual assessments are based on
using the annual output or profit as the objective function, presented
in the text that follows the video. Note that the design process is
still geared towards a single objective (i.e., the total output in kWh
or the total profit in dollars over a given period of time). Design
problems with multiple objectives will be covered later.<br />
<br />
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<br />
In GA, the solution depends largely on the choice of the objective
function (or the fitness function), which specifies how the main goal is calculated. For
example, if the main goal is to generate as much electricity as possible
on a given piece of land without the concern of the cost of the solar
panels, a design in which the solar panels are closely packed may be a good
choice. On the other hand, if the main goal is to generate as much
electricity as possible from each individual solar panel because of their high
price, a design in which rows of solar panels are far away from one
another would be a good choice. Unsurprisingly, in the case shown in the video, a single row of solar panels was found as the best solution. Aiming at maximizing the profit, the real-world problems always lie
between these two extremes, which is why they must be solved using the
principles of engineering design. The video above clearly illustrates
the design evolution driven by GA in the three cases (the two extremes
and an intermediate).<br />
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<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://media.licdn.com/dms/image/C5612AQHjo20h84DXHw/article-inline_image-shrink_1500_2232/0?e=2126476800&v=beta&t=9_IrHG8ea-ilT9uXXml9-4L8S2tBfXG1VWC623HvBAs" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-li-src="https://media.licdn.com/dms/image/C5612AQHjo20h84DXHw/article-inline_image-shrink_1500_2232/0?e=2126476800&v=beta&t=9_IrHG8ea-ilT9uXXml9-4L8S2tBfXG1VWC623HvBAs" data-media-urn="urn:li:digitalmediaAsset:C5612AQHjo20h84DXHw" height="237" src="https://media.licdn.com/dms/image/C5612AQHjo20h84DXHw/article-inline_image-shrink_1500_2232/0?e=2126476800&v=beta&t=9_IrHG8ea-ilT9uXXml9-4L8S2tBfXG1VWC623HvBAs" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i>Figure 1. An Energy3D model of an existing solar farm in
Massachusetts.</i></td></tr>
</tbody></table>
To test the usefulness of the GA implementation in Energy3D for
solving real-world problems, I picked an existing solar farm in
Massachusetts (Figure 1) to see if GA could find better solutions. A 3D
model of the solar farm had been created in the Virtual Solar Grid based on the information shown on
Google Maps and its annual output calculated using Energy3D. Because I
couldn't be exactly sure about the tilt angle, I also tweaked it a bit
manually and ensured that an optimal tilt angle for the array be chosen
(I found it to be around 32° in this case). The existing solar farm has
4,542 solar panels, capable of generating 2,255 MWh of electricity each
year, based on the analysis result of Energy3D. [I must declare here
that the selection of this site was purely for the purpose of scientific
research and any opinion expressed as a result of this research should
be viewed as exploratory and should not be considered as any kind of evaluation
of the existing solar farm and its designer(s). There might be other
factors beyond my comprehension that caused a designer to choose a
particular trade-off. The purpose of this article is to show that, if we
know all the factors needed to be considered in such a design task, we
can <b>use AI to augment our intelligence, patience, and diligence</b>.]<br />
<br />
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<tr><td style="text-align: center;"><img data-li-src="https://media.licdn.com/dms/image/C5612AQEs8RBn6oGjeA/article-inline_image-shrink_1500_2232/0?e=2126476800&v=beta&t=Pe11C-qJBF0wA5rNnCa1xor3rZJL7u45onQ5Mu05l6s" data-media-urn="urn:li:digitalmediaAsset:C5612AQEs8RBn6oGjeA" height="400" src="https://media.licdn.com/dms/image/C5612AQEs8RBn6oGjeA/article-inline_image-shrink_1500_2232/0?e=2126476800&v=beta&t=Pe11C-qJBF0wA5rNnCa1xor3rZJL7u45onQ5Mu05l6s" style="margin-left: auto; margin-right: auto;" width="377" /></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i>Figure 2. The results of 10 iterations. </i></td></tr>
</tbody></table>
Energy3D has a tool that allows the user to draw a polygon within
which the solar farm should be designed. This polygon is marked by white
lines. Using this tool, we can ensure that our solutions will always be
confined to the specified area. I used this tool to set the boundary of
the solar farm under design. This took care of an important spatial
constraint and guaranteed that GA would always generate solutions on
approximately the same land parcel as is situated by the existing solar
farm.<br />
<br />
For the objective function, we can select the total annual output,
the average annual output of a solar panel, or the annual profit. I
chose the annual profit and assumed that the generated electricity would
sell for 22.5 cents per kWh (the 2018 average retail price in
Massachusetts) and the daily cost of a solar panel (summing up the cost
of maintenance, financing, and so on) would be 20 cents. I didn't know how
accurate these ROI numbers would be. But let's just go with them for now. The
annual profit is the total sale income minus the total operational cost.
Qualitatively, we know that a higher electricity price and a lower
operational cost would both favor using more solar panels whereas a lower
electricity price and a higher operational cost would both favor using
less solar panels. Finding the sweet spots in the middle requires
quantitative analyses and comparisons of many different cases, which can be outsourced to AI.<br />
<br />
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://media.licdn.com/dms/image/C5612AQH5VINobNepCA/article-inline_image-shrink_1500_2232/0?e=2126476800&v=beta&t=M-cSqcoL0X1BcVDhEakFi5jAexhZyWV5CuHb4cJLYlk" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-li-src="https://media.licdn.com/dms/image/C5612AQH5VINobNepCA/article-inline_image-shrink_1500_2232/0?e=2126476800&v=beta&t=M-cSqcoL0X1BcVDhEakFi5jAexhZyWV5CuHb4cJLYlk" data-media-urn="urn:li:digitalmediaAsset:C5612AQH5VINobNepCA" height="238" src="https://media.licdn.com/dms/image/C5612AQH5VINobNepCA/article-inline_image-shrink_1500_2232/0?e=2126476800&v=beta&t=M-cSqcoL0X1BcVDhEakFi5jAexhZyWV5CuHb4cJLYlk" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i>Figure 3: The best design from 2,000 solutions</i></td></tr>
</tbody></table>
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<tr><td style="text-align: center;"><img data-li-src="https://media.licdn.com/dms/image/C5612AQHNWLrR0ekMmA/article-inline_image-shrink_1500_2232/0?e=2126476800&v=beta&t=h26b6sjeneeQdr7GQgsKF6JfqvITGIRC7zAeOIQUKL8" data-media-urn="urn:li:digitalmediaAsset:C5612AQHNWLrR0ekMmA" height="238" src="https://media.licdn.com/dms/image/C5612AQHNWLrR0ekMmA/article-inline_image-shrink_1500_2232/0?e=2126476800&v=beta&t=h26b6sjeneeQdr7GQgsKF6JfqvITGIRC7zAeOIQUKL8" style="margin-left: auto; margin-right: auto;" width="400" /></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i>Figure 4: The second best design from 2,000 solutions.</i></td></tr>
</tbody></table>
In Energy3D, GA always starts with the current design as part of the
first generation (so if you already have a good design, it will converge
quickly). In order for GA not to inherit anything from the existing
solar farm, I created an initial model that had only a rack with a few
solar panels on it and a zero tilt angle. The size of the population was
set to be 20. So at the beginning, this initial model would compete
with 19 randomly generated solutions and was almost guaranteed to lose
the chance to enter the next generation. In order to stop and check the
results, I let GA run for only 10 generations. For convenience, let's
call every 10 generations of GA evolution an iteration. Figure 2 shows
that GA generated solutions below the supposed human performance in the first two
iterations but quickly surpassed it after that. The solution kept
improving but got stuck in iterations 5-7 and then it advanced again and stagnated again in iterations 8-10. This process could continue
indefinitely, but I decided to terminate it after 10 iterations, or 100
generations. By this time, the software had generated and evaluated 2,000 solutions,
which took a few hours as it had to run 2,000 annual simulations for thousands of solar panels.<br />
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</div>
<i></i><br />
The best solution (Figure 3) that emerged from these 2,000 generated solutions
used 5,420 solar panels fixed at a tilt angle of 28.3° to generate 2,667 MWh per year and was about 16%
better than the existing one based on the ROI model described above.
The second best solution (Figure 4) used 4,670 solar panels fixed at a tilt angle of 38.6° to generate
2,340 MWh per year and was about 5.5% better than the existing one
based on the ROI model. Note that if we use
the average annual output per solar panel as the criterion, the second
best solution would actually be better than the best one, but we know that the average panel output is not a good choice for the fitness function as it can result in an optimal solution with very few solar panels.<br />
<br />
In conclusion, the generative design tools in Energy3D powered by AI can be used to search a large volume of the solution space and find a number of different solutions for the designer to pick and choose. The ability of AI to transcend human limitations in complex design is a significant application of AI and cannot be more exciting! We predict that future work will rely more and more on this power and today's students should be ready for the big time.</div>
</div>
Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com0tag:blogger.com,1999:blog-8457990200766739016.post-6047253501926259992018-05-18T21:17:00.001-04:002018-05-25T12:44:52.886-04:00Using Artificial Intelligence to Design Energy-Efficient Buildings<div dir="ltr" style="text-align: left;" trbidi="on">
The National Science Foundation issued <a href="https://www.nsf.gov/news/news_summ.jsp?cntn_id=245418">a statement on May 10, 2018</a> in which the agency envisions that "The effects of AI will be profound. <b>To stay competitive, all companies will, to some extent, have to become AI companies</b>. We are striving to create AI that works for them, and for all Americans." This is probably the strongest message and the clearest matching order
from a top science agency in the world about a particular area of
research thus far. The application of AI to the field of design, and more broadly, creativity, is considered by many as the <a href="https://www.ibm.com/watson/advantage-reports/future-of-artificial-intelligence/ai-creativity.html">moonshot</a> of the ongoing AI revolution, which is why I have chosen to dedicate a considerable portion of my time and effort to this strategically important area.<br />
<br />
I have added two more application categories of using genetic algorithms (GAs) to assist engineering design in <a href="http://energy3d.concord.org/" rel="nofollow noopener" target="_blank">Energy3D</a>, the main platform based on which I am striving to create a "designerly brain."
One example is to find the optimal position to add a new building with
glass curtain walls to an open space in an existing urban block so that
the new building would use the least amount of energy. The other example
is to find the optimal sizes of the windows on different sides of a
building so that the building would use the least amount of energy. To
give you a quick idea about how GAs work in these cases, I recorded the
following two screencast videos from Energy3D. To speed up the search
processes visualized in the videos, I chose the daily energy use as the objective
function and only optimized for the winter condition. The solutions
optimized for the annual energy use are shown later in this article.<br />
<br />
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<tr><td style="text-align: center;"><a href="https://media.licdn.com/dms/image/C5612AQFzDeqtrYNmQQ/article-inline_image-shrink_1500_2232/0?e=2126476800&v=beta&t=VTyk0Vfv3ybauDBa5B4kmp_EKR4r9bqt0n-dOubSWnc" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-li-src="https://media.licdn.com/dms/image/C5612AQFzDeqtrYNmQQ/article-inline_image-shrink_1500_2232/0?e=2126476800&v=beta&t=VTyk0Vfv3ybauDBa5B4kmp_EKR4r9bqt0n-dOubSWnc" data-media-urn="urn:li:digitalmediaAsset:C5612AQFzDeqtrYNmQQ" height="237" src="https://media.licdn.com/dms/image/C5612AQFzDeqtrYNmQQ/article-inline_image-shrink_1500_2232/0?e=2126476800&v=beta&t=VTyk0Vfv3ybauDBa5B4kmp_EKR4r9bqt0n-dOubSWnc" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i>Figure 1: A location of the building recommended by GA if it is in Boston.</i></td></tr>
</tbody></table>
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<tr><td style="text-align: center;"><a href="https://media.licdn.com/dms/image/C5612AQH0qmsCAVtKtg/article-inline_image-shrink_1500_2232/0?e=2126476800&v=beta&t=48jj3d2FiFBRxQQrwruhjU4k2LG8eW5MgwbCltMcR4E" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-li-src="https://media.licdn.com/dms/image/C5612AQH0qmsCAVtKtg/article-inline_image-shrink_1500_2232/0?e=2126476800&v=beta&t=48jj3d2FiFBRxQQrwruhjU4k2LG8eW5MgwbCltMcR4E" data-media-urn="urn:li:digitalmediaAsset:C5612AQH0qmsCAVtKtg" height="238" src="https://media.licdn.com/dms/image/C5612AQH0qmsCAVtKtg/article-inline_image-shrink_1500_2232/0?e=2126476800&v=beta&t=48jj3d2FiFBRxQQrwruhjU4k2LG8eW5MgwbCltMcR4E" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i>Figure 2: A location of the building recommended by GA if it is in Phoenix.</i></td></tr>
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For the first example, the energy use of a building in an urban block
depends on how much solar energy it receives. In the winter, solar
energy is good for the building as it warms up the building and saves
the heating energy. In the summer, excessive heating caused by solar
energy must be removed through air conditioning, increasing the energy
use. The exact amount of energy use per year depends on a lot of other
factors such as the fenestration of the building, its insulation, and
its size. In this demo, we only focus on searching a good location for a
building with everything else fixed. I chose a population with 32 individuals and let GA run for only five generations. Figures 1 and 2 show
the final solutions for Boston (a heating-dominant area) and Phoenix (a
cooling-dominant area), respectively. Not surprisingly, the GA results
suggest that the new building be placed in a location that has more
solar access for the Boston case and in location that has less solar
access for the Phoenix case.<br />
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<tr><td style="text-align: center;"><a href="https://media.licdn.com/dms/image/C5612AQGL50ahg246WA/article-inline_image-shrink_1500_2232/0?e=2126476800&v=beta&t=WJzBMVkH8kf6gUv0cRPEMKodO2Ocdg6cH35GYn3UAAk" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-li-src="https://media.licdn.com/dms/image/C5612AQGL50ahg246WA/article-inline_image-shrink_1500_2232/0?e=2126476800&v=beta&t=WJzBMVkH8kf6gUv0cRPEMKodO2Ocdg6cH35GYn3UAAk" data-media-urn="urn:li:digitalmediaAsset:C5612AQGL50ahg246WA" height="238" src="https://media.licdn.com/dms/image/C5612AQGL50ahg246WA/article-inline_image-shrink_1500_2232/0?e=2126476800&v=beta&t=WJzBMVkH8kf6gUv0cRPEMKodO2Ocdg6cH35GYn3UAAk" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i>Figure 3: Window sizes of a building recommended by GA for Chicago.</i></td></tr>
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<tr><td style="text-align: center;"><a href="https://media.licdn.com/dms/image/C5612AQEIEGVPdvXmnw/article-inline_image-shrink_1500_2232/0?e=2126476800&v=beta&t=CPMSIVynvjdAQ67O9l-jNeCWY8S4upYLci4TG5-0Y2E" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-li-src="https://media.licdn.com/dms/image/C5612AQEIEGVPdvXmnw/article-inline_image-shrink_1500_2232/0?e=2126476800&v=beta&t=CPMSIVynvjdAQ67O9l-jNeCWY8S4upYLci4TG5-0Y2E" data-media-urn="urn:li:digitalmediaAsset:C5612AQEIEGVPdvXmnw" height="238" src="https://media.licdn.com/dms/image/C5612AQEIEGVPdvXmnw/article-inline_image-shrink_1500_2232/0?e=2126476800&v=beta&t=CPMSIVynvjdAQ67O9l-jNeCWY8S4upYLci4TG5-0Y2E" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><i>Figure 4: Window sizes of a building recommended by GA for Phoenix. </i></td></tr>
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For the second example, the energy use of a building depends on how
much solar energy it receives through the windows and how much thermal
energy transfers through the windows (since windows typically have less
thermal resistance than walls). In the winter, while a larger window
allows more solar energy to shine into the building and warm it up
during the day, it also allows more thermal energy to escape through the
larger area, especially at night. In the summer, both solar radiation
and heat transfer through a larger window will contribute to the
increase of the energy needed to cool the building. And this complicated
relationship changes when the solution is designed for a different
climate. Figures 3 and 4 show the final solutions for Chicago and
Phoenix as suggested by the GA results, respectively. Note that not all
GA results are acceptable solutions, but they can play advisory roles
during a design process, especially for novice designers who do not have
anyone to consult with.<br />
<br />
In conclusion, artificial intelligence such as GA provides automated
procedures that can help designers find optimal solutions more
efficiently and thereby free them up from tedious, repetitive tasks if
an exhaustive search of the solution space is necessary. Energy3D
provides an accessible platform that integrates design, visualization,
and simulation seamlessly to demonstrate these potential and
capabilities. Our next step is to figure out how to translate this power into
instructional intelligence that can help students and designers develop
their abilities of creative thinking.</div>
Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com2tag:blogger.com,1999:blog-8457990200766739016.post-6660338125262518062018-02-19T20:55:00.001-05:002018-02-26T09:29:03.540-05:00 Virtual Solar Grid adds Crescent Dunes Solar Tower<div dir="ltr" style="text-align: left;" trbidi="on">
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<tr><td style="text-align: center;"><a href="https://4.bp.blogspot.com/-WsrJLPp5umU/WotatfVwAAI/AAAAAAAADOA/PIfrsh_WHl08AVldi4YFpFt_v1fB_PmXgCLcBGAs/s1600/crescent-dunes-01.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="953" data-original-width="1597" height="190" src="https://4.bp.blogspot.com/-WsrJLPp5umU/WotatfVwAAI/AAAAAAAADOA/PIfrsh_WHl08AVldi4YFpFt_v1fB_PmXgCLcBGAs/s320/crescent-dunes-01.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">The Crescent Dues Solar Tower as modeled in Energy3D</td></tr>
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<tr><td style="text-align: center;"><a href="https://2.bp.blogspot.com/--7nndu-DbLo/Wota9h-LuQI/AAAAAAAADOE/MJI-ANVrXfc-HTS4bpbSaXr_mY27g6awgCLcBGAs/s1600/crescent-dunes-02.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="953" data-original-width="1597" height="190" src="https://2.bp.blogspot.com/--7nndu-DbLo/Wota9h-LuQI/AAAAAAAADOE/MJI-ANVrXfc-HTS4bpbSaXr_mY27g6awgCLcBGAs/s320/crescent-dunes-02.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">A light field visualization in Energy3D</td></tr>
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<tr><td style="text-align: center;"><a href="https://1.bp.blogspot.com/-iTdE833VKgs/WotbtChuxMI/AAAAAAAADOc/ycikRCsSi9cpicjQUMrRSyqzQ4W4o5qJgCLcBGAs/s1600/crescent-dunes-08.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="953" data-original-width="1597" height="190" src="https://1.bp.blogspot.com/-iTdE833VKgs/WotbtChuxMI/AAAAAAAADOc/ycikRCsSi9cpicjQUMrRSyqzQ4W4o5qJgCLcBGAs/s320/crescent-dunes-08.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">A top view</td></tr>
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The Crescent Dunes Solar Power Tower is a 110 MW utility-scale concentrated solar power (CSP) plant with 1.1 GWh of molten salt energy storage, located about 190 miles northwest of Las Vegas in the United States (watch <a href="https://www.facebook.com/climatereality/videos/1207300966003212/">a video</a> about it). The plant includes a whopping number of 10,347 large heliostats that collect and focus sunlight onto a central receiver at the top of a 195-meter tall tower to heat 32,000 tons of molten salt. The molten salt circulates from the tower to some storage tanks, where it is then used to produce steam and generate electricity. Excess thermal energy is stored in the molten salt and can be used to generate power for up to ten hours, providing electricity in the evening or during cloudy hours. Unlike other CSP plants, Crescent Dunes' advanced storage technology eliminates the need for any backup fossil fuels to melt the salt and jumpstart the plant in the morning. Each heliostat is made up of 35 6×6 feet (1.8 m) mirror facets, adding up to a total aperture of 115.7 square meters. The total solar field aperture sums to an area of 1,196,778 square meters, or more than one square kilometer, in a land area of 1,670 acres (6.8 square kilometers). That is, the plant is capable of potentially collecting one seventh of all the solar energy that shines onto the field. Costing about $1 billion to construct, it was commissioned in September 2015.<br />
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<tr><td style="text-align: center;"><a href="https://2.bp.blogspot.com/-gPryVj0gynE/WotbJXOkyfI/AAAAAAAADOI/WOWY8xs6IG0mZarQiFSGZDkIPx7sYyXzACLcBGAs/s1600/crescent-dunes-07.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="953" data-original-width="1597" height="190" src="https://2.bp.blogspot.com/-gPryVj0gynE/WotbJXOkyfI/AAAAAAAADOI/WOWY8xs6IG0mZarQiFSGZDkIPx7sYyXzACLcBGAs/s320/crescent-dunes-07.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">A close-up view of accurate modeling of heliostat tracking</td></tr>
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Since <a href="https://www.linkedin.com/pulse/virtual-solar-grid-comes-online-charles-xie-1/">its inception</a> in January 2018, our <a href="http://energy.concord.org/energy3d/vsg/syw.html">Virtual Solar Grid</a> has included the Energy3D models of nearly <b>all</b> the existing large CSP power plants in the world. That covers more than 80 large CSP plants capable of generating more than 11 TWh per year. The ultimate goal of the Virtual Solar Grid is to <b>mirror every solar energy system in the world in the computing cloud</b> through crowdsourcing involving a large number of students interested in engineering, creating <b>an unprecedentedly detailed computational model for learning how to design a reliable and </b><b><b>resilient </b>power grid based completely on renewable energy</b> (solar energy in this phase). The modeling of the Crescent Dunes plant has put <a href="http://energy3d.concord.org/">our Energy3D software</a> to a stress test. Can it handle such a complex project with so many heliostats in such a large field?<br />
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<tr><td style="text-align: center;"><a href="https://4.bp.blogspot.com/-M8gbRFfNGAg/Wotbb8dURqI/AAAAAAAADOk/VI-3tPD8KVM7prAIZaQUVNF5PzyRTu5yQCEwYBhgL/s1600/crescent-dunes-10.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="953" data-original-width="1597" height="190" src="https://4.bp.blogspot.com/-M8gbRFfNGAg/Wotbb8dURqI/AAAAAAAADOk/VI-3tPD8KVM7prAIZaQUVNF5PzyRTu5yQCEwYBhgL/s320/crescent-dunes-10.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">A side view</td></tr>
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<tr><td style="text-align: center;"><a href="https://2.bp.blogspot.com/-8dN4I2AkXus/Wothi_4ztyI/AAAAAAAADOs/VgF6Qv5Fvuc7Vt-ZpNYgyao8XO0qhcbcwCLcBGAs/s1600/crescent-dunes-06.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="953" data-original-width="1597" height="190" src="https://2.bp.blogspot.com/-8dN4I2AkXus/Wothi_4ztyI/AAAAAAAADOs/VgF6Qv5Fvuc7Vt-ZpNYgyao8XO0qhcbcwCLcBGAs/s320/crescent-dunes-06.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Near the base of the tower</td></tr>
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<tr><td style="text-align: center;"><a href="https://1.bp.blogspot.com/-iVpcAYKqi54/WothofzILwI/AAAAAAAADOw/o_41C4tjjao22EtfJY_Mo9_SNMXcyjwMACLcBGAs/s1600/crescent-dunes-18.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="953" data-original-width="1597" height="190" src="https://1.bp.blogspot.com/-iVpcAYKqi54/WothofzILwI/AAAAAAAADOw/o_41C4tjjao22EtfJY_Mo9_SNMXcyjwMACLcBGAs/s320/crescent-dunes-18.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Over the shoulder of the tower</td></tr>
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<tr><td style="text-align: center;"><a href="https://4.bp.blogspot.com/-1fefEAbbgFk/WotkX3s1QwI/AAAAAAAADPA/oppjAx-ye8Uu3X678_sD3HZBlug-oxE9ACLcBGAs/s1600/crescent-dunes-05.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="953" data-original-width="1597" height="190" src="https://4.bp.blogspot.com/-1fefEAbbgFk/WotkX3s1QwI/AAAAAAAADPA/oppjAx-ye8Uu3X678_sD3HZBlug-oxE9ACLcBGAs/s320/crescent-dunes-05.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">The solar field</td></tr>
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This became my President's Day project. To make this happen, I had to first increase the resolution of Google Maps images supported in Energy3D. A free developer account of Google Maps can only get images of 640 <span style="background-color: white; color: #222222; display: inline; float: none; font-family: "roboto" , "arial" , sans-serif; font-size: x-small; font-style: normal; font-weight: 400; letter-spacing: normal; text-align: left; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;">×</span> 640 pixels. When you are looking at an area that is as big as a few square kilometers, that resolution gets you very blurry images. To fetch high-resolution images from Google without paying them, I had to basically make Energy3D download many more images and then knit them together to create a large image that forms an Earth canvas in Energy3D (hence you see a lot of Google logos and copyrights in the ground image that I could not get rid of from each patch). Once I had the Earth canvas, I then drew heliostats on top of it (that is, one by one for more than 10,000 times!) and compared their orientations and shadows rendered by Energy3D with those shown in the Google Maps images. Now, the problem is that Google doesn't tell you when the satellite image was taken. But based on the shadows of the tower and other structures, I could easily figure out an approximate time and date. I then set that time and date in Energy3D and confirmed that the shadow of the tower in the Energy3D model overlaps with that in the satellite image. After this calibration, every single virtual heliostat that I copied and pasted then automatically aligned with those in the satellite image (as long as the original copy specifies the tower that it points to), visually testifying that <b>the tracking algorithm for the virtual heliostats in Energy3D is just as good as the one used by the computers that control the motions of the real-world heliostats</b>. Matching the computer model with the satellite image is essential as the procedure ensures the accuracy of our numerical simulation.<br />
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<tr><td style="text-align: center;"><a href="https://3.bp.blogspot.com/-uI6kBSnNWkg/Wotkq60VZEI/AAAAAAAADPE/2mtsnWuLfQsiDLzhKSz7AQte8Jq402RpACLcBGAs/s1600/crescent-dunes-04.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="953" data-original-width="1597" height="190" src="https://3.bp.blogspot.com/-uI6kBSnNWkg/Wotkq60VZEI/AAAAAAAADPE/2mtsnWuLfQsiDLzhKSz7AQte8Jq402RpACLcBGAs/s320/crescent-dunes-04.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">The solar field</td></tr>
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After making numerous other improvements for Energy3D, the latest version (V7.8.4) was finally capable of modeling this colossal power plant. This includes the capability of being able to divide the whole project into nine smaller projects and then allow Energy3D to stitch the smaller 3D models together to create the full model using the Import Tool. This divide-and-conquer method makes the user interface a lot faster as neither you nor Energy3D need to deal with 9,000 existing heliostats while you are adding the last 1,000. The predicted annual output of the plant by Energy3D is 462 GWh, as opposed to the official projection of 500 GWh, assuming 90% of mirror reflectance and 25% of thermal-to-electric conversion.<br />
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One thing I had to do, though, was to double the memory requirement for the software from the default 256 MB to 512 MB for the Windows version (the Mac version is fine), which would make the software fail on really old computers that have only 256 MB of total memory (but I don't think such old computers would still work properly today anyways). The implication of this change is that, if you are a Windows user and have installed Energy3D before, you will need to re-install it using the latest installer from our website in order to take advantage of this update. If you are not sure, there is a way to know how much memory your Energy3D is allocated by checking the System Information and Preferences under the File Menu. If that number is about 250 MB, then you have to re-install the software -- if you really want to see the spectacular Crescent Dunes model in Energy3D without crashing it.
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<i>With basically only the three Ivanpah Solar Towers left to be modeled and uploaded, the Virtual Solar Grid has nearly incorporated all the operational solar thermal power plants in the world.</i> We will continue to add new CSP plants as they come online and show up in Google Maps. In our next phase, we will move to add more photovoltaic (PV) solar power plants to the Virtual Solar Grid. At this point, the proportion of the modeled capacity from PV stands at only 8% in the Virtual Solar Grid, compared with 92% from CSP. Adding PV power plants will really require crowdsourcing as there are many more PV projects in the world -- there are potentially millions of small rooftop systems in existence. On a separate avenue, <a href="https://www.nature.com/scitable/blog/eyes-on-environment/the_power_of_rooftop_solar">the National Renewable Energy Laboratory (NREL) has estimated</a> that, if we add solar panels to every square feet of usable roof area in the U.S., we could meet 40% of our total electricity need. Is their statement realistic? Perhaps only time can tell, but by adding more and more virtual solar power systems to the Virtual Solar Grid, we might be able to tell sooner.</div>
Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com0tag:blogger.com,1999:blog-8457990200766739016.post-42122330008616427102018-01-29T23:11:00.002-05:002018-02-03T21:52:15.081-05:00Virtual Solar Grid comes online<div dir="ltr" style="text-align: left;" trbidi="on">
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<tr><td style="text-align: center;"><a href="https://2.bp.blogspot.com/-X-HfC1JV24M/WnKJIB4Yt2I/AAAAAAAADNY/vWrBuLoMo2wMIlDZIUpTunWNssT5M7uVgCLcBGAs/s1600/vsg1.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="505" data-original-width="908" height="177" src="https://2.bp.blogspot.com/-X-HfC1JV24M/WnKJIB4Yt2I/AAAAAAAADNY/vWrBuLoMo2wMIlDZIUpTunWNssT5M7uVgCLcBGAs/s320/vsg1.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 1: Modeled output of the Virtual Solar Grid </td></tr>
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<tr><td style="text-align: center;"><a href="https://3.bp.blogspot.com/-8iK4tMWibPQ/Wm_mzA4a3mI/AAAAAAAADMM/oBs-Kuk2c2AUVC0wmbJSOfGOMdDifDJqACLcBGAs/s1600/vsg2.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="448" data-original-width="667" height="214" src="https://3.bp.blogspot.com/-8iK4tMWibPQ/Wm_mzA4a3mI/AAAAAAAADMM/oBs-Kuk2c2AUVC0wmbJSOfGOMdDifDJqACLcBGAs/s320/vsg2.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 2: A residential rooftop PV system.</td></tr>
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If you care about finding renewable energy solutions to environmental problems, you probably would like to join an international community of <a href="http://energy3d.concord.org/">Energy3D</a> users to model existing or design new solar power systems in the real world and contribute them to the <a href="http://energy.concord.org/energy3d/vsg/syw.html">Virtual Solar Grid</a> — a hypothetical power grid that I am developing from scratch to model and simulate interconnected solar energy systems and storage. My ultimate goal is to crowdsource an unprecedented fine-grained, time-dependent, and multi-scale computational model for anyone, believer or skeptic of renewables, to study how much of humanity's energy need can be met by solar power generation on the global scale — independent of any authority and in the spirit of citizen science. I have <a href="https://molecularworkbench.blogspot.com/2017/09/the-challenge-to-solarize-world.html">blogged about this ambitious plan before</a> and I am finally pleased to announce that an <a href="http://energy.concord.org/energy3d/vsg/syw.html">alpha version of the Virtual Solar Grid</a> has come online, of course, with a very humble beginning.<br />
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<tr><td style="text-align: center;"><a href="https://1.bp.blogspot.com/-5idXCIuE0LI/Wm_nOPxkKOI/AAAAAAAADMQ/7B-Yi64lGHYf4BEwM2HldRPZy6DUlQlDwCLcBGAs/s1600/vsg3.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="448" data-original-width="738" height="194" src="https://1.bp.blogspot.com/-5idXCIuE0LI/Wm_nOPxkKOI/AAAAAAAADMQ/7B-Yi64lGHYf4BEwM2HldRPZy6DUlQlDwCLcBGAs/s320/vsg3.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 3: The Micky Mouse solar farm in Orlando, FL.</td></tr>
</tbody></table>
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://2.bp.blogspot.com/-n2ta4RRxt3U/Wm_ntMAc7NI/AAAAAAAADMc/5AOPGGLY8VgfgddhrcEvgcWQVx3TPc-aQCLcBGAs/s1600/vsg4.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="455" data-original-width="739" height="197" src="https://2.bp.blogspot.com/-n2ta4RRxt3U/Wm_ntMAc7NI/AAAAAAAADMc/5AOPGGLY8VgfgddhrcEvgcWQVx3TPc-aQCLcBGAs/s320/vsg4.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 4: NOOR-1 parabolic troughs in Morocco.</td></tr>
</tbody></table>
As of the end of January, 2018, the Virtual Solar Grid has included 3D models of only a bit more than 100 solar energy systems, ranging from small rooftop photovoltaic solar panel arrays (10 kW) to large utility-scale concentrated solar power plants (100 MW) in multiple continents. At present, the Virtual Solar Grid has a lot of small systems in Massachusetts because we are working with many schools in the state.<br />
<br />
With this initial capacity, the Virtual Solar Grid is capable of generating roughly 4 TWh per year, approximately 0.02% of all the electricity consumed by the entire world population in 2016 (a little more than 2 PWh). Although 0.02% is too minuscule to count, it nonetheless marks the starting point of our journey towards an important goal of engaging and supporting anyone to explore the solar energy potential of our planet with serious engineering design. In a sense, you can think of this work as inventing a "Power Minecraft" that would entice people to participate in a virtual quest for switching humanity's power supply to 100% renewable energy.<br />
<br />
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://3.bp.blogspot.com/-cvas0Bjh5Co/Wm_qouWCJhI/AAAAAAAADMo/is3lMotnR1g14yzI4hh2kalSdewYEAblACLcBGAs/s1600/vsg5.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="455" data-original-width="738" height="197" src="https://3.bp.blogspot.com/-cvas0Bjh5Co/Wm_qouWCJhI/AAAAAAAADMo/is3lMotnR1g14yzI4hh2kalSdewYEAblACLcBGAs/s320/vsg5.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 5: Khi Solar One solar power tower in South Africa.</td></tr>
</tbody></table>
<div style="text-align: right;">
</div>
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://2.bp.blogspot.com/-tNcKOXPaxfE/WnZ1UCE5KiI/AAAAAAAADN0/p7fVCiu5Wqo6faO-9d4SLQ-N425xDDDZACLcBGAs/s1600/vsg9.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="591" data-original-width="839" height="225" src="https://2.bp.blogspot.com/-tNcKOXPaxfE/WnZ1UCE5KiI/AAAAAAAADN0/p7fVCiu5Wqo6faO-9d4SLQ-N425xDDDZACLcBGAs/s320/vsg9.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 6: PS 10 and PS 20 in Spain.</td></tr>
</tbody></table>
The critical infrastructure underlying the Virtual Solar Grid is our free, versatile <a href="http://energy3d.concord.org/">Energy3D</a> software that allows anyone from a middle school student to a graduate school student to model or design any photovoltaic or concentrated solar power systems, down to the exact location and specs of individual solar panels or heliostats. Performance analysis of solar power systems in Energy3D is based on a growing database of solar panel brand models and weather data sets for nearly 700 regions in every habitable continent. To construct a grid, micro or global, an Energy3D model can be geotagged — the geolocation is automatically set when you import a Google Maps image into an Energy3D model. Such a virtual model, when uploaded to the Virtual Solar Grid, will be deployed to a Google Maps application that shows exactly where it is in the world and how much electricity it produces at a given hour on a given day under average weather conditions. This information will be used to investigate how solar power and other renewables, with thermal and electric storage, can be used to provide base loads and meet peak demands for a power grid of an arbitrary size, so to speak.<br />
<br />
Finally, it is important to note that the Virtual Solar Grid project is generously funded by the U.S. National Science Foundation through <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1721054">grant number #1721054</a>. Their continuous support of my work is deeply appreciated.</div>
Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com1tag:blogger.com,1999:blog-8457990200766739016.post-47616332654677835782017-12-16T14:19:00.002-05:002018-01-09T20:14:01.349-05:00Energy2D used as a simulation tool in astrobiology research<div dir="ltr" style="text-align: left;" trbidi="on">
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<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://3.bp.blogspot.com/-KIQy4YXWDVk/WjVuSRrGRAI/AAAAAAAADLQ/Wb_VfOErkLMt0HSdBYoqm5k8J_KgmM13gCLcBGAs/s1600/Untitled-2.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="834" data-original-width="1170" height="228" src="https://3.bp.blogspot.com/-KIQy4YXWDVk/WjVuSRrGRAI/AAAAAAAADLQ/Wb_VfOErkLMt0HSdBYoqm5k8J_KgmM13gCLcBGAs/s320/Untitled-2.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 1: Frasassi Caves, Italy (credit: <i>Astrobiology</i>)</td></tr>
</tbody></table>
Deposition of minerals in caves may be affected by microbes. Geochemical analysis of these minerals can reveal biosignatures of subsurface life on a planet such as the Mars. Research in this area can help NASA build subsurface life probes for future planetary missions.<br />
<br />
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://3.bp.blogspot.com/-zQtQRsYKq1A/WjVuOXIM_tI/AAAAAAAADLM/oL1VKJdWGxEYfITo7YezNKLO21K405tjwCLcBGAs/s1600/Untitled-1.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="799" data-original-width="1517" height="168" src="https://3.bp.blogspot.com/-zQtQRsYKq1A/WjVuOXIM_tI/AAAAAAAADLM/oL1VKJdWGxEYfITo7YezNKLO21K405tjwCLcBGAs/s320/Untitled-1.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 2: Energy2D simulations (credit: <i>Astrobiology</i>)</td></tr>
</tbody></table>
<a href="http://www.liebertpub.com/ast">Astrobiology</a>, a peer-reviewed scientific journal covering research on the origin, evolution, distribution and future of life across the universe, just published a research paper titled "<a href="http://online.liebertpub.com/doi/abs/10.1089/ast.2017.1650">Transport-Induced Spatial Patterns of Sulfur Isotopes (δ<sup>34</sup>S) as Biosignatures</a>" by a group of researchers at Pennsylvania State University, the University of Texas at El Paso, and Rice University. The lead author is Dr. Muammar Mansor. The researchers analyzed sample sites in the Frasassi Caves, Italy (Figure 1) and used <a href="http://energy2d.concord.org/">Energy2D</a> to simulate the effects of convection and diffusion on the chemical deposition processes (Figure 2). According to the paper, the results of the deposition simulated using Energy2D are consistent with the data collected from the cave sites, suggesting the importance of the effect of natural convection.</div>
<br />
This is the second paper that uses Energy2D in astrobiology research (and the 16th published paper that used Energy2D in scientific research to simulate a natural or man-made system). In <a href="http://molecularworkbench.blogspot.com/2016/01/harvard-scientists-use-energy2d-to.html">the first paper</a>, Energy2D was used to simulate the thermal conditions for the origin of life. Once again, the publication of this paper provides fresh evidence for the broader impacts of our work.</div>
Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com0tag:blogger.com,1999:blog-8457990200766739016.post-50057467276553401952017-11-24T23:42:00.000-05:002017-11-27T12:32:24.588-05:00Energy3D uses intelligent agents to create adaptive feedback based on analyzing the "DNA of design"<div dir="ltr" style="text-align: left;" trbidi="on">
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://3.bp.blogspot.com/-WMM04tBx7Uc/WhjXh-kEdBI/AAAAAAAADK8/rWp9Fgqww7YU8C-fsfXdWy7KIQJTdafzgCLcBGAs/s1600/1.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="558" data-original-width="821" height="217" src="https://3.bp.blogspot.com/-WMM04tBx7Uc/WhjXh-kEdBI/AAAAAAAADK8/rWp9Fgqww7YU8C-fsfXdWy7KIQJTdafzgCLcBGAs/s320/1.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 1: A simple case of teaching thermal insulation.</td></tr>
</tbody></table>
<a href="http://energy3d.concord.org/">Energy3D</a> is a "smart" CAD tool because it can monitor the designer's behavior in real time, based on which it can generate feedback to the designer to regulate the design behavior. This capacity has tremendous implications to learning and teaching scientific inquiry and engineering design with open-ended nature that requires, ideally, one-to-one tutoring so intense that no teacher can easily provide in real classrooms.<br />
<br />
The computational mechanism for generating feedback in Energy3D is based on <a href="https://en.wikipedia.org/wiki/Intelligent_agent">intelligent agents</a>, which consist of sensors and actuators (in very generic terms). In Energy3D, all the events are logged behind the scenes. The events provide the raw data stream from which various sensors produce signals based on subsets of the raw data. For instance, a sensor can be created to monitor any event related to solar panels of a house. An agent then uses a <a href="https://en.wikipedia.org/wiki/Decision_tree_model">decision tree model</a> to determine which actuators should be called to provide feedback to the user or direct Energy3D to change its state. For instance, if a solar panel is detected to be placed on the north-facing roof, the agent can remind the designer to rethink about the decision. Just like what a teacher may do, the agent can even suggest a comparative analysis between a solar panel on the north-facing roof and a solar panel on the south-facing or west-facing roof. Although this type of inquiry and design can be also taught using directly scaffolded instruction that
guides students to explore step by step, in practice we have found the
effect of this approach often diminishes because many students do not
read instruction carefully enough and remember them long enough.
It is also challenging for teachers to guide the whole class through this kind of long learning process as students often pace differently. Adaptive feedback provides a way to help students only when they need
or just when a need is detected, thus providing a better chance to deliver effective instruction.<br />
<br />
Let's look at a very simple example. Figure 1 shows a learning activity, the goal of which is to teach how the thermal property of a wall, called the <a href="https://en.wikipedia.org/wiki/R-value_(insulation)">U-value</a>, affects the energy use of a house. Many students may walk away with a shallow understanding that the higher the U-value is, the more energy a house uses. The challenge is to help them deepen their understanding. For example, how can we make sure that students will collect enough data points to discover that the energy a house uses is proportional to the U-value? How can we support them to find out that the relationship is independent of seasonal change, wall orientation, and solar radiation (e.g., a lower U-value is good in both summer and winter, irrespective of whether or not the wall faces the sun). Helping students accomplish this level of understanding through inquiry-based activities is by no means a trivial task, even in this seemingly simple example. Let's explore what we may do in Energy3D now that we have a way to monitor students' interactions with it.<br />
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<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://1.bp.blogspot.com/-kvHQKZL8BfE/WhjSQcW-58I/AAAAAAAADKs/3IMp8oRwpf0eALaPd_rUNc97d255GdWuACLcBGAs/s1600/Untitled-2.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="107" data-original-width="227" height="150" src="https://1.bp.blogspot.com/-kvHQKZL8BfE/WhjSQcW-58I/AAAAAAAADKs/3IMp8oRwpf0eALaPd_rUNc97d255GdWuACLcBGAs/s320/Untitled-2.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 2: An event sequence coded like a DNA sequence.</td></tr>
</tbody></table>
In nearly all software that support learning and teaching, the events during a process can be coded as a string with characters representing the events and ordered by their timestamps, such as Figure 2. In this case, <i>A</i> represents an analysis event in the Energy3D CAD tool, <i>U</i> represents an event of changing the U-value of a wall, <i>C</i> represents an event of changing the date for the energy simulation, a questionmark (<i>?)</i> represents an event of requesting help from the software, an underscore (_) represents an inactive time period longer than a certain threshold, and * is a wildcard that represents any other event "silenced" in this expression in order to reduce the dimensionality of the problem. For those who know a bit about bioinformatics, this resembles a DNA sequence. In the context of Energy3D, we may also call it as <i>the DNA of a design</i>, if that helps your imagination.<br />
<br />
<div class="separator" style="clear: both; text-align: center;">
</div>
Now that we have converted the sequence of events into a string, we can use all sorts of techniques that have been developed to analyze strings to analyze these events, including those developed in bioinformatics such as <a href="https://en.wikipedia.org/wiki/Sequence_alignment">sequence alignment</a> or those developed in <a href="https://en.wikipedia.org/wiki/Natural_language_processing">natural language processing</a>. In this article, I am going to show how the widely-supported <a href="https://en.wikipedia.org/wiki/Regular_expression">regular expressions</a> (regex) can be used as a technique to detect whether a certain type of event or a certain combination of events occurred or how many times it occurred. I feel that regex, in our case, may be more accurate than <a href="https://en.wikipedia.org/wiki/Edit_distance">edit distances</a> such as the <a href="https://en.wikipedia.org/wiki/Levenshtein_distance">Levenshtein distance</a> in matching the pattern. For example, a single substitution of event may represent a very different process despite the short edit distance.<br />
<br />
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://1.bp.blogspot.com/-5Sd8o3rxr78/WhjQxlPaAuI/AAAAAAAADKg/PBeaih6oCi85Dx1cE5Fb6FT2006RXArFgCLcBGAs/s1600/Untitled-1.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="85" data-original-width="225" height="120" src="https://1.bp.blogspot.com/-5Sd8o3rxr78/WhjQxlPaAuI/AAAAAAAADKg/PBeaih6oCi85Dx1cE5Fb6FT2006RXArFgCLcBGAs/s320/Untitled-1.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 3: A sequence that shows high usage of feedback </td></tr>
</tbody></table>
We know that, a fundamental skill of inquiry is to keep everything else fixed but change only one variable at a time and then test how the system's output depends on that variable. Through this process of inquiry, we learn the meaning of that variable, as explained by <a href="https://www.aaas.org/sites/default/files/migrate/uploads/InquiryPart1.pdf">Bruce Alberts</a>, former president of the National Academy of Sciences and former Editor-in-Chief of the Science Magazine. In the example discussed here, that variable is the U-value of a selected wall of the house and the test is the simulation-based analysis. A pattern that has alternating <i>U</i> and <i>A</i> characters in the event string suggests a high probability of inquiry, which can be captured using a simple regex such as (U[_\\*\\?]*A)+. Between <i>U</i> and <i>A</i>, however, there may be other types of events that may or may not exist to weaken the probability or compromise the rigor. For example, changing the color of the wall between <i>U</i> and <i>A</i> may also result in an additional difference in energy use of the house that originates from the absorption of solar radiation by the external surface of the wall and has nothing to do with its U-value. In this case, changing multiple variables at a time appears to be a violation of the aforementioned inquiry principle that should be called out by the agent using another regex to analyze the substring between <i>U</i> and <i>A</i>.<br />
<br />
An interesting feature in Energy3D is that feedback itself is also logged. Figure 3 shows a sequence that has an alternation pattern similar to that of Figure 2, but it records a type of behavior showing that the user may rely overly on feedback from the system to learn (the questionmarks in the string stand for feedback requests made by the user) and avoid deep thinking on their own. This may be a common problem in many intelligent tutors (sometimes this behavior is called "gaming the system").<br />
<br />
The development of data mining and intelligent agents in Energy3D is opening interesting opportunities of research that will only grow more important in the era of artificial intelligence (AI). We are excited to be part of this wave of AI innovation.</div>
Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com0tag:blogger.com,1999:blog-8457990200766739016.post-63063752660680448392017-11-21T13:14:00.002-05:002017-11-21T13:14:53.699-05:00General Motors funds engineering education based on Energy3D<div dir="ltr" style="text-align: left;" trbidi="on">
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://1.bp.blogspot.com/-pELmWTwhFjo/WftEbDPkFyI/AAAAAAAADIY/h3FsCKasjmIrX9h_pqyFTBXuG-kP0REkgCLcBGAs/s1600/gm-logo.png" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="558" data-original-width="937" height="190" src="https://1.bp.blogspot.com/-pELmWTwhFjo/WftEbDPkFyI/AAAAAAAADIY/h3FsCKasjmIrX9h_pqyFTBXuG-kP0REkgCLcBGAs/s320/gm-logo.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Designing a parking lot solar canopy at Detroit Airport</td></tr>
</tbody></table>
General Motors (GM), along with other <a href="http://re100.org/">RE100 companies</a>, has <a href="http://media.gm.com/media/us/en/gm/news.detail.html/content/Pages/news/us/en/2016/sep/0914-renewable-energy.html">committed</a>
to powering its worldwide factories and offices with 100% renewable
energy by 2050. Last month, the company furthered its commitment by
giving the Engineering Computation Team at the Concord Consortium a
$200,000 grant to promote engineering education using renewable energy
as a learning context and artificial intelligence as a teaching
assistant.<br />
<br />
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://4.bp.blogspot.com/-remqD153CsY/WftFfUosJEI/AAAAAAAADIk/C91SUAShv88GxAf5HVc0NSzMYbDV1qV3ACLcBGAs/s1600/Untitled-1.png" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="558" data-original-width="937" height="190" src="https://4.bp.blogspot.com/-remqD153CsY/WftFfUosJEI/AAAAAAAADIk/C91SUAShv88GxAf5HVc0NSzMYbDV1qV3ACLcBGAs/s320/Untitled-1.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Modeling GM's rooftop solar arrays in Baltimore, MD</td></tr>
</tbody></table>
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="clear: right; float: right; margin-bottom: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://2.bp.blogspot.com/-2J4RN8Or_ZY/WeEtv8NuygI/AAAAAAAADHI/80zvSbxkfsM1T9EQL7aP4vfHkQPlSHM6QCLcBGAs/s1600/Untitled-2.png" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="565" data-original-width="1003" height="180" src="https://2.bp.blogspot.com/-2J4RN8Or_ZY/WeEtv8NuygI/AAAAAAAADHI/80zvSbxkfsM1T9EQL7aP4vfHkQPlSHM6QCLcBGAs/s320/Untitled-2.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Modeling GM's solar arrays in Warren, MI</td></tr>
</tbody></table>
The project will use our signature <a href="http://energy3d.concord.org/">Energy3D</a>
software, which is a one-stop-shop CAD tool for designing and
simulating all kinds of solar power systems including photovoltaic (PV)
and concentrated solar power (CSP), both of which have reached a very
competitive cost of merely 5¢ per kWh or below in the world market. A
unique feature of Energy3D is its ability to collect and analyze
"atomically" fine-grained process data while users are designing with
it. This capability makes it possible for us to develop <a href="https://en.wikipedia.org/wiki/Machine_learning">machine learning</a> algorithms to understand users' design behaviors, based on which we can develop <a href="https://en.wikipedia.org/wiki/Intelligent_agent">intelligent agents</a> to help users design better products and even unleash their creativity.<br />
<br />
The
generous grant from GM will allow us to bring this incredible
engineering learning tool and the curriculum materials it supports to
more science teachers across New England. It will also help extend our
fruitful collaboration with the <a href="http://vhslearning.org/">Virtual High School</a> (VHS) to convert our <a href="http://energy.concord.org/energy3d/sites.html">Solarize Your World</a>
curriculum into an online course for sustainable engineering. VHS
currently offers more than 200 titles to over 600 member schools.
Through their large network, we hope to inspire and support more
students and teachers to join the crucial mission that GM and other
RE100 companies are already undertaking.<br />
<br />
By supporting
today's students to learn critical engineering design skills needed to
meet the energy and environmental challenges, GM is setting an example
of preparing tomorrow's workforce to realize its renewable energy
vision.</div>
Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com0tag:blogger.com,1999:blog-8457990200766739016.post-44182922455384042182017-11-20T09:45:00.001-05:002017-11-20T13:53:48.078-05:00High Frequency Electronics and Thermtest feature Energy2D<div dir="ltr" style="text-align: left;" trbidi="on">
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://3.bp.blogspot.com/-5Xejb5JsWTY/WhLmPMlIKvI/AAAAAAAADJk/CeUD_v899ssorFvqQuRzYUDzqCPzz1qbwCLcBGAs/s1600/Untitled-1.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="780" data-original-width="583" height="320" src="https://3.bp.blogspot.com/-5Xejb5JsWTY/WhLmPMlIKvI/AAAAAAAADJk/CeUD_v899ssorFvqQuRzYUDzqCPzz1qbwCLcBGAs/s320/Untitled-1.png" width="239" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Credit: High Frequency Electronics</td></tr>
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<a href="http://highfrequencyelectronics.com/">High Frequency Electronics</a> is a magazine for engineers. In the cover article titled "<a href="http://highfrequencyelectronics.com/index.php?option=com_content&view=article&id=1885:substrate-selection-can-simplify-thermal-management&catid=149:2017-11-november-articles&Itemid=189"><i>Substrate Selection Can Simplify Thermal Management</i></a>" in its November 2017 issue, author John Ranieri included our <a href="http://energy2d.concord.org/">Energy2D</a> software as one of the modeling tools recommended to the reader, alongside with mainstream commercial products from industry leaders such as Mentor Graphics and ANSYS. The software is also featured by <a href="https://thermtest.com/thermal-resources/energy2d-heat-transfer-simulations">Thermtest</a>, a UK-based company that focuses on thermophysical instruments. Thermtest supplements the software with a database of standard materials, making it easier for engineers to use.<br />
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<tr><td style="text-align: center;"><a href="https://4.bp.blogspot.com/-48KDyDo2m60/WhMkbViT25I/AAAAAAAADKA/ckUnbOFcnus71ah5BFobSBS4BJYv7TDygCLcBGAs/s1600/Untitled-4.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="756" data-original-width="756" height="320" src="https://4.bp.blogspot.com/-48KDyDo2m60/WhMkbViT25I/AAAAAAAADKA/ckUnbOFcnus71ah5BFobSBS4BJYv7TDygCLcBGAs/s320/Untitled-4.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">An Energy2D model of a heat source and a heat sink</td></tr>
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According to the article, "heat haunts many RF/microwave and power electronics circuits and can limit performance and reliability. The heat generated by a circuit is a function of many factors, including input power, active device efficiencies, and losses through passive devices and transmission lines. It is often not practical to disperse heat from a circuit by convection fan-driven cooling, and heat must be removed from sensitive components and devices, by creating a thermal path to a metal enclosure or heat sink with good thermal conductivity." As a thermal simulation tool, Energy2D can certainly be very useful in helping engineers conceptualize and design such thermal paths.<br />
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More importantly, Energy2D can make your engineering experience as fun as playing a sandbox game! As one of our users recently wrote, "I am working as consulting engineer and we often have to make quick estimations where a steady-state node model is too simplified and setting up a complex FEM model is overkill. Energy2D is a very handy tool for something [like] that and I like the click'n'play sandbox feeling in combination with the physical correctness. I never thought FEM could be that fun."<br />
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Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com0tag:blogger.com,1999:blog-8457990200766739016.post-75506334304161954502017-11-04T23:42:00.002-04:002017-11-05T08:45:04.471-05:00Energy3D allows users to select brand name solar panels<div dir="ltr" style="text-align: left;" trbidi="on">
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<tr><td style="text-align: center;"><a href="https://3.bp.blogspot.com/-FR5CmbqYnJY/Wf5_nOwM8QI/AAAAAAAADI4/Oc18kSm03YEgWnvzAcsbGePYlAe1QnERACLcBGAs/s1600/Untitled-1.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="558" data-original-width="931" height="189" src="https://3.bp.blogspot.com/-FR5CmbqYnJY/Wf5_nOwM8QI/AAAAAAAADI4/Oc18kSm03YEgWnvzAcsbGePYlAe1QnERACLcBGAs/s320/Untitled-1.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 1: 20 brand name solar panels in Energy3D</td></tr>
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<tr><td style="text-align: center;"><a href="https://1.bp.blogspot.com/-gcbmDdC55EQ/Wf6HqFuUENI/AAAAAAAADJQ/0hAKMWrIutATaTzj6wOdsi_O1TemBFkbQCLcBGAs/s1600/Untitled-1.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="400" data-original-width="600" height="212" src="https://1.bp.blogspot.com/-gcbmDdC55EQ/Wf6HqFuUENI/AAAAAAAADJQ/0hAKMWrIutATaTzj6wOdsi_O1TemBFkbQCLcBGAs/s320/Untitled-1.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 2: The daily outputs of 20 types of solar panels</td></tr>
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Previous versions of <a href="http://energy3d.concord.org/">Energy3D</a> were based on a generic model of solar panel, which users can set its properties such as solar cell type, peak efficiency, panel dimension, color, nominal operating cell temperature, temperature coefficient of power, and so on. While it is essential for users to be able to adjust these parameters and learn what they represent and how they affect the output, it is sometimes inconvenient for a designer to manually set the properties of a solar panel to those of a brand name.<br />
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<tr><td style="text-align: center;"><a href="https://3.bp.blogspot.com/-36L9W991F8g/Wf6DJ0zBKPI/AAAAAAAADJE/YK7v3ohJ6oI-fFRXyB8Qo0yFQEicMLelACLcBGAs/s1600/Untitled-1.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="558" data-original-width="931" height="190" src="https://3.bp.blogspot.com/-36L9W991F8g/Wf6DJ0zBKPI/AAAAAAAADJE/YK7v3ohJ6oI-fFRXyB8Qo0yFQEicMLelACLcBGAs/s320/Untitled-1.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 3: The Micky Mouse solar farm</td></tr>
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From Version 7.4.4, I started to add support of brand name solar panels to Energy3D. Twenty brand names were initially added to this version (Figure 1). These models are: ASP-400M (Advanced Solar Photonics), CS6X-330M-FG (Canadian Solar), CS6X-330P-FG (Canadian Solar), FS-4122-3 (First Solar), HiS-M280MI (Hyundai), HiS-S360RI (Hyundai), JAM6(K)-60-300/PR (JA Solar), JKM300M-60 (Jinko), LG300N1C-B3 (LG), LG350Q1K-A5 (LG), PV-UJ235GA6 (Mitsubishi), Q.PRO-G4 265 (Q-cells), SPR-E20-435-COM (SunPower), SPR-P17-350-COM (SunPower), SPR-X21-335-BLK (SunPower), SPR-X21-345 (SunPower), TSM-325PEG14(II) (Trina Solar), TSM-365DD14A(II) (Trina Solar), VBHN330SA16 (Panasonic), and YL305P-35b (Yingli). Figure 2 shows a comparison of their daily outputs in Boston on June 22 when they are laid flat (i.e., with zero tilt angle). Not surprisingly, a smaller solar panel with a lower cell efficiency produces less electricity.<br />
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Note that these models are relatively new. There are hundreds of older and other types of solar panels that will take a long time to add. If your type is not currently supported, you can always fall back to defining it using the "Custom" option, which is the default model for a solar panel.<br />
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Adding these brand names helped me figure out that the solar panels deployed in the Micky Mouse Solar Farm in Orlando (Figure 3) are probably from First Solar -- only they make solar panels of such a relatively small size (1200 mm <span style="background-color: white; color: #222222; display: inline; float: none; font-family: "roboto" , "arial" , sans-serif; font-size: 16px; font-style: normal; font-weight: normal; letter-spacing: normal; text-align: left; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;">×</span> 600 mm).</div>
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Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com0tag:blogger.com,1999:blog-8457990200766739016.post-70370174676904651032017-10-14T20:20:00.002-04:002017-10-16T10:20:28.248-04:00The 2017 Energy Innovation Forum<div dir="ltr" style="text-align: left;" trbidi="on">
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<a href="https://2.bp.blogspot.com/-q2VInyjhJ-U/WeS_g-OrvBI/AAAAAAAADIE/vb5w8tCdp8Yktp-PCcu98rJiWijGH_N8QCLcBGAs/s1600/energy-innovation-forum-isv.png" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" data-original-height="1200" data-original-width="1600" height="240" src="https://2.bp.blogspot.com/-q2VInyjhJ-U/WeS_g-OrvBI/AAAAAAAADIE/vb5w8tCdp8Yktp-PCcu98rJiWijGH_N8QCLcBGAs/s320/energy-innovation-forum-isv.png" width="320" /></a>We are invited to present at <a href="https://www.uml.edu/conferences/energy-innovation-forum/schedule.aspx">the Energy Innovation Forum on October 18</a> organized by the University of Massachusetts Lowell and the Massachusetts Clean Energy Center. The event will connect about 30 companies in Massachusetts with funders, investors, university researchers, and industry leaders to stimulate innovations in energy technologies.<br />
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<a href="https://4.bp.blogspot.com/-Tuv3fBphqTY/WeS_i3jtZ8I/AAAAAAAADII/dK7yTwf83gU3JtWDBGDc3NKz-uMo312PQCLcBGAs/s1600/energy-innovation-forum-vsg.png" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" data-original-height="1200" data-original-width="1600" height="240" src="https://4.bp.blogspot.com/-Tuv3fBphqTY/WeS_i3jtZ8I/AAAAAAAADII/dK7yTwf83gU3JtWDBGDc3NKz-uMo312PQCLcBGAs/s320/energy-innovation-forum-vsg.png" width="320" /></a>For those who cannot attend the event, I am sharing our two posters here. You can also take a look at the PowerPoint slides for the <a href="http://energy.concord.org/~xie/papers/Energy-Innovation-Forum-Xie-IR.pdf">Infrared Street View Project</a> and the <a href="http://energy.concord.org/~xie/papers/Energy-Innovation-Forum-Xie-Solar.pdf">Virtual Solar Grid Project</a> (we will do both oral and poster presentations). Both projects focus on developing a unique crowdsourcing model that integrates STEM education and energy research. The projects provide examples of using citizen science to support and engage a large number of students to learn science and engineering and participate in large-scale energy research.<br />
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The Infrared Street View Project will support research and education in the field of energy efficiency whereas the Virtual Solar Grid Project will support research and education in the field of renewable energy (primarily solar energy at present). Both projects are based on cutting-edge technologies being developed in my lab.</div>
Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com0tag:blogger.com,1999:blog-8457990200766739016.post-59888604106720890802017-09-26T21:31:00.001-04:002017-10-14T19:36:36.232-04:00The challenge to solarize the world<div dir="ltr" style="text-align: left;" trbidi="on">
More and more <a href="https://www.forbes.com/sites/uhenergy/2017/03/31/100-renewables-by-2050-germany-pays-the-price-for-its-ambition/#149095f91e98">nations</a> and <a href="https://www.forbes.com/sites/trevornace/2017/08/01/california-goes-all-in-100-percent-renewable-energy-by-2045/#3fa01923570f">regions</a> in the world are planning to switch their power supplies to 100% renewable resources by midcentury. There has been, however, a well-publicized debate among scientists about the feasibility of powering the entire United States with only wind, water, and solar energy, triggered mostly by <a href="http://www.pnas.org/content/112/49/15060">a recent paper</a> by Stanford professor <a href="https://en.wikipedia.org/wiki/Mark_Z._Jacobson">Mark Jacobson</a> and colleagues. Both <a href="http://www.pnas.org/content/114/26/E5021">proponents</a> and <a href="http://www.pnas.org/content/114/26/6722">opponents</a> are leading energy researchers who support their claims with sophisticated computational models. Given the magnitude and complexity of the problem, there will likely be no clear winner in the near future. But the debate will continue to <a href="https://www.theguardian.com/commentisfree/2017/apr/29/bernie-sanders-climate-change-big-oil">influence</a> our energy and environmental policies in the years to come.<br />
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Since the world also belongs to the young, we are obliged to find a way to engage them in this high-stakes debate. Regardless of the sides people take, few would dispute the strategic importance of educating and preparing energy consumers and workforce of tomorrow. Motivating youth is so vital in Bill Gates’ call for an “energy miracle” that he urged high school students to “get involved” in the energy quest in <a href="https://www.gatesnotes.com/2016-Annual-Letter">his 2016 annual letter</a>. But, apart from becoming a conscientious user of energy, how can students make meaningful contributions?<br />
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<tr><td style="text-align: center;"><a href="https://3.bp.blogspot.com/-SrSdoe0sPKk/Wcr6kMxc26I/AAAAAAAADGY/7lK5ewpb69cDX0O9X1boWFHsixxpuSb8ACLcBGAs/s1600/Untitled-1.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="484" data-original-width="482" height="320" src="https://3.bp.blogspot.com/-SrSdoe0sPKk/Wcr6kMxc26I/AAAAAAAADGY/7lK5ewpb69cDX0O9X1boWFHsixxpuSb8ACLcBGAs/s320/Untitled-1.png" width="318" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 1: Energy3D covers nearly 600 regions in 185 countries.</td></tr>
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We envision a cyberinfrastructure that works like an “Energy Minecraft” to inspire and support millions of students to take on the energy challenge at the grassroots level on a global scale. On this platform, students will learn basic science concepts and engineering principles. Equipped with the knowledge and skills, they will then crowd-design an unprecedentedly fine-grained computational model that consists of millions of virtual solar panels, reflecting mirrors, and wind turbines accurately positioned around the world and connected to virtual storages and grids. A multiscale model with all these low-level details does not exist yet, but it may be a holy grail in energy research that can potentially settle the case and even provide a blueprint going forward to a 100% renewable energy future if possible at all.<br />
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This article introduces the <i>Solarize Your World</i> program, the first step towards realizing the above vision. Although the program currently focuses on solar energy, it has the essential elements of a computational model capable of supporting both STEM education and energy research. And it can be extended to include other renewables such as wind, hydroelectric, and geothermal energy.<br />
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<b>The complexity of modeling solar power in the real world</b></div>
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<tr><td class="tr-caption" style="text-align: center;">Fig. 2: Learn, apply, and explore</td></tr>
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The sun is a gigantic nuclear fusion reactor in the sky that emits a massive amount of energy. <a href="http://blogs.ucl.ac.uk/energy/2015/05/21/fact-checking-elon-musks-blue-square-how-much-solar-to-power-the-us/">Elon Musk</a> has famously asserted that covering “a fairly small corner” of a state like Nevada with solar panels can generate enough energy for the whole country. This makes you wonder what scientists are really debating about.<br />
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It turns out that building a reliable solar infrastructure is not as simple as laying down billions of solar panels in a square of 100×100 miles. There are countless technical, economic, and social constraints for solar deployment in reality. For example, people do not have unlimited space and budgets. Some are concerned about the aesthetics of buildings and landscapes with solar panels in sight. Governmental policies drive the cost of solar energy, hence people’s interest, up and down. Energy storage is needed to overcome solar intermittency to provide electricity after sun-set and grid stability at all time. A significant amount of energy is lost during the transmission from utility-scale solar power plants to population centers. All things considered, we have a problem far more complicated than Musk’s ballpark statement. This is why the National Renewable Energy Laboratory has been conducting research on estimating the solar energy potential of the country (e.g., see "<a href="https://www.nrel.gov/news/press/2016/24662.html">Rooftop Solar Photovoltaic Technical Potential in the United States: A Detailed Assessment</a>" by Pieter Gagnon, Robert Margolis, Jennifer Melius, Caleb Phillips, and Ryan Elmore in 2016).<br />
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<b>A crowdsourcing model that integrates education and research</b><br />
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<tr><td class="tr-caption" style="text-align: center;">Fig. 3: Photovoltaic solar farms in Energy3D</td></tr>
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A more accurate assessment of the planet’s true solar potential is to identify all possible locations where suitable types of solar power can be realistically deployed and compute their minute-by-minute outputs to global grids and storages for a cycle of 24 hours under typical meteorological conditions. To evaluate the cost effectiveness of this giant distributed network, a mix of financing models driven by local economics and policies can be used to estimate the scale of investment that needs to be made over a certain period of time. Creating such a multiscale, time-dependent model with details down to instantaneous outputs and levelized costs of individual solar modules is a daunting task that no single researcher can do. But we can call for help from millions of students who know and care about their corners of the world more than any outsider. The challenge is to teach them the science and empower them with appropriate engineering tools so that they can join the energy quest.<br />
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<i>Solarize Your World</i> is based on our <a href="http://energy3d.concord.org/">Energy3D</a> software, a revolutionary CAD tool for anyone to design any type of solar power system in cyberspace and calculate its hourly, daily, or yearly out-puts based on numerical simulation from first principles. With weather data of nearly 600 regions in 185 countries (Figure 1), Energy3D can produce satisfactory results for most parts of the inhabited world, enabling millions to work on local projects. The ultimate goal of Energy3D is to turn the tedious job of engineering design into a fun game like Minecraft, making learning, discovery, and invention playful experiences for all.<br />
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<b>A curriculum for learning and practicing science and engineering</b><br />
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<tr><td style="text-align: center;"><a href="https://2.bp.blogspot.com/-KGVvGV8_59k/Wcr8CFxulnI/AAAAAAAADGs/EVTZDSs_etoEHbl1g19rWk5xpZCOuEjyACLcBGAs/s1600/Untitled-4.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="531" data-original-width="831" height="204" src="https://2.bp.blogspot.com/-KGVvGV8_59k/Wcr8CFxulnI/AAAAAAAADGs/EVTZDSs_etoEHbl1g19rWk5xpZCOuEjyACLcBGAs/s320/Untitled-4.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 4: Concentrated solar power plants in Energy3D</td></tr>
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For students to succeed in creating authentic models of solar energy systems valuable to research, Solarize Your World provides comprehensive curriculum materials and classroom-to-afterschool pathways (Figure 2) that lead students to: 1) design solar energy systems for their homes, schools, villages, and cities; 2) design any type of photovoltaic and concentrated solar power plants wherever applicable; and 3) communicate their designs to potential stakeholders whenever appropriate. Figures 3 and 4 show solar power systems of different types and sizes on top of satellite images of the chosen sites from Google Maps (some of these systems were modeled or designed by students in our 2017 pilot tests).<br />
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The <i>Solarize Your World </i>curriculum consists of three connected parts. Part I teaches students the needed disciplinary core ideas, crosscutting concepts, and science and engineering practices as defined in the <a href="https://www.nextgenscience.org/">Next Generation Science Standards</a>. The disciplinary core ideas cover earth science, heat transfer, geometric optics, and electric circuits that are fundamental to solar power. The crosscutting concepts include energy and systems that are necessary to understanding how the energy from the sun can be converted into electricity to power the world. This part also strives to familiarize students with the practices of scientific inquiry and engineering design. Part II provides scores of open-ended, real-world projects for students to choose. For instance, students can design solar energy systems for their own homes or schools. If students cannot finish a project within the given timeframe in the classroom or wish to undertake more projects out of school, Part III supports them to continue in an online community, possibly in collaboration with many other participants similar to the case of Minecraft.<br />
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<b>The road ahead</b><br />
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The U.S. Department of Energy <a href="http://www.powermag.com/u-s-utility-scale-pv-meets-subsidy-free-price-target-three-years-early/">announced</a> on September 12, 2017 that the 2020 utility-scale solar cost goal set by its <a href="https://energy.gov/eere/sunshot/sunshot-initiative-goals">SunShot Initiative</a> had been met three years earlier. The price of utility-scale solar energy has now fallen to six cents per kilowatt hour. Despite this phenomenal plummet, the road to a 100% renewable energy future is still unclear and debatable. We invite students and teachers worldwide to join our <i>Solarize Your World</i> initiative to pave the way. Rarely have students been given a chance to help answer a question so crucial to humanity.</div>
Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com0tag:blogger.com,1999:blog-8457990200766739016.post-28401844244193220532017-09-14T13:34:00.003-04:002017-09-19T08:15:46.734-04:00Deciphering a solar array surprise with Energy3D<div dir="ltr" style="text-align: left;" trbidi="on">
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<tr><td class="tr-caption" style="text-align: center;">Fig. 1: An Energy3D model of the SAS solar farm</td></tr>
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<tr><td style="text-align: center;"><a href="https://3.bp.blogspot.com/-OnPUIraZPNM/Wbqdw39OnNI/AAAAAAAADE0/zHAz2DX8BqAa2W2HzcaXf2gMi2b-NWrVQCLcBGAs/s1600/solarGB.gif" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="593" data-original-width="627" height="302" src="https://3.bp.blogspot.com/-OnPUIraZPNM/Wbqdw39OnNI/AAAAAAAADE0/zHAz2DX8BqAa2W2HzcaXf2gMi2b-NWrVQCLcBGAs/s320/solarGB.gif" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 2: Daily production data (Credit: Xan Gregg)</td></tr>
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SAS, a software company based in Cary, NC, is powered by a solar farm consisting of solar panel arrays driven by horizontal single-axis trackers (HSAT) with the axis fixed in the north-south direction and the panels rotating from east to west to follow the sun during the day. Figure 1 shows an <a href="http://energy3d.concord.org/">Energy3D</a> model of the solar farm. Xan Gregg, JMP Director of Research and Development at SAS, <a href="https://community.jmp.com/t5/JMP-Blog/Solar-Array-Surprises/ba-p/29764">posted some production data</a> from the solar farm that seem so counter-intuitive that he called it a "solar array surprise" (which happens to also acronym to SAS, by the way).<br />
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The data are surprising because they show that the outputs of solar panels driven by HSAT actually dip a bit at noon when the intensity of solar radiation reaches the highest of the day, as shown in Figure 2. The dip is much more pronounced in the winter than in the summer, according to Mr. Gregg (he only posted the data for April, though, which shows a mostly flat top with a small dip in the production curve).<br />
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<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://1.bp.blogspot.com/-Rbs1l4XCGoo/WbquqUqBdbI/AAAAAAAADFU/XzzQGyvK39wcYvPXKdxueQ_0aZEOz7FQACLcBGAs/s1600/Untitled-1.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="518" data-original-width="966" height="171" src="https://1.bp.blogspot.com/-Rbs1l4XCGoo/WbquqUqBdbI/AAAAAAAADFU/XzzQGyvK39wcYvPXKdxueQ_0aZEOz7FQACLcBGAs/s320/Untitled-1.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 3: Energy3D results for four seasons.</td></tr>
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Anyone can easily confirm this effect with an Energy3D simulation. Figure 3 shows the results predicted by Energy3D for 1/22, 4/22, 7/22, and 10/22, which reveal a small dip in April, significant dips in January and October, and no dip at all in July. How do we make sense of these results?<br />
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<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://2.bp.blogspot.com/-qmpgXdJsd9I/Wbq3BLgS0vI/AAAAAAAADFw/zaJHXRYXAkkWpr1wIlI5tjp2ccN6qvEvACLcBGAs/s1600/Untitled-1.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="590" data-original-width="608" height="310" src="https://2.bp.blogspot.com/-qmpgXdJsd9I/Wbq3BLgS0vI/AAAAAAAADFw/zaJHXRYXAkkWpr1wIlI5tjp2ccN6qvEvACLcBGAs/s320/Untitled-1.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 4: Change of incident sunbeam angle on 1/22 (HSAT).</td></tr>
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One of the most important factors that affect the output of solar panels, regardless of whether or not they turn to follow the sun, is the angle of incidence of sunlight (the angle between the direction of the incident solar rays and the normal vector of the solar panel surface). The smaller this angle is, the more energy the solar panel receives (if everything else is the same). If we track the change of the angle of incidence over time for a solar panel rotated by HSAT on January 22, we can see that the angle is actually the smallest in early morning and gradually increases to the maximum at noon (Figure 4). This is opposite to the behavior of the change of the angle of incidence on a horizontally-fixed solar panel, which shows that the angle is the largest in early morning and gradually decreases to the minimum at noon (Figure 5). The behavior shown in Figure 5 is exactly the reason why we feel the solar radiation is the most intense at noon.<br />
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<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://3.bp.blogspot.com/-UENucPW4c98/Wbq3PCxl0RI/AAAAAAAADF0/7LzM8gSvQYs0CWGL17ZA5bbQpfp69RFQACLcBGAs/s1600/Untitled-1.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="590" data-original-width="607" height="311" src="https://3.bp.blogspot.com/-UENucPW4c98/Wbq3PCxl0RI/AAAAAAAADF0/7LzM8gSvQYs0CWGL17ZA5bbQpfp69RFQACLcBGAs/s320/Untitled-1.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 5: Change of incident sunbeam angle on 1/22 (fixed)</td></tr>
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If the incident angle of sunlight is the smallest at 7 am in the morning of January 22, as shown in Figure 4, why is the output of the solar panels at 7 am less than that at 9 am, as shown in Figure 3? This has to do with something called <a href="https://en.wikipedia.org/wiki/Air_mass_(solar_energy)">air mass</a>, a convenient term used in solar engineering to represent the distance that sunlight has to travel through the Earth's atmosphere before it reaches a solar panel as a ratio relative to the distance when the sun is exactly vertically upwards (i.e. at the zenith). The larger the air mass is, the longer the distance sunlight has to travel and the more it is absorbed or scattered by air molecules. The air mass coefficient is approximately inversely proportional to the cosine of the <a href="http://www.pveducation.org/pvcdrom/properties-of-sunlight/elevation-angle">zenith angle</a>, meaning that it is largest when the sun just rises from the horizon and the smallest when the sun is at the zenith. Because of the effect of air mass, the energy received by a solar panel will not be the highest at dawn. The exact time of the output peak depends on how the contributions from the incidental angle and the air mass -- among other factors -- are, relatively to one another.<br />
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So we can conclude that it is largely the motion of the solar panels driven by HSAT that is responsible for this "surprise." The constraint of the north-south alignment of the solar panel arrays makes it more difficult for them to face the sun, which appears to be shining more from the south at noon in the winter.<br />
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If you want to experiment further, you can try to track the changes of the incident angle in different seasons. You should find that the change of angle from morning to noon will not change as much as the day moves to the summer.<br />
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This dip effect becomes less and less significant if we move closer and closer to the equator. You can confirm that the effect vanishes in Singapore, which has a latitude of one degree. The lesson learned from this study is that the return of investment in HSAT is better at lower latitudes than at higher latitudes. This is probably why we see solar panel arrays in the north are typically fixed and tilted to face the south.<br />
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The analysis in this article should be applicable to parabolic troughs, which follow the sun in a similar way to HSAT.</div>
Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com2tag:blogger.com,1999:blog-8457990200766739016.post-31570204422368833332017-09-07T20:06:00.000-04:002017-09-07T20:07:04.038-04:00Energy3D exports Wavefront OBJ files<div dir="ltr" style="text-align: left;" trbidi="on">
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://2.bp.blogspot.com/-4OQqlE5X7DI/WbHYmpv3QGI/AAAAAAAADD4/_WHvkvqs7pM_ieXfrEhg8MqDgF8pdTFWwCLcBGAs/s1600/Untitled-1.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="568" data-original-width="887" height="203" src="https://2.bp.blogspot.com/-4OQqlE5X7DI/WbHYmpv3QGI/AAAAAAAADD4/_WHvkvqs7pM_ieXfrEhg8MqDgF8pdTFWwCLcBGAs/s320/Untitled-1.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 1: An Energy3D model of a house</td></tr>
</tbody></table>
Starting from Version 7.2.6, users can export most parts of <a href="http://energy3d.concord.org/">Energy3D</a> models in <a href="https://en.wikipedia.org/wiki/Wavefront_.obj_file">Wavefront's OBJ format</a>, which has been adopted by many 3D graphics applications and supported by many 3D printers. This provides a possibility to 3D-print Energy3D models and import them into other software.<br />
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<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://2.bp.blogspot.com/-20s_Ng8smh4/WbHYpZu7FKI/AAAAAAAADD8/vBsrOu_1yH8HTBFVAdkccMAigdxhJU3ZQCLcBGAs/s1600/Untitled-2.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="574" data-original-width="917" height="200" src="https://2.bp.blogspot.com/-20s_Ng8smh4/WbHYpZu7FKI/AAAAAAAADD8/vBsrOu_1yH8HTBFVAdkccMAigdxhJU3ZQCLcBGAs/s320/Untitled-2.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 2: OBJ output</td></tr>
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OBJ files can also be embedded within Web pages. This mechanism will be important in developing our Virtual Solar World platform, a Google Map application that collects and displays users' Energy3D models of buildings, solar farms, power plants, and so on. The Virtual Solar World is an important part of our Energy3D ecosystem. Figure 1 shows an Energy3D model and Figure 2 shows its OBJ form. As you can see, most of the features in the original Energy3D model are preserved after the conversion.<br />
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<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://3.bp.blogspot.com/-hsaD4WQlyjI/WbHa3xlw7ZI/AAAAAAAADEQ/u0uDLn-OeWUW90eh6jUe8g9DN6Q2KveaACLcBGAs/s1600/Untitled-2.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="568" data-original-width="887" height="204" src="https://3.bp.blogspot.com/-hsaD4WQlyjI/WbHa3xlw7ZI/AAAAAAAADEQ/u0uDLn-OeWUW90eh6jUe8g9DN6Q2KveaACLcBGAs/s320/Untitled-2.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 3: An Energy3D model of a solar tower</td></tr>
</tbody></table>
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://4.bp.blogspot.com/-4j6ulz78DPw/WbHa2NwhGmI/AAAAAAAADEM/hTZjOYzFR4ozJuUpYSLdaWdo8d64owsnwCLcBGAs/s1600/Untitled-1.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="500" data-original-width="863" height="185" src="https://4.bp.blogspot.com/-4j6ulz78DPw/WbHa2NwhGmI/AAAAAAAADEM/hTZjOYzFR4ozJuUpYSLdaWdo8d64owsnwCLcBGAs/s320/Untitled-1.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 4: OBJ output</td></tr>
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Power plants designed in Energy3D can be exported in the OBJ format as well. Figure 3 shows an Energy3D model of a solar power tower and Figure 4 shows its OBJ conversion.<br />
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Caveat: At this point, not all OBJ files exported from Energy3D are 3D-printable. Even when an OBJ model looks fine on the computer, it doesn't always get printed right. We are still investigating why the exported OBJ format is not compatible with some 3D printing services.</div>
Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com0tag:blogger.com,1999:blog-8457990200766739016.post-39490530180050125682017-08-17T08:13:00.003-04:002017-08-17T08:16:43.722-04:00National Science Foundation funds citizen science project to crowdsource an infrared street view<div dir="ltr" style="text-align: left;" trbidi="on">
We are pleased to announce that the National Science Foundation has <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1712676">awarded us</a> a two-year, $500,000 exploratory grant to develop, test, and evaluate a citizen science program that engages youth to investigate energy issues through scientific inquiry with innovative technology. The project will crowd-create the <a href="http://molecularworkbench.blogspot.com/2016/10/infrared-street-view-won-department-of.html">Infrared Street View</a>, a citizen science program that aims to produce a thermal version of Google's Street View using an affordable <a href="http://www.flir.com/flirone/">infrared (IR) camera attached to a smartphone</a>. In
collaboration with high schools and out-of-school programs in
Massachusetts, we will conduct pilot-tests with approximately
200 students in this exploratory phase. The project will develop SmartIR, a smartphone app that will guide users to collect IR images on both Android and iOS platforms for synthesizing a seamless street view. Figure 1 shows a prototype of the Infrared Street View and Figure 2 shows a little math behind the scenes.<br />
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<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://2.bp.blogspot.com/-E8kyn5rUX7s/WZWBOaHaJII/AAAAAAAADDU/2M31YNYfAs475cYGnMZ2B_mz4b_e1L2gwCLcBGAs/s1600/Untitled-1.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="721" data-original-width="638" height="400" src="https://2.bp.blogspot.com/-E8kyn5rUX7s/WZWBOaHaJII/AAAAAAAADDU/2M31YNYfAs475cYGnMZ2B_mz4b_e1L2gwCLcBGAs/s400/Untitled-1.png" width="353" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 1: A hemispherical infrared street view (prototype)</td></tr>
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In essence, an IR camera serves as a high-throughput data acquisition instrument that collects thousands of temperature data points each time a picture is taken. With this incredible tool, youth can collect massive geotagged thermal data that have considerable scientific and educational value for visualizing energy usage and improving energy efficiency at all levels. The Infrared Street View program will provide a Web-based platform for youth and anyone interested in energy efficiency to view and analyze the aggregated data to identify possible energy losses. By sharing their scientific findings with stakeholders, youth will make changes to the way energy is being used. <br />
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We are completely aware of possible legal implications and complications of the proposed citizen science program. In the case of <a href="https://en.wikipedia.org/wiki/Kyllo_v._United_States">Kyllo v. United States</a> in 2001, the Supreme Court has ruled that the use of a thermal camera from a public vantage point to monitor the radiation of heat from a person's home was a “search” within the meaning of the Fourth Amendment, and thus required a warrant. The ruling seems to be limited to the use of thermal cameras by law enforcement, however. Back then, IR cameras were available to only a handful of professionals, but they are only $200 nowadays and just a few clicks away on Amazon. The widespread use of smartphone-based IR cameras is making thermal images commonplace on the Internet and it is probably an interesting question for law scholars to study how civilian use of IR cameras should be regulated.<br />
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<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://2.bp.blogspot.com/-K9YaXAudgh4/WZWGrQKwMPI/AAAAAAAADDk/445-UQYWYCY2yBkQI3W11bXxlPK16J28ACLcBGAs/s1600/smartir.jpg" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1200" data-original-width="1600" height="240" src="https://2.bp.blogspot.com/-K9YaXAudgh4/WZWGrQKwMPI/AAAAAAAADDk/445-UQYWYCY2yBkQI3W11bXxlPK16J28ACLcBGAs/s320/smartir.jpg" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Fig. 2: Math behind the scenes.</td></tr>
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Regardless, we will take the privacy issue very seriously and will take
every precaution that we can think of to avoid potential side effects
resulted from this well-intentioned program. Fortunately, we have a lot of public supports to conduct this research
on large public buildings and possible commercial buildings, where the
concerns of privacy are far less than private residential buildings and
the needs to reduce the energy waste of those buildings and save
taxpayer dollars are far more pressing. Hence, we will start with school, public, and commercial buildings in
selected areas where performing thermal scan of the buildings and
publishing their thermal images for educational and research purposes
are permitted by school leaders, town officials, and property owners. <br />
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From a broader perspective, the Infrared Street View program could serve as a pilot test that may shed light on increasingly important issues related to citizen privacy in the era of the Internet of Things (IoT), which features the ubiquity of sensor data collection that could be viewed by many as invasive into their physical space (not just cyberspace). While no one can deny the tremendous potential of the technology in transforming the ways people learn, work, and live, careful research must be carried out to address legitimate concerns. This program could be one of those projects that provide a unique approach to meet those challenges from a citizen science point of view, which integrates many interesting scientific, technical, educational, and legal aspects. The lessons we can learn from conducting this work could be very useful to the citizen science community in the IoT era.</div>
Charles Xiehttp://www.blogger.com/profile/02429194577204237568noreply@blogger.com0