Category Archives: Molecular Workbench

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.

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."

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.

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 Tucson

Fig. 1: The Sundt solar power plant in Tucson, 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).

Simulation-based analysis of parabolic trough solar power plants around the world

Fig. 1: 3D heat map of the Keahole Plant in Hawaii
Fig. 2: SEGS-8 in California and NOOR-1 in Morocco
In Version 7.1.7 of Energy3D, I have added the basic functionality needed to perform simulation-based analysis of solar power plants using parabolic trough arrays. These tools include 24-hour yield analysis for any selected day, 12-month annual yield analysis, and the 3D heat map visualization of the solar field for daily shading analysis (Figure 1). The heat map representation makes it easy to examine where and how the design can be optimized at a fine-grained level. For instance, the heat map in Figure 1 illustrates some degree of inter-row shadowing in the densely-packed Keahole Solar Power Plant in Hawaii (also known as Holaniku). If you are curious, you can also add a tree in the middle of the array to check out its effect (most solar power plants are in open space with no external obstruction to sunlight, so this is just for pure experimental fun).
Fig. 3: Hourly outputs near Tuscon in four seasons

Fig. 4: Hourly outputs near Calgary in four seasons
As of July 12, I have constructed the Energy3D models for nine such solar power plants in Canada, India, Italy, Morocco, and the United States (Arizona, California, Florida, Hawaii, and Nevada) using the newly-built user interface for creating and editing large-scale parabolic trough arrays (Figure 2). This interface aims to support anyone, be she a high school student or a professional engineer or a layperson interested in solar energy, to design this kind of solar power plant very quickly. The nine examples should sufficiently demonstrate Energy3D's capability of and relevance in designing realistic solar power plants of this type. More plants will be added in the future as we make progress in our Solarize Your World Initiative that aims to engage everyone to explore, model, and design renewable energy solutions for a sustainable world.
Fig. 5: Hourly outputs near Honolulu in four seasons

An interesting result is that the output of parabolic troughs actually dips a bit at noon in some months of the year (Figure 3), especially at high altitudes and in the winter, such as Medicine Hat in Canada at a latitude of about 51 degrees (Figure 4). This is surprising as we perceive noon as the warmest time of the day. But this effect has been observed in a real solar farm in Cary, North Carolina that uses horizontal single-axis trackers (HSATs) to turn photovoltaic solar panels. Although I don't currently have operation data from solar farms using parabolic troughs, HSAT-driven photovoltaic solar arrays that align in the north-south axis work in a way similar to parabolic troughs. So it is reasonable to expect that the outputs from parabolic troughs should exhibit similar patterns. This also seems to agree with the graphs in Figure 6 of a research paper by Italian scientists that compares parabolic troughs and Fresnel reflectors.

The effect is so counter-intuitive that folks call it "Solar Array Surprises." It occurs only in solar farms driven by HSATs (fixed arrays do not show this effect). As both the sun and the solar collectors move in HSAT solar arrays, exactly how this happens may not be easy to imagine at once. Some people suggested that the temperature effect on solar cell efficiency might be a possible cause. Although it is true that the decrease of solar cell efficiency at noon when temperature rises to unfavorable levels in the summer of North Carolina can contribute to the dip, the theory cannot explain why the effect is also pronounced in other seasons. But Energy3D accurately predicts these surprises, as I have written in an article about a year before when I added supports for solar trackers to Energy3D. I will think about this more carefully and provide the explanation later in an article dedicated to this particular topic. For now, I would like to point out that Energy3D shows that the effect diminishes for sites closer to the equator (Figure 5).

Energy3D turns the globe into a powerful engineering design lab for everyone

Fig. 1: Dots represent regions supported in Energy3D.
Many of the readers of my blog may not know Energy3D is, in fact, also a Google Maps application. Energy3D allows users to import a satellite image of a site through the Google Maps API as the "ground image" in its 3D coordinate system, on top of which users can draw 3D structures such as buildings or power plants. Built-in simulation engines can then be used to test and analyze these structures without having to switch to another tool and leave the scene (something known as "concurrent analysis" in the CAD industry). These engines use large geographical and weather datasets for the site as inputs for simulations to accurately take environmental factors such as air temperature and solar radiation into account. As the climate is probably the single most important factor that drives the energy usage in buildings where we live and work, it is important to use weather data from a typical meteorological year (TMY) in a simulation. If no weather data is available for the site, Energy3D will automatically select the nearest location from a network of more than 525 supported worldwide regions (Figure 1) when you import the satellite image from Google Maps. The following table lists the numbers of regions in 176 countries that are currently supported in Energy3D. The United States is covered by a network of 164 nodes. So if you are in the United States, you will have a better chance to find a location that may represent the climate of your area.

Afghanistan 1 Albania 1 Algeria 6
Angola 1 Argentina 3 Armenia 1
Australia 11 Austria 1 Azerbaijan 1
Bahamas 1 Bahrain 1 Bangladesh 1
Belarus 1 Belgium 1 Belize 1
Bolivia 1 Bosnia & Herzegovina 1 Botswana 1
Brazil 8 Brunei 1 Bulgaria 1
Burkina Faso 1 Burundi 1 Cambodia 1
Cameroon 1 Canada 10 Cape Verde 1
Central African Republic 1 Chad 1 Chile 12
China 42 Colombia 2 Comoros 1
Congo 1 Costa Rica 1 Croatia 1
Cuba 1 Cyprus 2 Czech 1
DR Congo 1 Denmark 1 Djibouti 1
Dominica 1 Dominican Republic 1 East Timor 1
Ecuador 1 Egypt 1 El Salvador 1
Equatorial Guinea 1 Eritrea 1 Estonia 1
Ethiopia 1 Fiji 2 Finland 1
France 8 Gabon 1 Gambia 1
Georgia 1 Germany 12 Ghana 1
Greece 2 Guatemala 1 Guinea 1
Guinea-Bissau 1 Guyana 1 Haiti 1
Honduras 1 Hungary 1 Iceland 1
India 11 Indonesia 4 Iran 3
Iraq 1 Ireland 1 Israel 1
Italy 5 Ivory Coast 1 Jamaica 1
Japan 6 Jerusalem 1 Jordan 1
Kazakhstan 1 Kenya 1 Kosovo 1
Kuwait 1 Kyrgyzstan 1 Laos 1
Latvia 1 Lebanon 1 Lesotho 1
Liberia 1 Libya 2 Liechtenstein 1
Lithuania 1 Luxembourg 1 Macedonia 1
Madagascar 1 Malawi 1 Malaysia 2
Maldives 1 Mali 1 Malta 1
Marshall Islands 1 Mauritania 1 Mauritius 1
Mexico 4 Moldova 1 Monaco 1
Mongolia 1 Montenegro 1 Morocco 3
Mozambique 1 Myanmar 2 Namibia 1
Nepal 1 Netherlands 1 New Zealand 2
Nicaragua 1 Niger 1 Nigeria 1
North Korea 1 Norway 1 Oman 1
Pakistan 4 Panama 1 Papua New Guinea 1
Paraguay 1 Peru 2 Philippines 1
Poland 7 Portugal 2 Qatar 1
Republic of China 2 Romania 1 Russia 7
Rwanda 1 Saudi Arabia 2 Senegal 1
Serbia 2 Sierra Leone 1 Singapore 1
Slovakia 1 Slovenia 1 Solomon Islands 1
Somalia 1 South Africa 8 South Korea 2
South Pole 1 South Sudan 1 Spain 8
Sri Lanka 1 Sudan 1 Sweden 1
Switzerland 3 Syria 2 Tajikistan 1
Tanzania 2 Thailand 2 Togo 1
Trinidad & Tobago 1 Tunisia 1 Turkey 3
Turkmenistan 1 Uganda 1 Ukraine 2
United Arab Emirates 2 United Kingdom 6 United States 164
Uruguay 1 Uzbekistan 1 Venezuela 1
Vietnam 2 Western Sahara 1 Yemen 1
Zambia 1 Zimbabwe 1

Fig. 2: Solar sites in Fitchburg, MA.
Energy3D's capability of turning Google Maps into a gigantic virtual engineering design lab has tremendous potential in STEM education and energy revolution. It allows students to pick and choose sites for designing renewable energy and energy efficiency solutions that are most relevant to their lives, such as their home and school buildings (Figure 2). It gives students an authentic tool that supports them to scientifically investigate all sorts of possibilities to design a more sustainable world and effectively communicate their ideas to the public. And, most importantly, with Energy3D being a free tool that anyone can use at zero cost, this can happen at the global scale to engage every student in the world to act now and make a difference!

This global vision is not new. Back in 1995, the National Science Foundation funded my colleagues Boris Berenfeld, Bob Tinker, and Dan Barstow, who were at TERC at that time, a grant to develop a curriculum that they touted as the Globe Lab. The Global Lab Curriculum meant to provide an interdisciplinary, one-year course at the secondary level that supports science standards and school reform through intercultural, scientifically meaningful, and collaborative student investigations in environmental studies. Students were given the opportunity to experience all aspects of genuine scientific research: problem identification, background study, project design, collaboration, data analysis, and communication.

Fig. 3: Solar power plants around the world.
More than 20 years later, technology has advanced so much that we now have many more resources and tools to rethink about this idea. With Google Maps and weather data for countless regions in the world, Energy3D is poised to become a true example of Globe Lab for science and engineering. The integration of the software and our Solarize Your World Curriculum with the current, unstoppable waves of renewable energy innovation and movement worldwide will create numerous exciting possibilities for youth to become truly involved and engaged in shaping their world and their future (Figure 3). While we undertake this grand challenge, it is utterly important to keep in mind that renewable energy does not just stand for some kind of green ideology related only to potential tax hikes -- it also represents trillions of dollars worth of business opportunities and investment in the coming decades committed by almost all governments on the planet to revamp the world's energy infrastructure to provide cleaner air and healthier environment for their citizens. Given this level of global significance, our work will only become more essential and the implications will only become more profound.

As we are mourning the loss of Bob Tinker, one of the architects of the Global Lab Curriculum, carrying on this line of work will be the best way to remember his visions, honor his contributions, and celebrate his life.