Video tutorial: Solarize an imported building in Energy3D

We are pleased to announce that the solar panel and analysis tools in Energy3D (version 6.5.6 or higher) are now fully applicable to arbitrary imported structures. We hope that the new capabilities can help engineers who design rooftop solar systems and building solar facades to get their jobs done more efficiently and students who are interested in engineering to learn the theory and practice in an inquiry-based fashion. The six-minute video in this article demonstrates how easy it is to perform solar panel design and analysis in Energy3D. (Note: Unfortunately, the annotations in the video do not show if you are watching this on a smartphone.)



One of the handiest features is the automatic, real-time detection of the angle of the surface under a solar panel while the user is moving it. This feature basically allows the user to drag and drop a solar panel or a solar panel rack anywhere (on top of roofs, walls, or other surfaces) without having to set its tilt angle manually.

Solar heat map of a house with solar panels
Copy and paste a house with solar panels
Solar panels are "first-class citizens" in Energy3D as they are readily recognized by the built-in simulation engines. Energy3D provides a comprehensive list of properties that you can choose for each solar panel or solar panel array. For example, even the temperature coefficient of Pmax, a parameter that specifies the change of solar cell efficiency with regard to ambient temperature change, is supported. The software also has a variety of analytic tools for predicting the hourly, daily, and annual outputs of each solar panel and their sums. Interactive graphs are available to intuitively show the trends and allow the user to compare the outputs of different solar panels, of different arrays, on different days, or with different environmental settings (e.g., with or without a tree nearby).

These "native" solar panels are now completely blended into the "alien" meshes of structures imported into Energy3D from other CAD software or Google Earth. For example, once you drop a solar panel on a surface of the structure, it will stick to it. In other words, if you move or rotate the structure, the solar panel will go with it as if it were part of the original design. When you copy and paste the entire building, the solar panels will be copied and pasted as well (by the way, it takes only four clicks to copy and paste a building in Energy3D through the pop-up menu: one click to pick which one to act upon, one click to select the "Copy" action, one click to pick where to paste, and one last click to select the "Paste" action). That is to say, the native and alien meshes are completely meshed.

Automatic remeshing in Energy3D for solar analysis

A headache in the practice of simulation-based engineering or computer-aided engineering is the incompatibility of the meshes used to create and render structures (let's call them the drawing meshes) and the meshes needed to simulate and analyze certain functions (let's call them the analysis meshes). This incompatibility stems deeply from the fundamental differences in computer graphics for visual rendering and computer simulation for physics modeling.

When importing a model from a CAD tool into a simulation tool, engineering analysts often have to recreate new analysis meshes for their computation, which requires fine-grained discretization such is in the case of finite element analysis and other methods. It becomes a nightmare when the conversion from the drawing meshes to the analysis meshes can only be done manually. Even if only a few drawing meshes need to be taken care by hand while the majority of the meshes can be converted automatically, the analyst still would have to check all the meshes to make sure that every mesh is good for simulation. So we really need a tool that can deal with all sorts of scenarios. And this kind of tool is by no mean easy to develop.


Mesh incompatibility was (and may remain for a long time) a problem for Energy3D when it imports structure models created by other tools such as SketchUp. For example, in most architectural CAD models, a structural element such as a wall or a roof (or a part of them) is represented by two planar meshes that have exactly opposite normal vectors, representing the interior and exterior surfaces, respectively. These two meshes have identical coordinates for their vertices, though the orders of their definition are different (one goes clockwise and the other anticlockwise in order for their normal vectors to be exactly opposite). Whatever they represent has therefore no thickness, but with the two faces, we can apply two different textures to them so that the viewer can tell if they are looking at its interior or exterior surface.


While this sounds good to people who use such models in 3D games, it spells troubles to Energy3D. The first problem is that it increases the number of vertices that Energy3D has to load and, therefore, the memory footprint of such models. In a physics simulation, a structural element without thickness is meaningless. If we don't really need the two faces in most cases (we do in some other cases, but I will skip that line of discussion in the article), why bother to import them in the first place? Despite spending days on researching on the Internet and checking the Collada specification, I couldn't figure out how to force SketchUp to export only the exterior meshes (SketchUp's Colloda export function does provide the option of exporting two-sided faces or not, but it sometime exports only the interior sides for some meshes, mixed with exterior sides for others). So it occurred to me that I had to deal with them in the Energy3D code.

If we don't treat them and use the meshes as they are, we then have the second problem: which mesh should we pick to be the side that receives solar radiation? While it is self-evident which mesh of the two identical ones faces outside when we look at the 3D model after it is rendered on the computer screen, we absolutely have no way to tell from their coordinates alone in our code. Somehow, we have to invent an algorithm that simulates how people discern it when they look at the 3D view of the model on a computer screen.

Picking and choosing the right meshes, of course, is crucial to the correctness of the simulation results. In order to test the new algorithms in Energy3D, I selected the lower Manhattan island and the US Capitol Building as two test cases. The results of the lower Manhattan island have been reported in an earlier article. But the Capitol Building has turned out to be a harder case as -- with over 15,000 meshes -- it is geometrically more complicated and it has so many details that really put our code to test. After more than a week of my work on solving a myriad of problems, it seems that Energy3D has passed the test (or has it?). The screenshots of the solar irradiance heat maps of the Capitol Building from different angles show that severe anomalies are practically non-existent across the heat maps.


The heat maps occasionally suffer from a flickering effect called "Z-fighting" when two planar surfaces are visually close, especially when being looked at from a distance sufficiently far away. For the Z-fighting between identical meshes, this can be mitigated by increasing the offset of their distance. But, as this doesn't affect the simulation result, it is less a concern for the time being.

Automatic remeshing for solar analysis may also need to include automatic repair of models that contain errors. For example, some models may contain surfaces that have only one mesh with the normal vector pointing inward (this could happen if the original designer accidentally used the "Reverse Faces" feature in SketchUp without realizing that the normal vectors would be flipped). This kind of mesh is tolerated in SketchUp because it does not affect rendering. But they cannot be tolerated in Energy3D because such a single-face mesh with a north-pointing normal vector, which appears to the viewer as south-facing, will show little to zero solar radiation when the sun shines on it. Luckily, due to the visualization in Energy3D, we can quickly spot those incorrect surfaces and fix them by reversing their faces manually. But we will need to find a way to automate this process (not easy, but not impossible).

Data Science Education Technology Conference ready to welcome 100 thought leaders

We are proud to announce the Data Science Education Technology (DSET) Conference to be held February 15-17, 2017, at the David Brower Center in Berkeley, CA. Over 100 thought leaders from a range of organizations, including UC Berkeley, TERC, EDC, Desmos, SRI, Exploratorium, New York Hall of Science, Harvard’s Institute for Applied Computational Science, Lawrence Hall of Science, and Tuva will be in attendance at this groundbreaking event in the emerging field of data science education.

Conference attendees come from 15 states and 6 countries around the world. Map created with CODAP. View the data table and more information about attendees in this CODAP document.

“Data and analytics hold promise for revolutionizing all aspects of learning,” Chad Dorsey, president and CEO of the Concord Consortium, explains. “As we enter a world where practically every decision and moment of the day will connect to data in some way, preparing learners to explore, understand, and communicate with data must become a key national priority.”

The DSET conference addresses these new opportunities by focusing on two strands. The Teaching and Learning strand is designed for those thinking about curriculum development. This strand addresses the pedagogical challenges and opportunities associated with making use of data technologies in educational settings. Sessions will include data-driven learning experiences and discussion of lessons learned by curriculum designers. “It’s an exciting time to design activities to help students be ready for data science in the future,” notes Tim Erickson of Epistemological Engineering.

The Technology strand is designed especially for those with programming experience and focuses on software development. Attendees will build relevant, timely skills and learn to create a web app and increase efficiency. William Finzer, developer of the Common Online Data Analysis Platform (CODAP) at the Concord Consortium, will lead a session on data technology integration with online curricula and moderate a discussion on leveraging open-source software to enhance learners’ experience working with data.

You’re invited to attend the conference as a virtual participant. Registration is free! Join the virtual conference!

Register for the Virtual Conference

Where else can I connect with #dsetonline?

Virtual attendees are encouraged to join the conference backchannel social media conversation that will run concurrently with the face-to-face conference.

Evo-Ed Integrative Cases will be enriched to address NGSS

A new collaborative research project at the Concord Consortium and Michigan State University will develop and research learning materials on the molecular and cellular basis for genetics and the process of evolution by natural selection. These two areas are both difficult to teach and learn, and although they have been historically taught separately, they are interlinked and span multiple levels of organization. The goal of Connected Biology: Three-dimensional learning from molecules to populations is to design, develop, and examine the learning outcomes of a new connected curriculum unit for biology that embodies the conceptual framework of the Next Generation Science Standards.

Peter White, science education researcher and entomologist at Michigan State University (MSU); Louise Mead, Education Director at the BEACON Center for the Study of Evolution in Action at MSU; and postdoctoral fellow Alexa Warwick at the BEACON Center visited the Concord Consortium recently to plan our joint work together. Frieda Reichsman and Paul Horwitz will serve as the Principal Investigator and Co-PI at the Concord Consortium.

The new units will leverage the contextually rich Evo-Ed Integrative Cases, which build directly on the interlinked nature of evolution and genetics and connect the science ideas with meaningful real-world examples. The Evo-Ed case studies track the evolution of traits from their origination in DNA mutation, to the production of different proteins, to the fixation of alternate macroscopic phenotypes in reproductively isolated populations. For example, the Evolution of Lactase Persistence case study examines the genetics, cell biology, anthropology, and biogeography of this system.

The human lactase gene (LCT) is a 55 kilo-base pair segment of the second chromosome.

The curriculum will integrate the three dimensions of science—the core ideas of biology, the science and engineering practices, and the crosscutting concepts—to support all students in building toward deep understandings of biological phenomena. The project will be guided by two main research questions:

  1. How does learning progress when students experience a set of coherent biology learning materials that employ the principles of three-dimensional learning?
  2. How do students’ abilities to transfer understanding about the relationships between molecules, cells, organisms, and evolution change over time and from one biological phenomenon to another?

Note: If you’ve used the Evo-Ed cases in your classroom, we’d love to get your feedback! Please respond to this short survey to be entered into a drawing to receive a $50 Amazon gift card.

Teaching about water quality and the importance of fresh water

A new resolution may overturn the Interior Department’s “Stream Protection Rule,” which required coal mining companies to monitor and test the quality of local streams and rivers before, during, or after mining operations. There is no better time than the present to learn about the importance of water issues in our communities and environment. Three Concord Consortium projects focus on teaching middle and high school students about their local watersheds, careers in environmental conservation, and freshwater availability, and all of them offer free, high-quality resources ideal for classrooms or informal education settings.

The Teaching Environmental Sustainability: Model My Watershed project, a collaborative research project at the Concord Consortium, Millersville University, and the Stroud Water Research Center, has developed curricula for environmental/geoscience disciplines for high school classrooms, using the Model My Watershed (MMW) web-based application. The curricula also integrate low-cost environmental sensors, allowing students to collect and upload their own data and compare them to data visualized on the new MMW.

In Supporting Collaborative Inquiry, Engineering, and Career Exploration with Water (Water SCIENCE), middle school students from southern Arizona, central valley California, southeastern Pennsylvania, and eastern Massachusetts complete hands-on science and engineering activities, receive guidance and instruction from undergraduate and graduate student mentors, interact online with STEM professionals, and learn about careers in environmental conservation and engineering while investigating their community’s local water resources.

Melinda Daniels, Associate Research Scientist at the Stroud Water Research Center, describes her work. Watch additional videos about water scientists and environmental conservationists »

And in our High-Adventure Science project, we’ve developed a unit entitled “Will there be enough fresh water?” Students explore the distribution and uses of fresh water on Earth. They run experiments with computational models to explore the flow of groundwater, investigate the relationship of groundwater levels to rainfall and human impact, and hear from a hydrologist working on the same question. Students think about how to assess the sustainability of water usage locally and globally while considering their own water usage. Use these great resources today to help students understand critical water issues!

Aquifers

Students use computational models to explore water extraction from aquifers in urban and rural areas.

Solar analysis of metropolitan areas using Energy3D

Fig. 1: Sunshine at the lower Manhattan island
Energy3D can be used to analyze the solar radiation on houses, buildings, and solar power plants to help engineers design strategies for exploiting useful solar energy or mitigating excessive solar heating. This blog post shows that Energy3D may also be used to analyze the solar radiation in large urban areas (e.g., to study the effect of urban heat islands).

Fig. 2: Solar irradiance heat map of Manhattan on 4/25
To demonstrate this application, I chose a 3D model of a section of the lower Manhattan island as a test. The 3D model was downloaded from SketchUp's 3D Warehouse. It was supposedly created after the lower Manhattan island in 2008. I didn't bother to check for accuracy as this was supposed to be a test of Energy3D. Figure 1 shows the model of the Manhattan area.

Fig. 3: More solar irradiance heat maps.
The model has more than 8,000 meshes of various sizes (a mesh is a polygon area for computational analysis and graphical rendering in Energy3D). The entire area is so big that even a low-resolution daily simulation took more than five hours to complete on my Surface Book computer. Figures 2 and 3 show the rendering of the solar irradiance heat map on top of the 3D model after the computation completes.

Our next step is to figure out how to optimize our simulation engine to speed up the calculations. The latest version of Energy3D already includes some optimizations that allow faster re-rendering of the solar irradiance heat map by re-generating the texture images without re-calculating the solar irradiance distribution.

Importing and analyzing models created by other CAD software in Energy3D: Part 2


Fig.1: The Gherkin (London, UK)
In Part I, I showed that Energy3D can import COLLADA models and perform some analyses. This part shows that Energy3D (Version 6.3.5 or higher) can conduct full-scale solar radiation analysis for imported models. This capability officially makes Energy3D a useful daylight and solar simulation tool for sustainable building design and analysis. Its ability to empower anyone to analyze virtually any 3D structure with an intuitive, easy-to-use interface and speedy simulation engines opens many opportunities to engage high school and college students (or even middle school students) in learning science and engineering through solving authentic, interesting real-world problems.
Fig. 2: Beverly Hills Tower (Qatar)

There is an ocean of 3D models of buildings, bridges, and other structures on the Internet (notably from SketchUp's 3D Warehouse, which provides thousands of free 3D models that can be exported to the COLLADA format). These models can be imported into Energy3D for analyses, which greatly enhances Energy3D's applicability in engineering education and practice.

Fig. 3: Solar analysis of various houses
The images in this post show examples of different types of buildings, including 30 St Mary Axe (the Gherkin) in London, UK (Figure 1) and the Beverly Hills Tower in Qatar (Figure 2). Figure 3 shows the analyses of a number of single-family houses. All the solar potential heat maps were calculated and generated based on the total solar radiation that each unit area on the building surfaces receive during the selected day (June 22).

These examples should give you some ideas about what the current version of Energy3D is already capable of doing in terms of solar energy analysis to support, for example, the design of rooftop solar systems and building solar facades.

In the months to come, I will continue to enhance this analytic capacity to provide even more powerful simulation and visualization tools. Optimization, which will automatically identify the boundary meshes (meshes that are on the building envelope), is currently on the way to increase the simulation speed dramatically.

New features in CODAP

Our Common Online Data Analysis Platform (CODAP) software provides an easy-to-use web-based data analysis tool, geared toward middle and high school students, and aimed at teachers and curriculum developers. CODAP is already full of amazing features. We’re excited to announce several new features! Continue reading

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.

Why is Israel building the world’s tallest solar tower?

Fig. 1: Something tall in Negev desert (Credit: Inhabitat)
The Ashalim solar project (Figure 1) in the Negev desert of Israel will reportedly power 130,000 homes when it is completed in 2018. This large-scale project boasts the world’s tallest solar tower -- at 250 meters (820 feet), it is regarded by many as a symbol of Israel’s ambition in renewable energy.

Solar thermal power and photovoltaic solar power are two main methods of generating electricity from the sun that are somewhat complementary to each other. Solar tower technology is an implementation of solar thermal power that uses thousands of mirrors to focus sunlight on the top of a tower, producing intense heat that vaporizes water to spin a turbine and generate electricity. The physics principle is the same as a solar cooker that you have probably made back in high school.

Why does the Ashalim solar tower have to be so tall?

Surrounding the tower are approximately 50,000 mirrors that all reflect sun beams to the top of the tower. For this many mirrors to "see" the tower, it has to be tall. This is easy to understand with the following metaphor: If you are speaking to a large, packed crowd in a square, you had better stand high so that the whole audience can see you. If there are children in the audience, you want to stand even higher so that they can see you as well. The adults in this analogy represent the upper parts of mirrors whereas the children the lower parts. If the lower parts cannot reflect sunlight to the tower, the efficiency of the mirrors will be halved.

Fig. 2: Visualizing the effect of tower height
An alternative solution for the children in the crowd to see the speaker is to have everyone stay further away from the speaker (assuming that they can hear well) -- this is just simple trigonometry. Larger distances among people, however, mean that the square with a fixed area can accommodate less people. In the case of the solar power tower, this means that the use of the land will not be efficient. And land, even in a desert, is precious in countries like Israel. This is why engineers chose to increase the height of tower and ended up constructing the costly tall tower as a trade-off for expensive land.

Fig. 3: Daily output graphs of towers of different heights
But how tall is tall enough?

Fig. 4: Energy output vs. tower height
This depends on a lot of things such as the mirror size and field layout. The analysis is complicated and reflects the nature of engineering. With our Energy3D software, however, complicated analyses such as this are made so easy that even high school students can do. Not only does Energy3D provide easy-to-use 3D graphical interfaces never seen in the design of concentrated solar power, but it also provides stunning "eye candy" visualizations that clearly spell out the science and engineering principles in design time. To illustrate my points, I set up a solar power tower, copied and pasted to create an array of mirrors, linked the heliostats with the tower, and copied and pasted again to create another tower and another array of mirrors with identical properties. None of these tasks require complicated scripts or things like that; all they take are just some mouse clicks and typing. Then, I made the height of the second tower twice as tall as the first one and run a simulation. A few seconds later, Energy3D showed me a nice visualization (Figure 2). With only a few more mouse clicks, I generated a graph that compares the daily outputs of towers of different heights (Figure 3) and collected a series of data that shows the relationship between the energy output and the tower height (Figure 4). The graph suggests that the gain from raising the tower slows down after certain height. Engineers will have to decide where to stop by considering other factors, such as cost, stability, etc.

Note that, the results of the solar power tower simulations in the current version of Energy3D, unlike their photovoltaic counterparts, can only be taken qualitatively. We are yet to build a heat transfer model that simulates the thermal storage and discharge accurately. This task is scheduled to be completed in the first half of this year. By that time, you will have a reliable prediction software tool for designing concentrated solar power plants.