Monthly Archives: May 2017

New CODAP Website

Check out our newly revamped CODAP website!

Our new CODAP website design. https://codap.concord.org

Our newly designed and upgraded CODAP website has a new and fresher look. Visit us at codap.concord.org

We hope you like our new CODAP website, which is designed to make it easier to find information about CODAP and data science education. In particular, we hope you like the design which, is more modern and is intended to be easy to navigate.

We wanted a new website to better collaborate with our CODAP community, including educators, software developers, partners, researchers, and curriculum developers. You are now able to post comments, CODAP questions, and share use cases on our CODAP forums. We check the forum every day to keep in touch with you about all the things happening related to what we’re most passionate about—helping our users work with each other in the emerging field of data science education!

In addition to our new website, we invite you to join us for our upcoming webinar series starting on May 30.

Our first speaker is Cliff Konold of SRRI Education. Cliff will lead a discussion entitled “Modeling as a core component of structuring data” based on an upcoming article (Konold, Finzer, & Kreetong, in press), which describes research on student understanding of and ability to organize complex data for analysis. We expect lively discussion with Cliff’s facilitation.

Please RSVP for our May 30 webinar using our Eventbrite page here.

We are continuing to update our help pages and example documents with useful information about our work and also hope to include useful information on data science education related issues, so please do check back with us for updates.

Please contact us at codap@concord.org to let us know what you think of our new website—all comments and feedback are welcome. Please also let us know if you cannot find something or would like to make any suggestions for new information or topics.

Many thanks for your ongoing support and we look forward to hearing from you!

A Mickey Mouse-shaped solar farm

Fig. 1: An aerial view of the Mickey Mouse-shaped solar farm
Fig. 2: An Energy3D model of the Mickey Mouse-shaped solar farm
If I didn't tell you that this is an actual solar farm near the Epcot Theme Park in the Disney World in Orlando, Florida, you probably would think this is some kind of school project done by kids. But no, this 22-acre 5 MW project was designed and installed by Duke Energy and it has been powering Disney World's facilities since 2016 (Figure 1 is an image from Disney.com). So this is some kind of serious business and has drawn a lot of media attention. The solar farm is so new that even the latest version of Google Maps in May 2017 still does not show it (it is available through Google Maps API that we are using, though).

By shaping the beloved Mickey Mouse character with tens of thousands of solar panels, Disney World has delivered a strong message to the world that the company is committed to a sustainable future.

Fig. 3: A solar radiation heat map representation (June 22).
But who says that kids should not do this? Perhaps they couldn't do it because of the lack of appropriate support and tool. Not any more. Thanks to the support from the National Science Foundation, our powerful Energy3D software and our Solarize Your World curriculum can probably turn every wild imagination in solar power into virtual reality, particularly for children who may need more inquiry- and design-based activities that connect so deeply to their world and their future. Figure 2 shows a model of the Mickey Mouse-shaped solar farm in Energy3D and Figure 3 shows a heat map representation of the solar radiation onto the solar panel arrays.

Designing ground-mounted solar panel arrays: Part III

Fig. 1: Rows of solar panels on racks in a solar farm
The most common configuration of solar farms is perhaps arrays consisting of rows of solar panel racks such as shown in Figure 1. But have you ever thought about why? Can we challenge this conventional wisdom?

Fig.2: Cover the field with horizontally-placed solar panels
Obviously, some inter-row spacing allows for easier cleaning and maintenance and, perhaps, even integration with agricultural farming (e.g., growing mushrooms that prefer shaded areas). But let's put those benefits aside for now and just consider the energy part of the problem. Let me point out a fact: If we completely cover the entire field with solar panels with zero tilt angle and zero gap (Figure 2), we are guaranteed to capture almost every single photon that strikes the area regardless of time and location. Such a simple-minded "design" will produce the maximal output of any given field at any location and time and there is absolutely no such problem as inter-row shading. So what solar design?
Fig. 3: Comparing two hypothetical fields.

It turns out that, although the simple-minded design can surely generate maximum electricity, each individual solar panel in it does not necessarily generate a maximum amount of electricity over the course of a year, compared with other designs. In other words, it may just use more solar panels to generate more electricity. As engineering design must consider cost effectiveness and even put it as a top priority, an engineer's job is then to look for a better solution that maximizes the production of each solar panel.

Fig. 4: Compare outputs of single panels in two fields (Boston).
A great advantage of Energy3D is that it allows one to experiment with ideas rapidly. So let's create a field with tilted rows of solar panels and leave some gap between them and then use the Group Analysis Tools to compare the daily and annual outputs of individual solar panels in the two hypothetical fields (Figure 3). And let's assume the fields are in Boston.

Fig. 5: Compare outputs of single panels in two fields (Phoenix).
Figure 4 shows that the total annual output of a single solar panel in the field of tilted rows is nearly 20% higher than that of a single solar panel in the field of flat cover in Boston (42° N). In this simulation, the tilt angle was set to be equal to the latitude. This cost effectiveness is one of the main reasons why we choose tilted rows of solar panels in high-latitude areas (aside from the fact that tilted angles allow rain to wash panels more efficiently and snow to slide from them more quickly).

Caveat for low-latitude locations


Fig. 6: Compare outputs of single panels in two fields (Mexico).
Note that this result applies only to high-latitude areas such as Boston. If we are designing solar farms for tropical areas such as Singapore, the story may be completely different. In low-latitude areas, small or even zero tilt angles make sense. Therefore, the design principle may be to cover the field with as many solar panels as possible or to use trackers to increase individual outputs (whichever is more economic depends on the relative prices of solar panels and solar trackers that change all the time). You can experiment with Energy3D to find out at which latitude this principle starts to become dominant. Figure 5 shows that the results in cities with a lower latitude such as Phoenix (33° N) and Mexico City (19° N) in North America. In the case of Phoenix, AZ, the gain from the tilted rows drops to about 10%. In the case of Mexico City, it drops to 5%. So designing a ground-mounted solar array for Mexico may be very different from designing a ground-mounted solar array for Canada.

National Science Foundation funds research and development of an IoT platform for smart schools

Fig. 1: A schematic illustration of IoT as a STEM learning integrator
Future sustainable and resilient infrastructure is expected to be powered by renewable energy, be able to respond intelligently to changes in the environment, and support smart and connected communities. We are pleased to announce that the National Science Foundation (NSF) has awarded our team a $2.9 million, four-year grant to explore the STEM education and workforce development challenges and opportunities in the coming transformation of our nation's infrastructure.

One of the core innovations will be a cyber-physical engineering platform for designing Internet of Things (IoT) systems that manage the resources, space, and processes of a community based on real-time analysis of data collected by various sensors. This innovation is potentially transformative as it can turn the entire building of a home, the entire campus of a school, or the entire area of a town into an engineering laboratory with virtually unlimited opportunities for learning, research, and exploration.

Fig. 2: A possible IoT system for managing a parking lot
Designing an IoT system provides plenty of opportunities to learn math, science, engineering, and computation practices in an integrated fashion, rather than in isolation. Working with sensors allows students to learn the science behind them through inquiry. For example, to calibrate an IoT system, students must understand what specific variables the sensor data represent scientifically. They must analyze the data to explore in what ranges the variables are supposed to vary in different scenarios in order to determine which type of response should be triggered, to what, and when. The acquired knowledge is then applied to the design of an IoT system, which requires engineering design thinking to make trade-off decisions, optimize system performance, and achieve cost effectiveness. Finally, the control, response, and integration of the entire system are realized through computer programming that deals with all foreseeable complexities. The overlaps among three basic skills—scientific reasoning, design thinking, and computational thinking—supported by the IoT platform provide researchers an opportunity to study their integration, as illustrated in Figure 1. (In fact, mathematical thinking is also involved, but let's just leave that out for now.)

This project is unique to engineering and computer science education because IoT is not only a crucial part of electrical engineering and information technology, but it is also one of the few ways through which computer programming can be directly linked to scientific inquiry and engineering design in the material world. Figure 2 provides an example.

This work is supported by the NSF under grant number 1721054. Any opinions, findings, and conclusions or recommendations expressed in this paper, however, are those of the author(s) and do not necessarily reflect the views of the NSF.

A complete 3D model of the PS20 solar power plant

According to Wikipedia, the 20 MW PS20 Solar Power Plant in Seville, Spain consists of a solar field of 1,255 heliostats. Each heliostat, with a surface area of 120 square meters(!), automatically tracks the sun on two axes and reflects the solar radiation it receives onto the central receiver, located at the top of a tower that is as tall as 165 meters. The concentrated heat vaporizes water and produces steam that drives a turbine to generate electricity. The Wikipedia page mentions that PS20 uses a thermal storage system, but it is not clear whether it is a molten salt tank or not.

PS20 generates about 48,000 MWh per year, or roughly 132 MWh per day on average without considering seasonal variations.

The full 3D model of the PS20 plant is now available in Energy3D and can be downloaded from http://energy.concord.org/energy3d/designs/ps20-solar-tower.ng3. While it generally costs hundreds of millions of dollars to design and build such a futuristic power plant, it costs absolutely nothing to do so in the virtual space of Energy3D. In a way, Energy3D gives everyone, especially those in developing nations, a powerful tool to explore the solar potential of their regions. Whether you live in a desert or on the coast, near or far away from the equator, in cities or rural areas, you can imagine all sorts of possibilities with it.

I am working on heat transfer, energy conversion, and thermal storage models that can predict the electricity generation accurately. Right now, Energy3D estimates the raw solar radiation input to the receiver on June 22 to be about 656 MWh, considering all the shadowing and blocking losses. If the system efficiency of heat transfer and energy conversion is in the range of 30-50%, then Energy3D's prediction will fall into a reasonable range.

Artificial intelligence research for engineering design

Have you ever thought about what a pity it is when a senior engineer with 40 years of problem-solving experience retires? Have you ever thought about what a loss it is when a senior teacher with 40 years of teaching experience retires? Imagine what we could do for humanity if we find a way to somehow preserve their experience, expertise, and intelligence automatically before these incredible treasures are taken to the graveyard...

Heat map visualizations of different patterns of design task transition
Funded by the National Science Foundation, I have been working on the research and development of artificial intelligence (AI) for engineering design for a number of years and have been developing the Visual Process Analytics for visualizing and analyzing engineering design process data. This exciting intersection among AI (basically everything about how intelligence can be realized), engineering (basically a generative and creative discipline), and cognitive science (basically everything about how humans acquire intelligence) is full of tremendous challenges, but it also creates unprecedented opportunities that constantly entice and enlighten me.

I have recently written a short article to explain my research to the lay people (mostly educators, but the implications are not limited only to education). Check it out at http://energy.concord.org/~xie/papers/aired.pdf

National Teacher Appreciation Day & Teaching System Modeling with SageModeler

We are delighted to highlight the work of Erin Cothran from Hudson (Massachusetts) High School, for National Teacher Appreciation Day! Erin is teaching a 10th grade chemistry unit she developed based on the driving question, “How can something that can’t be seen crush a 67,000 lb. oil tanker made of half-inch steel?” The unit includes four activities:

  • Why do my ears hurt when I dive in the deep end of the pool?
  • Why do I have to let air out of my car tires in the summer but add more air to my tires in the winter?
  • Why does a soda can explode if it is left unopened and in a hot car?
  • How can a big metal drum be crushed using air pressure?

Each activity includes opportunities for students to build, test, and revise systems models using our free, web-based SageModeler dynamic modeling software.

Erin said, “Using SageModeler has changed how I teach about systems modeling. With the the Next Generation Science Standards being adopted by many schools, modeling has become a main focus of lessons. Working with SageModeler over the past year has allowed me insight into how computer models can be used to help answer questions.”

“I am able to facilitate my students’ learning about the components that make good models effective,” she noted. “Even more importantly, students are discovering that models need to be adjusted as science evolves, that it is okay to not get it correct on the first attempt. Through learning how to build models they are able to define relationships between variables and test their ideas. They love picking custom images, making links, and running the simulation to see the outcomes.”

Dan Damelin, a Principal Investigator of our Building Models project, which is developing SageModeler, reports, “Erin is a very thoughtful teacher who engages students by using the models they generate to drive class discussion.”

The Building Models project is a collaboration between the Concord Consortium and the CREATE for STEM Institute at Michigan State University. The project is researching how the use of a semi-quantitative systems dynamics tool to construct external models helps students build mental models as well as how teachers and curriculum materials can support and scaffold student learning with respect to the interplay between external and internal models. We look forward to learning more from Erin and all the Building Models research teachers.

Designing ground-mounted solar panel arrays: Part II

To design a solar panel array, we need to understand the specifications of the type of solar panel that we are going to use (here is an example of the specs of SunPower's X21-series). Although all solar panels provide nominal maximum power outputs (Pmax or Pnom), those numbers specify the DC power outputs under the Standard Test Conditions (STC) or PVUSA Test Conditions (PTC). Those numbers only provide some standardized values for customers' reference and cannot be used to calculate the electricity generation in the real world. Although each brand of solar panel may be designed in different ways and the specs vary, there are a few scientific principles that govern most of them. The calculation of power generation can therefore be drawn upon these fundamental principles. This article covers some of these principles.

The first parameter for solar power calculation is the solar cell efficiency, which defines the percentage of incident sunlight that can be converted into electricity by a cell of the solar panel. This property is usually determined by the semiconductor materials used to make the cell. Monocrystalline silicon-based materials tend to have a higher efficiency than polycrystalline ones. As of 2017, the solar cell efficiency for most solar panels in the market typically ranges from 15% to 25%. The higher the efficiency, the more expensive the solar panel.

Figure 1: All cells in a series (left) and diode bypasses (right)
The solar cell efficiency generally decreases when the temperature increases. To reflect this relationship, solar panels usually specify the Nominal Operating Cell Temperature (NOCT) and the Temperature Coefficient of Pmax. The former describes how high the temperature of the cell rises to under the sun. The latter describes how much the solar cell efficiency drops as the cell temperature rises. If we know the solar cell efficiency under STC, the NOCT, the Temperature Coefficient of Pmax, the air temperature, and the solar radiation density on the surface of the cell, we can compute the actual efficiency of the solar cell at current time.

Now, in order to compute the actual power output of the cell, we will need to know two more things: the area of the cell and the angle between the surface of the cell and the direction of the sun. The area of the cell is related to the packing density of the cells on a solar panel. Polycrystalline solar cells can have nearly 100% of packing density as they are usually rectangular, whereas monocrystalline ones have less packing density as they usually have round corners (therefore, they can't use up the entire surface area of a solar panel). The angle between the cell and the sun depends on how the solar panel is installed. This usually comes down to its tilt angle and azimuth.

Figure 2: Landscape vs. portrait (diode bypasses, location: Boston)
All these parameters are needed in Energy3D's solar radiation simulation. As a user, what you have to do is to understand the meaning of these parameters while designing your solutions and set the parameters correctly for your simulations. As Energy3D hasn't provided a way to select a solar panel model and then automatically import all of its specs, you still have to define a solar panel brand by setting its properties manually.

The next thing we must consider is a little tricky. A solar panel is made of many cells, arranged in an array of, for example 6 × 10. In order for the cells to produce usable voltage, they are usually connected in a series (the left image in Figure 1). In this case, the electric current flowing through each cell is the same but the voltage adds up. However, the problem with a series circuit is that, if one cell gets shaded by, say, a leaf that falls on it, and as a result generates a weaker current, every other cell of the panel will end up generating a smaller output (worse, all the generated electricity that cannot flow freely will turn into heat and damage the cells). To mitigate this problem, most solar panels today use diode bypasses (the right image in Figure 1) or similar technologies to allow the part of the solar panel that is not shaded to be able to contribute to the overall output. However, if the shade is not as spotty as is in the case of a leaf, even the diode bypasses will not be able to prevent complete loss (this video nicely demonstrates the problem). Therefore, our design of solar arrays must consider the actual wiring of the solar cells on the solar panel that we choose.

Figure 3: Month-by-month outputs of four arrays in Figure 2.
What are the implications of the cell wiring? Figure 2 shows four solar panel arrays with two different inter-row distances but the same number of identical solar panels that connect their cells with diode bypasses. The size of each solar panel is about 1 meter × 2 meters. On the racks of two arrays, the solar panels are placed in the landscape orientation -- each rack has therefore four rows of solar panels. On the racks of the other two arrays, they are placed in the portrait orientation -- each rack has therefore two rows of solar panels. When the inter-row spacing between two adjacent racks is the same, our simulation suggests that the landscape array always generates more electricity than the portrait array. This difference demonstrates the effect of the cell wiring using diode bypasses. In the front part of Figure 2 for arrays with narrower inter-row spacing, the simulation shows that about a quarter of the area on the racks after the first one is shaded during the course of the day (as indicated by their blue coloring). When the solar panels at the bottom of a rack is shaded, a portrait orientation reduces the output of 50% of the solar panels (there are two rows of solar panels on each rack in the portrait array shown in Figure 2), while a landscape orientation reduces the output of 25% of the solar panels (there are four rows of solar panels on each rack in the landscape array shown in Figure 2). The difference becomes less when the inter-row distance is longer. So when you have a limited space to place your solar arrays, you should probably favor the landscape orientation.

Figure 4: Shadow analysis shows inter-row shading in four seasons.
Of course, the output of a solar array depends also on the season. When the sun is high in the sky in the summer, the inter-row shading becomes less a problem. It is during the winter months when the shading loss becomes significant. This is shown in Figure 3. A snapshot of the shadow analysis (Figure 4) illustrates the difference visually.

For sites in the snowy north, another factor in the winter that favors the landscape orientation is the effect of snow accumulation on the panels. As soon as snow slides off the upper third of a solar panel in the landscape arrangement, it will start to generate some electricity. In the case of the portrait arrangement, it has to wait until all the snow comes off the panel.

Note that this article is concerned only with the cell wiring on a solar panel. The wiring of solar panels in an array is another important topic that we will cover later.