Global pattern of insolation predicted by Energy3D

Saturday, May 31st, 2014 by Charles Xie
Figure 1. Global insolation pattern from Pole to Pole
The Sun's power drives the climate of the Earth. Accurately modeling the incident solar radiation, namely, insolation, at a given location is important to the design of high-performance buildings. As I have blogged last week, the insolation calculation in our Energy3D software considers the incident angle of the Sun to the surface, the duration of the day, and the air mass. And we have recently incorporated the effect of altitude and the ambient inputs.

Figure 2. Real data for the three locations (source)
In Energy3D, we can easily investigate the global pattern of insolation by horizontally placing a sensor module on the ground and then collecting the sensor data throughout the year. We can easily change the latitude and collect a new set of data. Figure 1 shows the global insolation pattern from the North Pole to the South Pole. The time integral of each curve represents the total solar energy a location at the corresponding latitude receives. There is an interesting observation from Figure 1: The Equator doesn't actually have the highest peak value and its peak values are not in the summer but in the spring and fall. However, because the insolation does not differ very much from season to season in the Equator, its time integral is much larger, which is the Equator is hot all year round.
Figure 3. Energy3D's prediction

How accurate are the predictions of Energy3D? Let's pick three locations that someone has collected real data, as shown in Figure 2. More insolation data can be found on this website. (Surprisingly, the peak solar energy at the South Pole is higher than the peak solar energy at the Equator.)

Figure 3 shows that the insolation values predicted by Energy3D. As you can see, the predicted trend agrees reasonably well with the trend in the real data. Overall, Energy3D tends to underestimate the insolation by about 50% (after unit conversion), however.

The effect of air mass on building solar performance

Monday, May 26th, 2014 by Charles Xie
Figure 1
As it travels through the atmosphere of the Earth, the light from the Sun interacts with the molecules in the air and are scattered or absorbed, causing the radiation energy that ultimately reaches the ground to weaken. This effect is more significant in early morning or late afternoon than at noon because sunlight has to travel a longer distance in the atmosphere before reaching the ground (Figure 1) and, therefore, has a higher chance of being scattered or absorbed in the atmosphere. This is also why stars appear to be less bright at the horizon than above head.

In solar energy engineering, this effect is called the air mass, which defines the length of the sunlight’s path through the atmosphere (not to be confused with air mass in meteorology, which defines a volume of air that can be as large as thousands of square miles).

Why is the air mass important to predicting solar energy performance of a building?

Figure 2
Let's consider a high latitude location like Boston. For a south-facing window, the Sun is lower in the sky in the winter, causing more solar energy to shine into the house, compared with the case in the summer. This is known as the projection effect (Figure 2). Does this mean that we get a lot more solar energy from the window in the winter like the cross-sections of the solar beams in Figure 2 seem to indicate? Not so fast.

In Boston, the day in the winter can be as short as 9 hours and the day in the summer can be as long as 15 hours. So even if the projection effect favors the winter condition, the duration of the day doesn't. Our calculation must consider these two competing factors. After these considerations, the solar energy the window gains in 12 months is shown in Figure 3: It turns out that the window still gets more energy in the winter -- the projection effect wins big!

For now.

OK, let's now include the effect of the air mass in the calculation. Big surprise (at least to me when I first saw the results)!

Figure 3
Figure 4 shows that a south-facing window no longer picks up the highest amount of solar energy in the winter. Its solar gain peaks in the spring and fall and the difference between the summer value and the winter value decreases dramatically.

As comparisons, Figures 3 and 4 also show the results for a west-facing and a north-facing window, both peaking in the summer (for different reasons that we will not elaborate here).

Figure 4
Is this finding the universal truth? Not at all! It turns out that the peak solar gain of a south-facing window depends on the latitude. Figure 5 shows the comparison of four locations from Miami to the North Pole. In Miami, the energy gain peaks in the winter and declines almost to zero in the summer. In contrast, at the North Pole (to which anywhere else is south), the energy gain peaks in the summer and is nearly zero for almost six months. The situation of Moscow is between Boston and the North Pole, with the peaks moving more towards the summer and are less distinguishable.
Figure 5

Given Figure 5, the fact that a south-facing window in Boston receives peak solar energy in the spring and fall becomes comprehensible now -- by the law of mathematics, the transition from the Equator to the North Pole must be smooth and the energy peak of any latitude in-between must be somewhere between winter (the season of peak energy in the Equator) and summer (the season of peak energy in the North Pole).

Interestingly enough, the peak energy at the North Pole is comparable to that at any other location in Figure 5. Considering that the Arctic has 24 hours of sun in the summer and the projection effect reaches maximum, this result is in fact a good demonstration of the air mass effect. Without the air mass, the North Pole would have gotten three times of solar energy as it does now, making the Arctic a tropical resort in the summer. Our planet would have been quite different.

All these analytic capabilities are freely available in our Energy3D software and all you need to do are some mouse clicks and some thinking. For the air mass calculation, you can choose to use the Homogeneous Sphere Model (default) or Kasten-Young Model. You can also turn the air mass off temporarily to evaluate its effect, just like what I showed in this article -- this is a piece of cake in Energy3D but is impossible to do in reality because you cannot turn the atmosphere off!

Iranian studies show the effectiveness of Molecular Workbench

Wednesday, May 7th, 2014 by Charles Xie
A Molecular Workbench virtual experiment used in the Iranian study.
In the May Issue of Journal of Educational and Social Research, published by MCSER (Mediterranean Center of Social and Educational Research) in Rome, researchers from Iran and Malaysia reported that "students who were taught using the Molecular Workbench software performed better in post-tests on five chemistry topics as compared with those who received conventional instruction." This study was conducted in Iranian secondary schools with 70 students. The researchers also reported that "students using the software also found this software useful in the learning of chemistry." Their paper, titled with "Molecular Workbench Software as Computer Assisted Instruction to Aid the Learning of Chemistry", is freely available in this open-access journal. The authors are Elaheh Khoshouie, Ahmad Fauzi Mohd Ayub, and Farhad Mesrinejad, from two universities in Iran and Malaysia, respectively.

This example, once again, demonstrates the power of visualization in science education. Regardless of the culture or religion children may have grown up with, scientific visualization transcends all the man-made barriers to convey science messages to the young minds. In the case of Molecular Workbench, the effect is even more profound because the heart of it has actually been written in the universal language of humanity -- mathematics.

More analytic capabilities added to Energy3D

Sunday, April 27th, 2014 by Charles Xie

Add any number of sensors to a house.
According to Wikipedia, computer-aided engineering (CAE) is typically done through the following steps:
  1. Pre-processing: This step defines the 3D geometry, the initial conditions, and the boundary conditions of the model.
  2. Analysis solver: This step predicts the properties of the model and, sometimes, their time evolution.
  3. Post-processing: This step visualizes the results of the analysis using maps and graphs.
Sensor graphs.
In real engineering practices, these three steps are often done using different computer programs, run on different computers, or even carried out by different people. This time-consuming and complicated process often prevents CAE tools from being productive in the classroom, as many students cannot overcome the long learning curve in a very short time permitted by their schedules.

One of the critical features that distinguish our Energy3D software from other CAD tools is that it eliminates all these gaps and delays. From day one of the Energy3D project, we have envisioned a CAD tool that supports concurrent inquiry and design, thus allowing students to explore many design options with scientific inquiry and rapidly get feedback to help them make design decisions. This is an essential innovation that makes a CAD tool broadly useful for teaching engineering design skills rather than just computer drawing skills.
Construction cost.

To move closer to our goal, we have recently added many new, exciting features to Energy3D Version 3.0 to greatly advance its analytic capacity. These features include:

1) Virtual sensors. Students can add any number of sensor modules to any surface of the buildings under design to measure insolation density and heat transfer at the building envelope. This is analogous to using sensor-based data loggers in building diagnostics. Virtual sensors will allow Energy3D to eventually support systems design, creating opportunities for students to practice systems thinking in the context of building intelligence. For example, students will be able to simulate Google's NEST Learning Thermostat and explore how much energy can be saved using these smart house technologies.

Energy vs. orientation
2) Construction cost analysis. Any real engineering project is subject to a budgetary limit. While students are designing, Energy3D predicts the construction cost and breaks it down into categories. They will get a warning when a design goes over a budget, creating a financial constraint for a design project.

3) Building orientation optimization. Students can rotate a building and explore how energy can be saved by simply choosing an optimal orientation.

Together with the features of seasonal analyses announced before, Energy3D provides an increasingly comprehensive simulation environment for learning engineering design in the context of sustainable housing.

Green building design with Energy3D: How big should south-facing windows be?

Thursday, April 17th, 2014 by Charles Xie
Many people know that south-facing windows can help to heat a house in the winter because they let a lot of sunlight in. Exactly how much of the south-facing wall should we allocate to windows? What are the downsides? How can we avoid them? Our Energy3D software allows students to explore the problems and find the solutions.

Figure 1

Suppose we have a simple house like the one shown in Figure 1 and we are in the Boston area. Energy3D supports students to try a design choice, run a simulation, collect the data, analyze the result, and evaluate the solution -- all in real time as is shown in the video in this post. Energy3D's powerful simulation and analysis tools provide instantaneous feedback to students so that their design processes can be guided and informed by the scientific and engineering principles built in the software. Let's use the investigation of south-facing windows described above as an example.

Figure 2 (Excel graph)
Suppose a student follows the design trajectory as shown in Figure 1. A challenge is to keep the yearly energy cost needed to maintain the temperature of the house at 20℃ to be as low as possible. The student begins with adding a small window to the south side of the house. By running the seasonal energy analysis tool in Energy3D, she immediately discovers that, by adding a small window, she can cut the energy cost a bit. Then she enlarges the window and finds that more energy can be saved. So she goes on to increase the size of the window. However, she finds that, at some point, larger windows on the south side start to cost more energy. After she adds two large windows, the energy cost increases over 15%, compared with the case of no window at all. Figure 2 shows the energy cost, broken down to heater and AC, as a function of the window area. That doesn't quite make sense to her. So she has to stop and think about why.
Figure 3 (Energy3D graph)

The trend in Figure 2 suggests that, with the enlargement of windows on the south side, the cooling cost continues to rise while the heating cost levels off. A monthly breakdown in Figure 3 reveals this trend more clearly. As shown by the golden dashed line in Figure 2, the solar heating through the windows increases rapidly when their total area gets enlarged.

Figure 4 (Energy3D graph)
Figure 5 (Energy3D graph)
If she wants to keep the large window area in the south side (for natural lighting and sanity of the occupants!), she has to reduce the solar heating effect through the windows in the summer. One way to do this is to plant tall deciduous trees in front of the windows as shown in Figure 1. The trees provide shading for the windows in the summer but let sunlight shine through to the windows in the winter (in Energy3D, deciduous trees have leaves from May 1st to November 30th). Figure 4 shows the effect of the two deciduous trees on the solar gain through the two south-facing windows. From the graph, she can see that the trees cut down the solar heating in the summer. As a result, the AC cost is reduced, as shown in Figure 5, whereas the heating cost is almost unchanged.

She concludes that, with the trees planted to the south of the house, the net energy cost over a year can be decreased to lower than the case of no window at all, providing an acceptable solution that takes care of view, lighting, and landscaping.

The Energy3D graphs in this blog post show that students can keep the results of previous runs (the curve of each run is labeled by a number) and superimpose new data on top of them. As the data view can get quite complex, Energy3D provides options to turn on/off data types and runs. The embedded video shows how those features work for visualizing and analyzing the simulation results.

PS: Some readers may notice that our calculations predict higher AC cost in September than in August or July. This is because when those calculations were done, the house had no window on the east or west side. Adding windows to those sides, the AC cost will peak around July or August -- even when the trees are not present.

The first paper on learning analytics for assessing engineering design?

Thursday, January 30th, 2014 by Charles Xie
Figure 1
The International Journal of Engineering Education published our paper ("A Time Series Analysis Method for Assessing Engineering Design Processes Using a CAD Tool") on learning analytics and educational data mining for assessing student performance in complex engineering design projects. I believe this is the first time learning analytics was applied to the study of engineering design -- an extremely complicated process that is very difficult to assess using traditional methodologies because of its open-ended and practical nature.

Figure 2
This paper proposes a novel computational approach based on time series analysis to assess engineering design processes using our Energy3D CAD tool. To collect research data without disrupting a design learning process, design actions and artifacts are continuously logged as time series by the CAD tool behind the scenes, while students are working on an engineering design project such as a solar urban design challenge. These "atomically" fine-grained data can be used to reconstruct, visualize, and analyze the entire design process of a student with extremely high resolution. Results of a pilot study in a high school engineering class suggest that these data can be used to measure the level of student engagement, reveal the gender differences in design behaviors, and distinguish the iterative (Figure 1) and non-iterative (Figure 2) cycles in a design process.

From the perspective of engineering education, this paper contributes to the emerging fields of educational data mining and learning analytics that aim to expand evidence approaches for learning in a digital world. We are working on a series of papers to advance this research direction and expect to help with the "landscaping" of  those fields.

Tablet-friendly STEM Resources

Friday, January 24th, 2014 by Jen Goree

Is your New Year’s resolution to find more interactive STEM resources that are tablet-ready? (We understand — we make similar technology-related resolutions, too!) We’ve optimized many of our browser-based interactive resources to run on popular tablets. By tuning our code, we’re able to make the power of our models available for your students!

For example, this Phase Change interactive runs 60% faster than it did before our recent code improvements:

And Metal Forces runs 33% faster:

Here’s a few to try now:





For even more, check out a complete list of our tablet-friendly STEM resources.

Fireplaces at odd with energy efficiency? An Energy2D simulation

Saturday, January 18th, 2014 by Charles Xie
In the winter, a fireplace is the coziest place in the house when we need some thermal comfort. It is probably something hard to remove from our living standards and our culture (it is supposed to be the only way Santa comes into your house). But is the concept of fireplace -- an ancient way of warming up a house -- really a good idea today when the entire house is heated by a modern distributed heating system? In terms of energy efficiency, the advice from science is that it probably isn't.

Figure 1. A fire is lit in the fireplace.
When the wood burns, a fireplace creates an updraft force that draws the warm air from the house to the outside through the chimney. This creates a "negative pressure" that draws the cold air from the outside into the house through small cracks in the building envelope. This is called the stack effect. So while you are getting radiation heat from the fireplace, you are also losing heat in the house at a faster rate through convection. As a result, your furnace has to work harder to keep other parts of your house warm.

Figure 2. No fire.
Our Energy2D tool can be used to investigate this because it can simulate both the stack effect and thermostats. Let's just create a house heated by a heating board on the floor as shown in the figures in this article. The heating board is controlled by a thermostat whose temperature sensor is positioned in the middle of the house. A few cracks were purposely created in the wall on the right side to let the cold air from the outside in. Their sizes were exaggerated in this simulation.

Figure 1 shows the duty cycles of the heating board within two hours when the house was heated from 0 °C to 20 °C with a fire lit in the fireplace. A heating run is a segment of the temperature curve in which the temperature increases, indicating the house is being heated. In our simulation, the duration of a heating run is approximately the same under different conditions. The difference is in the durations of the cooling runs. A more drafty house tends to have shorter cooling runs as it loses energy more quickly. Let's just count those heating runs. Figure 1 shows that 15 heating runs were recorded in this case.

Figure 2 shows the case when there was no fire in the fireplace and the fireplace door was closed. 13 heating runs were recorded in this case.

What does this result mean? This means that, in order to keep the house at 20 °C, you actually need to spend a bit more on your energy bill when the fireplace is burning. This is kind of counter-intuitive, but it may be true, especially when you have a large drafty house.

Figure 3. In a house without cracks...
How do we know that the increased energy loss is due to the cracks? Easy. We can just nudge the window and the wall on the right to close the gaps. Now we have a tight house. Re-run the simulation shows that  only 11 heating runs were recorded (Figure 3). In this case, you can see in Figure 3 that the cooling runs lasted longer, indicating that the rate of heat loss decreased.

Note that this Energy2D simulation is only an approximation. It does not consider the radiation heat gain from the fireplace. And it assumes that the fire would burn irrespective of air supply. But still, it illustrates the point.

This example demonstrates how useful Energy2D may be for all precollege students. In creating this simulation, all I did is to drag and drop, change some parameters, run the simulation, and then count the heating runs. As simple as that, this tool could be a game changer in science and engineering education in high schools or even middle schools. It really creates an abundance of learning opportunities for students to experiment with concepts and designs that would otherwise be inaccessible. Similar experiences are currently only possible at college level with expensive professional software that typically cost hundreds or even thousands of dollars for just a single license. Yet, according to some of our users, our Energy2D rivals those expensive tools to some extent (I would never claim that myself, though).

The time of infrared imaging in classrooms has arrived

Thursday, January 9th, 2014 by Charles Xie
At the Consumer Electronics Show (CES) 2014, FLIR Systems debuted the FLIR ONE, the first thermal imager for smartphones that sells for $349. Compared with standalone IR cameras that often cost between $1,000 and $60,000, this is a huge leap forward for the IR technology to be adopted by millions.

With this price tag, FLIR ONE finally brings the power of infrared imaging to science classrooms. Our unparalleled Infrared Tube is dedicated to IR imaging experiments for science and engineering education. This website publishes the experiments I have designed to showcase cool IR visualizations of natural phenomena. Each experiment comes with an illustration of the setup (so you can do it yourself) and a short IR video recorded from the experiment. Teachers and students may watch these YouTube videos to get an idea about how the unseen world of thermodynamics and heat transfer looks like through an IR camera -- before deciding to buy such a camera.

For example, this post shows one of my IR videos that probably can give you some idea why the northern people are spraying salt on the road like crazy in this bone-chilling weather. The video demonstrates a phenomenon called freezing point depression, a process in which adding a solute to a solvent decreases the freezing point of the solvent. Spraying salt to the road melts the ice and prevents water from freezing. Check out this video for an infrared view of this mechanism! 

Dart projects of Energy2D and Quantum Workbench announced

Wednesday, January 8th, 2014 by Charles Xie
Last month, Google announced Dart 1.0, a new programming language for the Web that aims to greatly accelerate Web development. Dart uses HTML5 as the UI. It can either run on the Dart Virtual Machine being built in Chrome or be compiled into JavaScript to run in other browsers. Dart can also be used to create standalone apps (I guess it is meant to be the main programming language for Google's own Chrome OS) or server-side software. An ECMA Technical Committee (TC 52) has been formed to make Dart into an international standard.

This is the moment I have been waiting for. As a developer with C/Java background, I am not convinced that JavaScript is made for large, complex projects (as Web programming seems to be moving towards) -- even after reading many articles and books about JavaScript. The facts that after ten years Google Docs still has only a tiny fraction of functionality of Word and basic functions such as positioning an image have not improved much suggest that its JavaScript front end has probably reached its limit.

Don't get me wrong. JavaScript is an excellent choice for creating interactive Web experiences. I use JavaScript extensively to create Web interfaces for interacting with the Energy2D applet. But I think it is in general healthy for the developer community if we are given more options. Recognizing the weaknesses of JavaScript, the community has already created CoffeeScript and TypeScript (supersets of JavaScript that strips off unproductive features of JavaScript) that also require compilation into native JavaScript. Dart is Google's solution to these problems that should be welcomed. To a Java developer like me, Dart provides a much better option because it returns the power of class-based object-oriented programming to developers who must create Web-based front ends. What is even sweeter is that its SDK provides a familiar Eclipse-based programming platform that makes many developers feel at home.

Excited about the potential of this new language (plus it is from Google and will be highly performant on Chrome), I am announcing the development of the Dart versions of our Energy2D and Quantum Workbench software. These software are based on complex mathematical solutions of extremely complex partial differential equations and will hopefully provide some showcases to anyone interested in Dart. This is not to say the development of the Java versions will cease. We are committed to develop and maintain both Dart and Java versions.

Hopefully 2014 will be an exciting year for us!