Category Archives: Molecular Workbench

Importing and analyzing models created by other CAD software in Energy3D: Part 1

Fig. 1: Solarize a COLLADA model in Energy3D
Fig.2: A house imported from SketchUp's 3D Warehouse
Energy3D is a relatively simple CAD tool that specializes in building simulation and solar simulation. Its current support for architectural design is fine, but it has limitations. It is never our intent to reinvent the wheel and come up with yet another CAD tool for architecture design. Our primary interest is in physics modeling, artificial intelligence, and computational design. Many users have asked if we can import models created in other CAD software such as SketchUp and then analyze them in Energy3D.

Fig. 3: A house imported from SketchUp's 3D Warehouse
I started this work yesterday and completed the first step today. Energy3D can now import any COLLADA models (*.dae files) on top of a foundation. The first step was the inclusion of the mesh polygons in the calculation of solar radiation. The polygons should be able to cast shadow on any object existing in an Energy3D model. This means that, if you have a 3D model of a neighboring building to the target building, you can import it into Energy3D so that it can be taken into consideration when you design solar solutions for your target. Once you import a structure, you can always translate and rotate it in any way you want by dragging its foundation, like any existing class of object in Energy3D.

Fig. 4: A house at night in Energy3D
Due to some math difficulties, I haven't figured out how to generate a solar radiation heat map overlaid onto the external surfaces of an imported structure that are exposed to the sun. This is going to be a compute-intensive task, I think. But there is a shortcut -- we can add Energy3D's solar panels to the roof of an imported building (Figure 1). In this way, we only have to calculate for these solar panels and all the analytic capabilities of Energy3D apply to them. And we can get pretty good results pretty quickly.

Fig. 5: A 3D tree imported from SketchUp's 3D Warehouse
Figures 2-4 show more examples of how houses designed with SketchUp look like in Energy3D after they are imported. This interoperability makes it possible for architects to export their work to Energy3D to take advantage of its capabilities of energy performance analysis.

Being able to import any structure into Energy3D also allows us to use more accurate models for landscapes. For instance, we can use a real 3D tree model that has detailed leaves and limbs, instead of a rough approximation (Figure 5). Of course, using a more realistic 3D model of a tree that has tens of thousands of polygons slows down the graphic rendering and simulation analysis. But if you can afford to wait for the simulation to complete, Energy3D will eventually get the results for you.

Modeling the six MW solar farm at the Palmer Metropolitan Airfield in Massachusetts

Fig. 1 Aerial view of PMA (courtesy of Borrego Solar)
Fig. 2 The polygon tool for drawing land parcels
The Palmer Metropolitan Airfield (PMA) solar farm (Figure 1) is the first and, at 6 MW, the largest Massachusetts Department of Energy Resources qualified brownfield project under the SREC II solar energy incentive program. The solar farm consists of 20,997 solar panels of three different types (5,161 Suniva, 13,851 Yingli, and 1,985 Canadian Solar), connected by 74 SMA string inverters. It is expected to produce an estimate of 8.5 GWh annually, enough to power 1,000 homes and offset 4,000 tons of carbon dioxide every year -- according to this news source. The PMA solar farm was engineered by our partner, Borrego Solar, the third largest company in the commercial solar market in the US.

Fig. 3 The Automatic Layout Wizard for solar rack arrays
The PMA solar farm is the first test of Energy3D's capacity of seriously designing utility-scale (greater than 1 or 5 MW, depending on your point of view) photovoltaic solar power plants. This design capacity was enabled by three critical new features that were added only recently to Energy3D (V6.2.2): 1) A tool to draw polygons that represent parcels of land for solar farms; 2) a tool to automatically generate solar panel and rack array layouts within selected parcels of land; and 3) accelerated graphical user interface and numerical simulation to handle 10,000+ solar panels (which I have blogged earlier this month).

Fig. 4 The result of the Automatic Layout Wizard
Since Energy3D can import an Earth view image from Google Maps, you can directly draw polygons on top of the image to trace the parcel of land for designing your solar farm (Figure 2). Note that if you have multiple parcels of land that are separate from one another, you may have to use multiple foundations in Energy3D as each foundation is allowed to have one and only one polygon for the time being.

Fig. 5 Heat map representations of output in four seasons.
Fig. 6 Annual yield vs. tilt angle
As soon as you are all set with your land plans, you can use the Automatic Layout Wizard of Energy3D to add solar panel rack arrays (Figure 3). This wizard will automatically generate the array layout within the selected land parcel and assign properties to the solar panels based on the parameters of your choice. For instance, you can select how many rows of solar panels you want to have on each rack (I picked four because that is what Google Maps shows about the setting in PMA). Figure 4 shows the result of applying the Automatic Layout Wizard to populate the three subfields of the PMA solar farm.
Fig. 7 Monthly yields vs. tilt angle

After the layout is done, you can always revise the field. You can drag any rack to resize or move it, delete it, copy and paste it, or add a new rack. The Automatic Layout Wizard is not the only way to add solar panel arrays. It is just a super fast way to add thousands of solar panels at once -- without the wizard, it would have been too time-consuming to manually add solar panel racks one by one. The solar panel field is always editable after a layout is applied.

Let's now check how close our model is to reality. The total number of solar panels of our model is 21,064 -- only 67 more than that of the real PMA solar farm (I had no information about the exact types of solar panels deployed in PMA, so I guessed and selected two different sizes 0.99m x 1.65m and 0.99m x 1.96m for different subfields).

In terms of the annual output, Energy3D predicts approximately 9.6 GWh, about 12% higher than the estimated output of 8.5 GWh by Borrego Solar. I currently do not have access to the real operational data, though.

Having created a computer model allows us to experiment with it to study how to optimize the design. For example, we can easily change the tilt angles of the arrays and investigate how the annual yield is affected. Figure 6 shows that a tilt angle close to the latitude (42 degrees) seems to result in the highest overall annual output.

But the total annual output is not necessarily the only criterion. Sometimes, it is necessary for solar companies to consider load balancing to guarantee stable outputs throughout the year (assuming that we want to minimize the use of base load from burning fossil fuels). It is, therefore, interesting to also take a look at the outputs across 12 months of a year. Figure 7 suggests that a smaller tilt angle will produce peak power in the summer, whereas a larger tilt angle will produce peak power in early fall. If the demand of electricity in the summer is higher than that in the fall, it may be more lucrative to position solar panels at a lower tilt angle.

Ten research papers utilizing Energy2D published in the past two years

Screenshots from recent papers that use Energy2D
Energy2D simulation of fire
Energy2D is a multiphysics simulation program that was created from scratch and is still under development (though its progress has slowed down significantly because my priority has been given to its Energy3D cousin). The software was originally intended to be a teaching and learning tool for high school students who are interested in studying engineering. Over the past two years, however, we have seen 10 research papers published in various journals and conferences that involved significant applications of Energy2D as a scientific research tool for modeling natural phenomena and engineering systems. The problems that these researchers simulated range from solar energy, industrial processes, geophysics, and building science. The authors come from universities from all over the world, including top-notch institutions in US, Europe, and China.

Energy2D simulation of thermal bridge
Among them, researchers from Delft University of Technology, Technical University of Darmstadt, and Eindhoven University of Technology wrote in their recent paper about the validity of Energy2D: "The software program Energy2D is used to solve the dynamic Fourier heat transfer equations for the Convective Concrete case. Energy2D is a relatively new program (Xie, 2012) and is not yet widely used as a building performance simulation tool. To gain more confidence in the predictions with Energy2D, an analytical validation study was therefore carried out first, inspired by the approach described in Hensen and Nakhi (1994). Those analytical solutions and the simulation results of the dynamic response to a 20°C temperature step change on the surface of a concrete construction with the following properties were compared for this research." They concluded that "the simulation results never divert from the exact solution more than 0.45°C and it is therefore considered acceptable to further use this model."

The publication of these papers and very positive user feedback suggest that Energy2D seems to have found itself an interesting niche market. Many scientists and engineers are unable to invest a lot of time and money on its complicated commercial counterparts. But they nonetheless need a handy simulation tool that is much more flexible, intuitive, and capable than formulas in books to deal with realistic geometry -- at least in 2D. This is where Energy2D comes into play.

Reaching this milestone is critically important to the free and open-source Energy2D software, whose future will be reliant on community support. Its modest popularity among scientists is a valid demonstration of the broader impact expected by the National Science Foundation that funded its development. One can only imagine that there are many more users who used the software in their workplace but didn't publish. Now that good words about it have spread, we expect the usage to continue and even accelerate. To better support our users, we have added a community forum recently. We also plan to work with Professor Bob Hanson to port the Java code to JavaScript through his SwingJS translator so that the program can run on more devices.

The list of these papers is as follows:
  1. Mahfoud Abderrezek & Mohamed Fathi, Experimental Study of the Dust Effect on Photovoltaic Panels' Energy Yield, Solar Energy, Volume 142, pp 308-320, 2017
  2. Dennis de Witte, Marie L. de Klijn-Chevalerias, Roel C.G.M. Loonen, Jan L.M. Hensen, Ulrich Knaack, & Gregor Zimmermann, Convective Concrete: Additive Manufacturing to Facilitate Activation of Thermal Mass, Journal of Facade Design and Engineering, Volume 5, No. 1, 2017
  3. Javier G. Monroy & Javier Gonzalez-Jimenez, Gas Classification in Motion: An Experimental Analysis, Sensors and Actuators B: Chemical, Volume 240, pp 1205-1215, 2017
  4. Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, & Frank Wood, Bayesian Optimization for Probabilistic Programs, 30th Conference on Neural Information Processing Systems, Barcelona, Spain, 2016
  5. E. Rozos, I. Tsoukalas, & C. Makropoulos, Turning Black into Green: Ecosystem Services from Treated Wastewater, 13th IWA Specialized Conference on Small Water and Wastewater Systems, Athens, Greece, 2016
  6. W. Taylor Shoulders, Richard Locke, & Romain M. Gaume, Elastic Airtight Container for the Compaction of Air-Sensitive Materials, Review of Scientific Instruments, Volume 87, 063908, 2016
  7. Zachary R. Adam, Temperature Oscillations near Natural Nuclear Reactor Cores and the Potential for Prebiotic Oligomer Synthesis, Origins of Life and Evolution of Biospheres, Volume 46, Issue 2, pp 171-187, 2016
  8. Jiarui Chen, Shuyu Qin, Xinglong Wu, & Paul K Chu, Morphology and Pattern Control of Diphenylalanine Self-Assembly via Evaporative Dewetting, ACS Nano, Volume 10, No. 1, pp 832-838, 2016
  9. Atanas Vasilev, Geothermal Evolution of Gas Hydrate Deposits: Bulgarian Exclusive Economic Zone in the Black Sea, Comptes rendus de l‘Académie bulgare des Sciences, Volume 68, No. 9, pp 1135-1144, 2015
  10. Pedro A. Hernández, et al., Magma Emission Rates from Shallow Submarine Eruptions Using Airborne Thermal Imaging, Remote Sensing of Environment, Volume 154, pp 219-225, November 2014

Accelerating solar farm design in Energy3D with a new model of solar panel racks

Fig. 1: A solar farm of 5,672 solar panels on 8/16 in Boston
The solar simulation in Energy3D is based on discretizing a solar panel, a reflector, a solar water heater, a window, or any other surface into many small cells (mesh), calculating the solar radiation to the centers of the cells, and then summing the results up to obtain the total energy output. For example, a photovoltaic solar panel can be divided into 6x10 cells (this is also because many residential versions of solar panels are actually designed to have 6x10 solar cells). The simulation runs speedily when we have only a few dozen solar panels such is in the case of rooftop solar systems.

Fig. 2: Simulation of 5,672 solar panels on 8/16 in Boston
Unlike rooftop solar systems, large-scale solar farms typically involve thousands of solar panels (mega utility-scale solar farms may have hundreds of thousands of solar panels). If we use the same discretization method for each panel, the simulation would run very slowly (e.g., the speed drops to 1% when the number of solar panels are 100 times more). This slowdown basically makes Energy3D impractical to use by those who cannot afford to wait such as students in the classroom who need to get the results quickly.

Fig. 3: The result of the accelerated model
Fig. 4: The result of the original model
Luckily, solar panel arrays are often installed on parallel long racks in many solar farms (Figure 1). For such solar panel arrays, a lot of calculations could be spared without compromising the overall accuracy of the simulation too much. This allows us to develop a more efficient model of numeric simulation to do solar radiation calculation and even explore methods that use non-uniform meshes to better account for areas that are more likely to be shaded, such as the lower parts of the solar panel arrays. By implementing this new model, we have succeeded in speeding up the calculation dramatically. For example, the daily solar simulation of a solar farm consisting of more than 5,000 solar panels took about a second on my Surface Book computer (Figure 2 -- in this scene I deliberately added a couple of trees so that you can see the result of shading). With the previous model I would probably have to wait for hours to see the result and the graphics card of my computer would take a very deep breath to render more than 5,000 dynamic textures. This is a huge improvement.

Figures 3 and 4 show a comparison of the simulation results between the new and old models. Quantitatively, the total output of the new model is 93.63 kWh for the selected day of June 22 in Boston, compared with 93.25 kWh from the original model. Qualitatively, the color shading patterns that represent the distribution of solar radiation in the two cases are also similar.

The new rack model supports everything about solar panels. It has a smart user interface that allows the user to draw racks of any size and in any direction -- it automatically trims off any extra length so that you will never see a partial solar panel on a rack. When tracking systems are used with long, linear racks, there is only one way to do it -- horizontal single-axis tracker (HSAT). The new model can handle HSAT with the same degree of speed-up. For other trackers such as the vertical single-axis tracker (VSAT) or the altazimuth dual-axis trackers (AADAT), the speed-up will not be as significant, however, as the inter-rack shading is more dynamically complex and each rack must be treated independently.

Infrared Street View won Department of Energy’s JUMP competition

Creating an infrared street view using SmartIR and FLIR ONE
Our Infrared Street View (ISV) program has won the JUMP Competition sponsored jointly by CLEAResult, the largest provider of energy efficiency programs and services in North America, and the National Renewable Energy Laboratory (NREL), a research division of the US Department of Energy (DOE). This JUMP Competition called for innovations in using smartphones' sensing capabilities to improve residential energy efficiency. Finalists were selected from a pool of submitted proposals and invited to make their pitches to the audience at the CLEAResult Energy Forum held in Austin, TX on October 4-6, 2016. There is only one winner among all the good ideas for each competition. This year, we just happened to be one.

IR homework
We envision the Infrared Street View as an infrared (IR) counterpart of Google's Street View (I know, I know, this is probably too big to swallow for an organization that is a few garages small). Unlike Google's Street View in the range of visible light, the Infrared Street View will provide a gigantic database of thermal images in the range of invisible IR light emitted by molecular vibrations related to thermal energy. If you think about these images in a different way, they actually are a massive 3D web of temperature data points. What is the value of this big data? If the data are collected in the right way, they may represent the current state of the energy efficiency of our neighborhoods, towns, cities, and even states. In a sense, what we are talking about is in fact a thermographic information system (TIS).

We are not the only group that realized this possibility (but we are likely the first one that came up with the notion and name of TIS). A few startup companies in Boston area have worked in this frontier earlier this decade. But none of them has tapped into the potential of smartphone technologies. With a handful of drive-by trucks or fly-by drones with a bunch of mounted infrared cameras, it probably would take these companies a century to complete this thermal survey for the entire country. Furthermore, the trucks can only take images from the front of a building and the drones can only take images from above, which mean that their data are incomplete and cannot be used to create the thermal web that we are imagining. In some cases, unsolicited thermal scan of people's houses may even cause legal troubles as thermal signatures may accidentally disclose sensitive information.

Our solution is based on FLIR ONE, a $200-ish thermal camera that can be plugged into a smartphone (iOS or Android). The low cost of FLIR ONE, for the first time in history, makes it possible for the public to participate in this thermal survey. But even with the relatively low price tag, it is simply unrealistic to expect that a lot of people will buy the camera and scan their own houses. So where can we find a lot of users who would volunteer to participate in this effort?

Let's look elsewhere. There are four million children entering the US education system each year. Every single one of them is required to spend a sizable chunk of their education on learning thermal science concepts -- in a way that currently relies on formalism (the book shows you the text and math, you read the text and do the math). IR cameras, capable of visualizing otherwise invisible heat flow and distribution, is no doubt the best tool for teaching and learning thermal energy and heat transfer (except for those visually impaired -- my apology). I think few science teachers would disagree with that. And starting this year, educational technology vendors like Vernier and Pasco are selling IR cameras to schools.

What if we teach students thermal science in the classroom with an IR camera and then ask them to inspect their own homes with the camera as a homework assignment? At the end, we then ask them to acquire their parents' permissions and contribute their IR images to the Infrared Street View project. If millions of students do this, then we will have an ongoing crowdsourcing project that can engage and mobilize many generations of students to come.

Sensor-based artificial intelligence
We can't take students' IR images seriously, I hear you criticizing. True, students are not professionals and they make mistakes. But there is a way to teach them how to act and think like professionals, which is actually a goal of the Next Generation Science Standards that define the next two or three decades of US science education. Aside from a curriculum that teaches students how to use IR cameras (skills) and how to interpret IR images (concepts), we are also developing a powerful smartphone app called SmartIR. This app has many innovations but two of them may lead to true breakthroughs in the field of thermography.

Thermogram sphere
The first one is sensor-based intelligence. Modern smartphones have many built-in sensors, including the visible light cameras. These sensors and cameras are capable of collecting multiple types of data. The increasingly powerful libraries of computer vision only enrich this capability even more. Machine learning can infer what students are trying to do by analyzing these data. Based on the analysis results, SmartIR can then automatically guide students in real time. This kind of artificial intelligence (AI) can help students avoid common mistakes in infrared thermography and accelerate their thermal survey, especially when they are scanning buildings independently (when there is no experienced instructor around to help them). For example, the SmartIR app can check if the inspection is being done at night or during the day. If it is during the day (because the clock says so or the ambient light sensor says so), then SmartIR will suggest that students wait to do their scan until nightfall eliminates the side effect of solar heating and lowers the indoor-outdoor temperature difference to a greater degree. With an intelligent app like this, we may be able to increase the quality and reliability of the IR images that are fed to the Infrared Street View project.
Virtual infrared reality (VIR) viewed with Google Cardboard

The second one is virtual infrared reality, or VIR in short, to accomplish true, immersive thermal vision. VIR is a technology that integrates infrared thermography with virtual reality (VR). Based on the orientation and GPS sensors of the phone, SmartIR can create what we called a thermogram sphere and then knit them together to render a seamless IR view. A VIR can be uploaded to Google Maps so that the public can experience it using a VR viewer, such as Google's Cardboard Viewer. We don't know if VIR is going to do any better than 2D IR images in promoting the energy efficiency business, but it is reasonable to assume that many people would not mind seeing a cool (or hot) view like this while searching their dream houses. For the building science professionals, this may even have some implications because VIR provides a way to naturally organize the thermal images of a building to display a more holistic view of what is going on thermally.

With these innovations, we may eventually be able to realize our vision of inventing a visual 3D web of thermal data, or the thermographic information system, that will provide a massive data set for governments and companies to assess the state of residential energy efficiency on an unprecedented scale and with incredible detail.

National Science Foundation funds chemical imaging research based on infrared thermography

The National Science Foundation (NSF) has awarded Bowling Green State University (BGSU) and Concord Consortium (CC) an exploratory grant of $300 K to investigate how chemical imaging based on infrared (IR) thermography can be used in chemistry labs to support undergraduate learning and teaching.

Chemists often rely on visually striking color changes shown by pH, redox, and other indicators to detect or track chemical changes. About six years ago, I realized that IR imaging may represent a novel class of universal indicators that, instead of using  halochromic compounds, use false color heat maps to visualize any chemical process that involves the absorption, release, or distribution of thermal energy (see my original paper published in 2011). I felt that IR thermography could one day become a powerful imaging technique for studying chemistry and biology. As the technique doesn't involve the use of any chemical substance as a detector, it could be considered as a "green" indicator.

Fig. 1: IR-based differential thermal analysis of freezing point depression
Although IR cameras are not new, inexpensive lightweight models have become available only recently. The releases of two competitively priced IR cameras for smartphones in 2014 marked an epoch of personal thermal vision. In January 2014, FLIR Systems unveiled the $349 FLIR ONE, the first camera that can be attached to an iPhone. Months later, a startup company Seek Thermal released a $199 IR camera that has an even higher resolution and can be connected to most smartphones. The race was on to make better and cheaper cameras. In January 2015, FLIR announced the second-generation FLIR ONE camera, priced at $231 in Amazon. With an educational discount, the price of an IR cameras is now comparable to what a single sensor may cost (e.g., Vernier sells an IR thermometer at $179). All these new cameras can take IR images just like taking conventional photos and record IR videos just like recording conventional videos. The manufacturers also provide application programming interfaces (APIs) for developers to blend thermal vision and computer vision in a smartphone to create interesting apps.

Fig. 2: IR-based differential thermal analysis of enzyme kinetics
Not surprisingly, many educators, including ourselves, have realized the value of IR cameras for teaching topics such as thermal radiation and heat transfer that are naturally supported by IR imaging. Applications in other fields such as chemistry, however, seem less obvious and remain underexplored, even though almost every chemistry reaction or phase transition absorbs or releases heat. The NSF project will focus on showing how IR imaging can become an extraordinary tool for chemical education. The project aims to develop seven curriculum units based on the use of IR imaging to support, accelerate, and expand inquiry-based learning for a wide range of chemistry concepts. The units will employ the predict-observe-explain (POE) cycle to scaffold inquiry in laboratory activities based on IR imaging. To demonstrate the versatility and generality of this approach, the units will cover a range of topics, such as thermodynamics, heat transfer, phase change, colligative properties (Figure 1), and enzyme kinetics (Figure 2).

The research will focus on finding robust evidence of learning due to IR imaging, with the goal to identify underlying cognitive mechanisms and recommend effective strategies for using IR imaging in chemistry education. This study will be conducted for a diverse student population at BGSU, Boston College, Bradley University, Owens Community College, Parkland College, St. John Fisher College, and SUNY Geneseo.

Partial support for this work was provided by the National Science Foundation's Improving Undergraduate STEM Education (IUSE) program under Award No. 1626228. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Infrared Street View selected as a finalist in Department of Energy’s JUMP competition

JUMP is an online crowdsourcing community hosted by five national laboratories of the US Department of Energy (DOE) and some of the top private companies in the buildings sector. The goal is to broaden the pool of people from whom DOE seeks ideas and to move these ideas to the marketplace faster.

In July, the National Renewable Energy Laboratory (NREL) and CLEAResult launched a Call for Innovation to leverage crowdsourcing to solicit new ideas for saving energy in homes based on smartphone technologies. Modern smartphones are packed with a variety of sensors capable of detecting all kinds of things about their surroundings. Smartphones can determine whether people are home, or close to home, which may be useful for managing their HVAC systems and controlling lighting and appliances. Smartphones can also gather and analyze data to inform homeowners and improve residential energy efficiency.

Infrared images of houses
We responded to the call with a proposal to develop a smartphone app that can be used to create an infrared version of Google's Street View, which we call Infrared Street View. NREL notified us this week that the proposal has been selected as a finalist of the competition and invited us to pitch the idea at the CLEAResult Energy Forum in Austin, TX next month.

The app will integrate smartphone-based infrared imaging (e.g., FLIR ONE) and Google Map, along with built-in sensors of the smartphone such as the GPS sensor and the accelerometer, to create thermal views of streets at night in the winter in order to reveal possible thermal anomalies in neighborhoods and bring awareness of energy efficiency to people. These infrared images may even have business values. For example, they may provide information about the conditions of the windows of a building that may be useful to companies interested in marketing new windows.

The app will be based on the SDK of FLIR ONE and the Google Map API, backed by a program running in the cloud to collect, process, and serve data. The latest FLIR ONE model now costs $249 and works with common Android and iOS devices, making it possible for us to implement this idea. A virtual reality mode will also be added to enhance the visual effect. So this could be an exciting IR+VR+AR (augmented reality) project.

You may be wondering who would be interested in using the app to create the infrared street views. After all, the success of the project depends on the participation of a large number of people. But we are not Google and we do not have the resources to hire a lot of people to do the job. Our plan is to work with schools. We have a current project in which we work with teachers to promote infrared imaging as a novel way to teach thermal energy and heat transfer in classrooms. This is an area in science education that every school covers. Many teachers -- after seeing an infrared camera in action -- are convinced that infrared imaging is the ultimate way to teach thermal science. If this project is used as a capstone activity in thermal science, it is possible that we can reach and motivate thousands of students who would help make this crowdsourcing project a success.

Those who know earlier efforts may consider this initiative a new round to advance the idea. The main new things are: 1) our plan is based on crowdsourcing with potentially a large number of students who are equipped with smartphone-based IR cameras, not a few drive-by trucks with cameras that homeowners have no idea about; 2) the concerns of privacy and legality should be mitigated as students only scan their own houses and neighbors with permissions from their parents and neighbors and only publish their images in the Google Map app when permitted by their parents and neighbors; and, most importantly, 3) unlike the previous projects that do not put people first, our project starts with the education of children and has a better chance to convince adults.

Modeling Charlottesville High School’s solar project using Energy3D

Schools have plenty of roof space that can be turned into small power plants to provide electricity to students. Many schools have already taken actions. Some teachers even use the subject matter in their teaching. But in most cases, students are not profoundly involved in solarizing their own schools.

Fig. 1: Google Map 3D vs. Energy3D
Sure, students are not professional engineers and adults may not trust them when making serious investments in solar energy. But there is a safe way to let them try: Computer simulation allows students to model and design solar panel arrays for their schools without incurring any cost, risk, or injury.

Fig. 2: 88-panel arrays on CHS's roof.
There have been scores of software programs for professional solar designers. But they usually cost $1,000 per license or annual subscription as their market is really a small niche. In addition to this cost barrier for schools, most of these tools do not necessarily cover education standards or support student learning. Thanks to the National Science Foundation, there is now a powerful free alternative for all students and teachers -- Energy3D. A one-stop shop for solar power design and simulation, Energy3D is an extremely versatile CAD tool that can be used to design rooftop solar solutions for not only average homes but also large buildings (you probably have also seen that it can be used to design utility-scale concentrated solar power stations as well). Importantly, Energy3D provides excellent 3D graphics, rich visualizations, and powerful analytical tools that support scientific inquiry and engineering design at fundamental levels. These features make Energy3D a perfect tool for engaging students and fostering learning.

Fig. 3: Solar irradiance map (June 22)
We are collaborating with Charlottesville High School (CHS) in Virginia to plan for a pilot test of the Solarize Your School project, in which students will learn science and engineering concepts and principles through designing large-scale solar panel arrays that achieve optimal cost effectiveness. To make sure every student has the same building to solarize, I sketched up an Energy3D model of CHS as shown in Figure 1 to provide to students as the starting point. If you want to do this for your own school, you can import a Google Map image of the school using the Geo-Location Menu in Energy3D. After the map image shows up in the view, you can draw directly on top of it to get the basic shape right. While it may not be possible to get the exact heights in Google Map, you can use the elevation data provided by Google Earth to calculate the heights of the walls and roofs.

CHS currently has six arrays of solar panels installed on their roof. Five arrays have 88 panels each and one has 10. The panels are arranged in three rows, with the portrait orientation and a tilt angle of 10 degrees (Figure 2). All the panels are 240W AP-240 PK from Advanced Solar Photonics (ASP). Their solar cell efficiency is 14.82%. Their temperature coefficient of Pmax (a property that measures the decrease of solar output when the temperature rises) is -0.4%/°C. Their size is 1650 x 992 x 50 mm. Each panel has three internal bypass diodes. The arrays use REFUsol string inverters to convert electricity from DC to AC, meaning that these arrays probably have little to no tolerance to shade and should be placed away as far from any tall structure as possible. I couldn't find the efficiency of the string inverters, so I chose 90% as it seems typical. I also didn't know the dust level in the area and the cleaning schedule, so I applied 5% of dust loss throughout the year (although the dust loss tends to be higher in the spring due to pollen). Since they went into operation on March 1, 2012, these panels have generated a total of 605 megawatt hours (MWh) as of September 8, 2016, amounting to an average of annual yield at 135 MWh.

Fig. 4: Prediction vs. reality.
I added these solar panel arrays to the Energy3D model with their parameters set for simulation. Figure 3 shows a heat map visualization of solar irradiance on June 22, indicating the ranges of major shading areas. Figure 4 shows the comparison of the predicted output and the actual output in the past 12 months. As some of the arrays were in maintenance for some time in the past year, I picked the highest-performing array and multiplied its output by five to obtain a number that would fairly represent the total yield in the ideal situation. Also note that as there is currently no weather data for Charlottesville, I picked the nearby Lynchburg, which is about 68 miles southwest of Charlottesville, as the location.

The prediction of the total output by Energy3D is a bit higher than the actual output in the past year (139 MWh vs. 130 MWh). If we compare the predicted result with the four-year average, the difference is less (139 MWh vs. 135 MWh). In terms of monthly trend, it seems Energy3D underestimates the winter outputs and overestimates the summer outputs. While the result may be satisfactory for educational use, we will continue to improve the fidelity of Energy3D simulations.

Designing heliostat layouts of concentrated solar power stations with Energy3D

Fig. 1: PS20 field output heat map (June, 22)
Fig. 2: PS20 field output heat map (December, 22)
Fig. 3: Fermat spiral layout (6/22, Phoenix, AZ)
In an earlier article, I have discussed the concepts and issues (shadowing, blocking, cosine efficiency, etc.) related to the design of heliostat layouts for concentrated solar power (CSP) tower stations. I also showed that these problems can be nicely visualized in Energy3D so that people can immediately see them. Instant visual feedback in design time may be very useful to a designer (in fact, this is known as concurrent analysis in the CAD/CFD community, meaning that the tasks of structure design and function simulation run immediately after each other to shorten the wait time between ideation and analysis). Figures 1 and 2 are the heat map visualizations of PS20, a CSP station in Spain, that instantly suggest the possibility of minor blocking problems for some heliostats in the summer and winter. The heat map on each reflector is based on the reflected portion of the direct solar radiation onto a 8 x 8 grid on the reflector plane. Hence it already includes shadowing loss, blocking loss, and attenuation loss. And you didn't read the image wrong, each heliostat reflector has a whopping area of 120 square meters (12 x 10 meters), dwarfing the vehicle in the image!

This blog post features several new tools that were just added to Energy3D to support the actual design tasks.

Fig. 4: Variations of layouts
The first tool is a field layout wizard that provides basic steps for customizing three different types of layout: circular, rectangular, and spiral. It allows you to select the width and height of the heliostat reflectors as well as a variety of parameters to automatically generate a layout. Of course, you can also easily copy and paste to create linear arrays of heliostats to create rectangular layouts. But the wizard does the job faster. Rectangular layouts can be seen at the Jülich Solar Tower in Germany and the Delingha Solar Tower in China. The latter just went into operation this August.

Note that, in Energy3D, the heliostat field must be built on top of a foundation. The size of the foundation you draw sets the boundary of the heliostat field. As the field layout must be done on a foundation, the layout wizard can only be accessed through the popup menu of a foundation.

The spiral layout that Energy3D supports (Figure 3) is an interesting addition. It currently provides the Fermat spiral, which is the pattern you see from a sunflower head. It is so amazing that solar science seems to always go back to the sunflower. The solar trackers for photovoltaic arrays mimic the motion of sunflowers to follow the sun. The spiral pattern of a sunflower head may hold a key to optimal heliostat layouts (Noone, Torrilhon, and Mitsos, Solar Energy, Vol. 862, pp. 792–803, 2012). This may not be too surprising considering that the sunflower has probably evolved into that particular pattern to ensure that each seed has enough room to grow and fair access to sunlight.

Fig. 5: Superimposed heliostats on top of map images (PS20)
The layout wizard provides a baseline model that you can always modify manually to get what you want (Figure 4). All heliostats can be easily dragged, dropped, or removed.

If you want to model after an existing CSP station, you can use the Geo-Location menu of Energy3D to import a map image of the station and then superimpose 3D heliostats on top of the map image where the images of the actual heliostats are located. Figure 5 shows that an Energy3D model of the PS20 station can be perfectly created using this method. The shadows on the ground cast by the heliostats in the Energy3D model even aligns very well with those captured in the map image (I must confess that I tried to guess the right date and time from the shadow of the tower and the rest just follows).

Visualizing design issues in heliostat layouts of concentrated solar power stations with Energy3D

Fig. 1: Visualizing shadowing loss
As a one-stop-shop for solar solutions, Energy3D supports the design of concentrated solar power (CSP) stations. Although the main competitor of the CSP technology, the photovoltaic (PV) power stations, have become dominant in recent years due to the plummet of PV panel price, CSP has its own advantages and potential, especially in energy storage. According to the US Department of Energy, the levelized cost of electricity (LCOE) for CSP has dropped to 13 cents per kWh in the US in 2015, comparable to the LCOE for PV (12 cents per kWh). In general, it is always better to have options than having none. A combination of PV and CSP stations may be what is good for the world: CSP can complement PV to generate stable outputs and provide electricity at night. As a developer of solar design and simulation software, we are committed to supporting the research, development, and education of all forms of solar technologies.

Numerical simulation plays an important role on designing optimal CSP stations. Concentrated solar power towers are the first type of CSP stations covered by the modeling engine of Energy3D. This blog post shows some progress towards the goal of eventually building a reliable simulation and visualization kernel for CSP tower technology in Energy3D. The progress is related to the study of heliostat layouts (the heat transfer part is yet to be built).

Numerous studies of heliostat layouts have been reported in literature in the past three decades, resulting in a variety of proposals for minimizing the land use and/or maximizing the energy output (see a recent review: Li, Coventry, Bader, Pye, & Lipiński, Optics Express, Vol. 24, No. 14, pp. A985-A1007, 2016). The latest is an interesting biomimetic pattern suggested by Noone, Torrilhon, and Mitsos (Solar Energy, Vol. 862, pp. 792–803, 2012), which resembles the spiral patterns of a sunflower head (each floret is oriented towards the next by the golden angle of 137.5°, forming a Fermat spiral that is probably Mother Nature's trick to ensure that each seed has enough room to grow and fair access to sunlight).

Fig. 2: Visualizing blocking loss
If you haven't worked in the field of solar engineering, you may be wondering why there has been such a quest for optimal layouts of heliostats. At first glance, the problem seems trivial -- well, a tower-based CSP station is just a gigantic solar cooker, isn't it? But things are not always what they seem.

The design of the heliostat layout is in fact a very complicated mathematical problem. We have some acres of land somewhere to begin with. The sun moves in the sky and its trajectory varies from day to day. But that is OK. The heliostats can be programmed to reflect sunlight to the receiver automatically. These all sound good until we realize that the heliostats' large reflectors can cast shadow to one another if they are too close or the sun is low in the sky (Figure 1). Like the case of PV arrays, shadowing causes productivity loss (but luckily, reflectors -- unlike solar panels based on strings of connected solar cells -- do not completely lose power if only a part of it is in the shadow).
Fig. 3: Annual outputs of the heliostats in Fig. 2

Unlike the case of PV arrays, heliostats have an extra problem -- blocking. A heliostat must reflect the light to the receiver at the top of the tower and that path of light can be blocked by its neighbors. Of course, we rarely see the case of complete blocking. But if a portion of the reflector area is denied optical access to the receiver, the heliostat will lose some productivity. Energy3D can visualize this loss on each heliostat reflector. The upper image of Figure 2 shows the insolation to the reflectors whereas the lower one shows the portion of the insolation that actually reaches the receiver (you can see that the reflectors closer to the tower get more insolation). Figure 3 shows a comparison of the outputs of the heliostats over the course of a year. As you can see, the blue parts of the reflectors can never bounce light to the receiver because the heliostats in front of them block the reflection path for the lower parts of those heliostats. The way to mitigate this issue is to gradually increase the spacing between the heliostats when they are farther away from the tower.
Fig. 4: Visualizing cosine efficiency

Another problem with CSP tower technology is the so-called cosine efficiency. As we know, the insolation onto a surface is maximal when the surface directly faces the sun (this is known as the projection effect). In the northern hemisphere, however, the heliostats to the south of the tower (the south field) cannot face the sun directly as they must be positioned at an angle so that the incident sunlight can be reflected to a northern position (where the receiver is located). Figure 4 shows a visualization of the cosine effect and Figure 5 shows the comparison of the annual outputs of the heliostats. Clearly, the cosine efficiency is the lowest in the winter and the highest in the summer.
Fig. 5: Cosine efficiency is lower in the winter

Does the cosine efficiency mean that we should only deploy heliostats in the north field as is shown in Figure 6? This depends on a number of factors. Yes, the cosine efficiency does reduce the output of a heliostat in the south field in the winter (maybe early spring and late fall, too), but a heliostat far away from the tower in the north field also produces less energy. For a utility-scale CSP station that must use thousands of heliostats, the part of the south field close to the tower may not be such a bad place to put heliostats, compared with the part of the north field far away from the tower. This is more so when the site is closer to the equator. If the site is at a higher latitude to the point that it makes more sense to deploy all heliostats in the north field, dividing the site into multiple areas and constructing a tower for each area may be a desirable solution. The downside is that additional towers will increase the constructional cost.
Fig. 6: Semicircular layout in the north field

We now multiply these three problems (shadowing, blocking, and cosine effect) with thousands of heliostats, confine them within an area of a given shape, and want to spend as less money as possible while producing as much electricity as possible. That is the essence of the mathematical challenge that we are facing in CSP field design. With even more functionalities to be added in the future, Energy3D could become a powerful design tool that anyone can use to search for their own solutions.