Energy2D used as a simulation tool in astrobiology research

Fig. 1: Frasassi Caves, Italy (credit: Astrobiology)

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.

Fig. 2: Energy2D simulations (credit: Astrobiology)

Astrobiology, 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 "Transport-Induced Spatial Patterns of Sulfur Isotopes (δ³⁴S) as Biosignatures" 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 Energy2D 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.

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 the first paper, 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.

Energy3D uses intelligent agents to create adaptive feedback based on analyzing the "DNA of design"

Fig. 1: A simple case of teaching thermal insulation.

Energy3D 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.

The computational mechanism for generating feedback in Energy3D is based on intelligent agents, 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 decision tree model 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.

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 U-value, 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.

Fig. 2: An event sequence coded like a DNA sequence.

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, A represents an analysis event in the Energy3D CAD tool, U represents an event of changing the U-value of a wall, C represents an event of changing the date for the energy simulation, a questionmark (?) 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 the DNA of a design, if that helps your imagination.

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 sequence alignment or those developed in natural language processing. In this article, I am going to show how the widely-supported regular expressions (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 edit distances such as the Levenshtein distance in matching the pattern. For example, a single substitution of event may represent a very different process despite the short edit distance.

Fig. 3: A sequence that shows high usage of feedback

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 Bruce Alberts, 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 U and A characters in the event string suggests a high probability of inquiry, which can be captured using a simple regex such as (U[_\\*\\?]*A)+. Between U and A, 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 U and A 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 U and A.

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").

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.

General Motors funds engineering education based on Energy3D

Designing a parking lot solar canopy at Detroit Airport

General Motors (GM), along with other RE100 companies, has committed 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.

Modeling GM's rooftop solar arrays in Baltimore, MD

Modeling GM's solar arrays in Warren, MI

The project will use our signature Energy3D 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 machine learning algorithms to understand users' design behaviors, based on which we can develop intelligent agents to help users design better products and even unleash their creativity.

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 Virtual High School (VHS) to convert our Solarize Your World 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.

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.

High Frequency Electronics and Thermtest feature Energy2D

Credit: High Frequency Electronics

High Frequency Electronics is a magazine for engineers. In the cover article titled "Substrate Selection Can Simplify Thermal Management" in its November 2017 issue, author John Ranieri included our Energy2D 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 Thermtest, 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.

An Energy2D model of a heat source and a heat sink

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.

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."

Energy3D allows users to select brand name solar panels

Fig. 1: 20 brand name solar panels in Energy3D

Fig. 2: The daily outputs of 20 types of solar panels

Previous versions of Energy3D 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.

Fig. 3: The Micky Mouse solar farm

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.

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.

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 × 600 mm).

The 2017 Energy Innovation Forum

We are invited to present at the Energy Innovation Forum on October 18 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.

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 Infrared Street View Project and the Virtual Solar Grid Project (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.

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.

The challenge to solarize the world

More and more nations and regions 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 recent paper by Stanford professor Mark Jacobson and colleagues. Both proponents and opponents 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 influence our energy and environmental policies in the years to come.

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 his 2016 annual letter. But, apart from becoming a conscientious user of energy, how can students make meaningful contributions?

Fig. 1: Energy3D covers nearly 600 regions in 185 countries.

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.

This article introduces the Solarize Your World 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.

The complexity of modeling solar power in the real world

Fig. 2: Learn, apply, and explore

The sun is a gigantic nuclear fusion reactor in the sky that emits a massive amount of energy. Elon Musk 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.

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 "Rooftop Solar Photovoltaic Technical Potential in the United States: A Detailed Assessment" by Pieter Gagnon, Robert Margolis, Jennifer Melius, Caleb Phillips, and Ryan Elmore in 2016).

A crowdsourcing model that integrates education and research

Fig. 3: Photovoltaic solar farms in Energy3D

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.

Solarize Your World is based on our Energy3D 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.

A curriculum for learning and practicing science and engineering

Fig. 4: Concentrated solar power plants in Energy3D

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).

The Solarize Your World 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 Next Generation Science Standards. 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.

The road ahead

The U.S. Department of Energy announced on September 12, 2017 that the 2020 utility-scale solar cost goal set by its SunShot Initiative 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 Solarize Your World initiative to pave the way. Rarely have students been given a chance to help answer a question so crucial to humanity.

Deciphering a solar array surprise with Energy3D

Fig. 1: An Energy3D model of the SAS solar farm

Fig. 2: Daily production data (Credit: Xan Gregg)

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 Energy3D model of the solar farm. Xan Gregg, JMP Director of Research and Development at SAS, posted some production data 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).

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).

Fig. 3: Energy3D results for four seasons.

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?

Fig. 4: Change of incident sunbeam angle on 1/22 (HSAT).

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.

Fig. 5: Change of incident sunbeam angle on 1/22 (fixed)

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 air mass, 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 zenith angle, 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.

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.

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.

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.

The analysis in this article should be applicable to parabolic troughs, which follow the sun in a similar way to HSAT.

Energy3D exports Wavefront OBJ files

Fig. 1: An Energy3D model of a house

Starting from Version 7.2.6, users can export most parts of Energy3D models in Wavefront's OBJ format, 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.

Fig. 2: OBJ output

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.

Fig. 3: An Energy3D model of a solar tower

Fig. 4: OBJ output

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.

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.

National Science Foundation funds citizen science project to crowdsource an infrared street view

We are pleased to announce that the National Science Foundation has awarded us 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 Infrared Street View, a citizen science program that aims to produce a thermal version of Google's Street View using an affordable infrared (IR) camera attached to a smartphone. 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.

Fig. 1: A hemispherical infrared street view (prototype)

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. 

We are completely aware of possible legal implications and complications of the proposed citizen science program. In the case of Kyllo v. United States 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.

Fig. 2: Math behind the scenes.

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.  

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.

Canadian researchers use Energy3D to design renewable energy systems for mobile hospitals in Libya

Fig. 1: A H-shaped mobile hospital designed using Energy3D

Prof. Tariq Iqbal and his student Emadeddin Hussein from the Department of Electrical and Computer Engineering at the Memorial University of Newfoundland in Canada published a paper in the Journal of Clean Energy Technologies titled with "Design of Renewable Energy System for a Mobile Hospital in Libya."

The researchers recognized that the United Nations' efforts to provide field hospitals have recently decreased in areas that face a high risk in transportation, lack of power, and lack of security for field officers, such as war-torn countries like Libya and Syria. In those unfortunate parts of the world, lack of aids and health resources have a major effect on people's lives. Their paper proposes a photovoltaics (PV) hybrid system for supplying an electric load of a mobile hospital in an area where there is no grid. Such a hybrid system is believed to be a cost-effective solution to power a mobile hospital capable of providing uninterrupted power to support a doctor and two nurses.

Our Energy3D software was used in their research as a simulation tool to study the heat load and optimize the design solution. Figure 1 shows a H-shaped design from their paper (I guess the H-shape was chosen because it is the initial of the word "hospital").

Fig. 2: Energy3D supports 450 regions from 117 countries.

We highly appreciate the researchers' efforts in finding ways to help people living in remote areas and war zones in the world. We are glad to learn that our software may have helped a bit in providing humanitarian aids to those people. Inspired by their work, we will add more weather data to Energy3D to cover areas in the state of unrest (455 regions from 120 countries are currently supported in Energy3D, as shown in Figure 2). In the future, we will also develop curriculum materials and design challenges to engage students all over the world to join these humanitarian efforts through our global drive and outreach.

Polish researchers independently validated Energy3D with Building Energy Simulation Test (BESTEST)

Fig. 1: BESTEST600 test case

Fig. 2: Comparison of Energy3D results with those of other simulation tools

The Building Energy Simulation Test (BESTEST) is a test developed by the International Energy Agency for evaluating various building energy simulation tools, such as EnergyPlus, BLAST, DOE2, COMFIE, ESP-r, SERIRES, S3PAS, TASE, HOT2000, and TRNSYS. The methodology is based on a combination of empirical validation, analytical verification, and comparative analysis techniques. A method was developed to systematically test whole building energy simulation programs. Geometrically simple cases, such as cases BESTEST600 to 650, are used to test the ability of a subject program to model effects such as thermal mass, direct solar gain windows, shading devices, infiltration, internal heat gain, sunspaces, earth coupling, and setback thermostat control. The BESTEST procedure has been used by most building simulation software developers as part of their standard quality control program. More information about BESTEST can be found at the U.S. Department of Energy's website.

Prof. Dr. Robert Gajewski, Head of Division of Computing in Civil Engineering, Faculty of Civil Engineering, Warsaw University of Technology, and his student Paweł Pieniążek recently used BESTEST600-630 test case (Figure 1) to evaluate the quality of Energy3D's predictions of heating and cooling costs of buildings. By comparing Energy3D's results with those from major building energy simulation tools (Figure 2), they concluded that, "[Energy3D] proved to be an excellent tool for qualitative and quantitative analysis of buildings. Such a program can be an excellent part of a computer supported design environment which takes into account also energy considerations."

Their paper was published here.

Modeling parabolic dish Stirling engines in Energy3D

Fig. 1: A parabolic dish Stirling engine

Fig. 2: The Tooele Army Depot solar project in Utah

A parabolic dish Stirling engine is a concentrated solar power (CSP) generating system that consists of a stand-alone parabolic dish reflector focusing sunlight onto a receiver positioned at the parabolic dish's focal point. The dish tracks the sun along two axes to ensure that it always faces the sun for the maximal input (for photovoltaic solar panels, this type of tracker is typically known as dual-axis azimuth-altitude tracker, or AADAT). The working fluid in the receiver is heated to 250–700 °C and then used by a Stirling engine to generate power. A Stirling engine is a heat engine that operates by cyclic compression and expansion of air or other gas (the working fluid) at different temperatures, such that there is a net conversion of thermal energy to mechanical work. The amazing Stirling engine was invented 201 years ago(!). You can see an infrared view of a Stirling engine at work in a blog article I posted early last year.

Although parabolic dish systems have not been deployed at a large scale -- compared with its parabolic trough cousin and possibly due to the same reason that AADAT is not popular in photovoltaic solar farms because of its higher installation and maintenance costs, they nonetheless provide solar-to-electric efficiency above 30%, higher than any photovoltaic solar panel in the market as of 2017.

In Version 7.2.2 of Energy3D, I have added the modeling capabilities for designing and analyzing parabolic dish engines (Figure 1). Figure 2 shows an Energy3D model of the Tooele Army Depot project in Utah. The solar power plant consists of 429 dishes, each having an aperture area of 35 square meters and outputting 3.5 kW of power.

Fig. 3: All four types of real-world CSP projects modeled in Energy3D

With this new addition, all four types of main CSP technologies -- solar towers, linear Fresnel reflectors, parabolic troughs, and parabolic dishes, have been supported in Energy3D (Figure 3). Together with its advancing ability to model photovoltaic solar power, these new features have made Energy3D one of the most comprehensive and powerful solar design and simulation software tools in the world, delivering my promise made about a year ago to model all major solar power engineering solutions in Energy3D.

An afterthought: We can regard a power tower as a large Fresnel version of a parabolic dish and the compact linear Fresnel reflectors as a large Fresnel version of a parabolic trough. Hence, all four concentrated solar power solutions are based on parabolic reflection, but with different nonimaging optical designs that strike the balance between cost and efficiency.

Thermal imaging as a universal indicator of chemical reactions: An example of acid-base titration

Fig. 1: NaOH-HCl titration

Funded by the National Science Foundation, we are exploring the feasibility of using thermal imaging as a universal indicator of chemical reactions. The central tenet is that, as all chemical reactions absorb or release thermal energy (endothermic or exothermic), we can infer certain information from the time evolution and spatial distribution of the temperature field.

To prove the concept, we first chose titration, a common laboratory method of quantitative chemical analysis that is used to determine the unknown concentration of an identified analyte, as a beginning example. A reagent, called the titrant, is prepared as a standard solution. A known concentration and volume of titrant reacts with a solution of analyte to determine its concentration.

The experiment we did today was an acid-base titration. An acid–base titration is the determination of the concentration of an acid or base by exactly neutralizing the acid or base with a base or acid of known concentration. Such a titration is typically done with a burette that drops titrant into an Erlenmeyer flask containing the analyte. A pH indicator is used to determine whether the equivalence point has been reached. The pH indicator usually depends on the analyte and the titrant. But a differential thermal analysis based on infrared imaging may provide a universal indicator as the technique depends only on the heat of reaction and thermal energy is universal.

Fig. 2: The dish-array titration revealed by FLIR ONE

Figures 1 and 2 in this article show the results of the NaOH+HCl titration, taken using a FLIR ONE thermal camera attached to my iPhone 6. A solution of 10% NaOH was prepared as the analyte of "unknown" concentration and 1%, 3%, 5%, 7%, 10%, 12%, 15%, 18%, and 20% HCl were used as the titrant. The experiment was conducted with a 3×3 array of Petri dishes. Hence, we call this setup as dish-array titration. Preliminary results of this first experiment appeared to be encouraging, but we have to be cautious as the dissolving of HCl after the acid-base neutralization completes can also release a significant amount of heat. How to separate the thermal signatures of reaction and dissolving requires some further thinking.

Analyzing the linear Fresnel reflectors of the Sundt solar power plant in Tuscon

Fig. 1: The Sundt solar power plant in Tuscon, AZ

Fig. 2: Visualization of incident and reflecting light beams

Tucson Electric Power (TEP) and AREVA Solar constructed a 5 MW compact linear Fresnel reflector (CLFR) solar steam generator at TEP’s H. Wilson Sundt Generating Station -- not far from the famous Pima Air and Space Museum. The land-efficient, cost-effective CLFR technology uses rows of flat mirrors to reflect sunlight onto a linear absorber tube, in which water flows through, mounted above the mirror field. The concentrated sunlight boils the water in the tube, generating high-pressure, superheated steam for the Sundt Generating Station. The Sundt CLFR array is relatively small, so I chose it as an example to demonstrate how Energy3D can be used to design, simulate, and analyze this type of solar power plant. This article will show you how various analytic tools built in Energy3D can be used to understand a design principle and evaluate a design choice.

Fig. 3: Snapshots

One of the "strange" things that I noticed from the Google Maps of the power station (the right image in Figure 1) is that the absorber tube stretches out a bit at the northern edge of the reflector assemblies, whereas it doesn't at the southern edge. The reason that the absorber tube was designed in such a way becomes evident when we turn on the light beam visualization in Energy3D (Figure 2). As the sun rays tend to come from the south in the northern hemisphere, the focal point on the absorber tube shifts towards the north. During most days of the year, the shift decreases when the sun rises from the east to the zenith position at noon and increases when the sun lowers as it sets to the west. This shift would have resulted in what I call the edge losses if the absorber tube had not extended to the north to allow for the capture of some of the light energy bounced off the reflectors near the northern edge. This biased shift becomes less necessary for sites closer to the equator.

Energy3D has a way to "run the sun" for the selected day, creating a nice animation that shows exactly how the reflectors turn to bend the sun rays to the absorber pipe above them. Figure 3 shows five snapshots of the reflector array at 6am, 9am, 12pm, 3pm, and 6pm, respectively, on June 22 (the longest day of the year).

As we run the radiation simulation, the shadowing and blocking losses of the reflectors can be vividly visualized with the heat map (Figure 4). Unlike the heat maps for photovoltaic solar panels that show all the solar energy that hits them, the heat maps for reflectors show only the reflected portion (you can choose to show all the incident energy as well, but that is not the default).

There are several design parameters you can explore with Energy3D, such as the inter-row spacing between adjacent rows of reflectors. One of the key questions for CLFR design is: At what height should the absorber tube be installed? We can imagine that a taller absorber is more favorable as it reduces shadowing and blocking losses. The problem, however, is that, the taller the absorber is, the more it costs to build and maintain. It is probably also not very safe if it stands too tall without sufficient reinforcements. So let's do a simulation to get in the ballpark. Figure 5 shows the relationship between the daily output and the absorber height. As you can see, at six meters tall, the performance of the CLFR array is severely limited. As the absorber is elevated, the output increases but the relative gain decreases. Based on the graph, I would probably choose a value around 24 meters if I were the designer.

Fig. 4: Heat map visualization

An interesting pattern to notice from Figure 5 is a plateau (even a slight dip) around noon in the case of 6, 12, and 18 meters, as opposed to the cases of 24 and 30 meters in which the output clearly peaks at noon. The disappearance of the plateau or dip in the middle of the output curve indicates that the output of the array is probably approaching the limit.

Fig. 5: Daily output vs. absorber height

If the height of the absorber is constrained, another way to boost the output is to increase the inter-row distance gradually as the row moves away from the absorber position. But this will require more land. Engineers are always confronted with this kind of trade-offs. Exactly which solution is the optimal depends on comprehensive analysis of the specific case. This level of analysis used to be a professional's job, but with Energy3D, anyone can do it now.

Modeling linear Fresnel reflectors in Energy3D

Fig. 1: Fresnel reflectors in Energy3D.

Fig. 2: An array of linear Fresnel reflectors

Linear Fresnel reflectors use long assemblies of flat mirrors to focus sunlight onto fixed absorber pipes located above them, thus capable of concentrating sunlight to as high as 30 times of its original intensity (Figures 1 and 2). This concentrated light energy is then converted into thermal energy to heat a fluid in the pipe to a very high temperature. The hot fluid gives off the heat through a heat exchanger to power a steam generator, like in other concentrated solar power plants such as parabolic troughs and power towers.

Fig. 3: Heap map view of reflector gains

Compared with parabolic troughs and power towers, linear Fresnel reflectors may be less efficient in generating electricity, but they may be cheaper to build. According to Wikipedia and the National Renewable Energy Laboratory, Fresnel reflectors are the third most used solar thermal technology after parabolic troughs and power towers, with about 15 plants in operation or under construction around the world. To move one small step closer to our goal of providing everyone a one-stop-shop solar modeling software program for solarizing the world, I have added the design, simulation, and analysis capabilities of this type of concentrated solar power technology in Version 7.1.8 of Energy3D.

Fig. 4: Compact linear Fresnel reflectors.

Fig. 5: Heat map view of linear Fresnel reflectors for two absorber pipes.

Like parabolic troughs, Fresnel reflectors are usually aligned in the north-south axis and rotate about the axis during the day for maximal efficiency (interestingly enough, however, some of the current Fresnel plants I found on Google Maps do not stick to this rule -- I couldn't help wondering the rationale behind their design choices). Unlike parabolic troughs, however, the reflectors hardly face the sun directly, as they have to bounce sunlight to the absorber pipe. The reflectors to the east of the absorber start the day with a nearly horizontal orientation and then gradually turn to face west. Conversely, those to the west of the absorber start the day with an angle that faces east and then gradually turn towards the horizontal direction. Due to the cosine efficiency similar to the optics related to heliostats for power towers, the reflectors to the east collect less energy in the morning than in the afternoon and those to the west collect more energy in the morning and less in the afternoon.

Like heliostats for power towers, Fresnel reflectors have both shadowing and blocking losses (Figure 3). Shadowing losses occur when a part of a reflector is shadowed by another. Blocking losses occur when a part of a reflector that receives sunlight cannot reflect the light to the absorber due to the obstruction of another reflector. In addition, Fresnel reflectors suffer from edge losses -- the focal line segments of certain portions near the edges may fall out of the absorber tube and their energy be lost, especially when the sun is low in the sky. In the current version of Energy3D, edge losses have not been calculated (they are relatively small compared with shadowing and blocking losses).

Linear Fresnel reflectors can focus light on multiple absorbers. Figure 4 shows a configuration of a compact linear Fresnel reflector with two absorber pipes, positioned to the east and west of the reflector arrays, respectively. With two absorber pipes, the reflectors may be overall closer to the absorbers, but the downside is increased blocking losses for each reflector (Figure 5).