Category Archives: Projects

Simulating PTC and NTC heating elements with Energy2D

Figure 1: A demo simulation.
A heating element converts electricity into heat through Joule heating: Electric current passing through the element encounters resistance, causing the temperature of the element to rise. A thermistor is a type of resistor whose resistance changes significantly with temperature. In a heating element that uses a thermistor with a positive temperature coefficient (PTC), called a PTC heating element, the temperature increases rapidly. In a heating element that uses a thermistor with a negative temperature coefficient (NTC), called a NTC heating element, the heating will gradually weaken when the temperature increases.

Figure 2: Setting the temperature coefficient.
Several Energy2D users have requested adding PTC/NTC controls to the software. So this was added last night. You can now set the temperature coefficient while defining a power source, as shown in Figure 2.

Figure 1 shows the comparison of the temperature increasing in a PTC heater, a constant-power heater, and a NTC heater, with the temperature coefficients being 0.1, 0, and -0.1, respectively. Note that in the case of constant power, the temperature increases linearly in time (as per the definition of constant power), whereas PTC and NTC exhibit nonlinear behaviors.

You can click the link under the image to run the simulation yourself.

Design replay: Reconstruction of students’ engineering design processes from Energy3D logs

One of the useful features of our Energy3D software is the ability to record the entire design process of a student behind the scenes. We call the reconstruction of a design process from fine-grained process data design replay.


Design replay is not a screencast technology. The main difference is that it records a sequence of CAD models, not in any video format such as MP4. This sequence is played back in the original CAD tool that generated it, not in a video player. As such, every snapshot model is fully functional and editable. For instance, a viewer can pause the replay and click on the user interface of the CAD tool to obtain or visualize more information, if necessary. In this sense, design replay can provide far richer information than screencast (which records as much information as the pixels in the recording screen permit).


Design replay provides a convenient method for researchers and teachers to quickly look into students' design work. It compresses hours of student work into minutes of replay without losing any important information for analyses. Furthermore, the reconstructed sequence of design can be post-processed in many ways to extract additional information that may shed light on student learning, as we can use any model in the recorded sequence to calculate any of its properties.



The three videos embedded in this post show the design replays of three students' work from a classroom study that we just completed yesterday in a Massachusetts high school. Sixty-seven students spent approximately two weeks designing zero-energy houses -- a zero-energy house is a highly energy-efficient house that consumes net zero (or even negative) energy over a year due to its use of passive and active solar technologies to conserve and generate energy. These videos may give you a clue how these three students solved the design challenge.

Towards a multiphysics Energy2D

Figure 1: Particle motions driven by convective flow.
Up to yesterday, our Energy2D software has been a program for simulating, mostly, fluid and heat flows. But there are also objects in the world that are not fluids. To simulate that part of the world, we have to incorporate some other physics. A simple addition is to couple particles with fluids. This technique is commonly known as discrete phase modeling in the CFD community. It is used to model things such as suspension particles in fluids.

Figure 2: Heat traces of fireballs.
The latest version of Energy2D has a particle solver and a particle editor. Particles in Energy2D observe collision dynamics among themselves and interact with fluid and heat flows: particles can not only be moved by the fluid but also exert reaction force and transfer heat to the fluid. Figure 1 shows the motion of two types of particles driven by a convective flow. Depending on its density (relative to the fluid density), a particle may be buoyant enough to flow with the fluid or so heavy that it must sink to the bottom. This is shown in Figure 1: The black particles are the heavy ones and the white ones are the light ones; the convective force is not strong enough to move the black ones.

Particles can also transfer physical properties such as energy and momentum to the fluid while they are moving. Figure 2 shows the heat traces left by fireballs of different sizes.

Figure 3: Thermophoresis (Soret's effect)
With this new capacity, we can simulate phenomena such as thermophoresis, in which the different particle types in a mixture respond to a temperature gradient differently and thereby can be separated by just heating them up.

If you are enticed enough to want to see these simulations at work, click the links below the figures.

These new features represent an overdue step towards making Energy2D a versatile multiphysics simulation system. For engineering simulations, multiphysics is essential as real-world problems are often complicated by more than one mechanisms, each driven by its own physics.

The particle dynamics shown here is very simple (just a weekend's work). In the long run, I expect that a generic contact dynamics engine such as that of Box2D will be implemented in Energy2D. Coupling the Eulerian and Lagrangian reference frames, this integration will make Energy2D more interesting and useful. That would be a critical step towards our goal for Energy2D to simulate as many energy-related natural phenomena as possible.

Temperature change may not represent heat transfer; heat flux does.

Figure 1 (go to simulation)
There has been some confusion lately about the heat transfer representations in Energy2D simulations. By default, Energy2D shows the temperature distribution and uses the change of the distribution to visualize heat flow. This is all good if we have only one type of medium or material. But in reality, different materials have different thermal conductivities and different volumetric heat capacities (i.e., the ability of a given volume of a substance to store thermal energy when the temperature increases by one degree; the volumetric heat capacity is in fact the specific heat multiplied by the density).

A
Figure 2 (go to simulation)
According to the Heat Equation, the change of temperature is affected by the thermal diffusivity, which is the thermal conductivity divided by the volumetric heat capacity (now that I have written the terminology down, I can see why these terms are so confusing). In general, a higher thermal conductivity and a lower volumetric heat capacity will both result in faster temperature change.

To illustrate my points, Figure 1 shows a comparison of temperature changes in two materials. The pieces that have the same texture are made of the same material. The upper ones have a lower thermal conductivity but a higher thermal diffusivity. The lower ones have a higher thermal conductivity but a lower thermal diffusivity. In both upper and lower setups, the piece on the left side maintains a higher temperature to provide the heat source. Everything else starts with a low temperature initially. The entire container is completely insulated -- no heat in, no heat out. Two thermometers are placed just at the right ends of the middle rods. Their results show that the temperature rises more quickly in the upper setup (Figure 1) -- because it has a higher diffusivity.

The fact that something diffuses faster doesn't mean it diffuses more. In order to see that, we can place two heat flux sensors somewhere in the rods to capture the heat flows. Figure 2 shows the results from the heat flux sensors. Obviously, there is a lot more heat flow in the lower setup in the same time period.

Figure 3 (go to simulation)
The conclusion is that it is the heat flux, not the temperature change, that ultimately measures heat transfer. If you want to know how fast heat transfer occurs, the thermal conductivity is a good measure. However, if you want to know how fast temperature changes, the thermal diffusivity is a good measure. This may be also important to remember for those who use infrared cameras: Infrared cameras only measure temperature distribution, so what we really see from infrared images is actually thermal diffusion and thermal diffusion alone could be deceiving.

Figure 4 (go to simulation)
To make this even more fun (or confusing), let's replace the pieces on the right of the container with two pieces that are made of the same material that has a volumetric heat capacity between those of the other upper and lower ones. You wouldn't think this change would affect the results, at least not qualitatively. But the truth is that, the temperature in the lower setup in this case rises more quickly than the temperature in the upper setup -- exactly opposite to the case shown in Figure 1! The surprising result indicates how unreliable temperature change may be as an indicator of heat transfer. In this case, the temperature field of the middle rod is affected by what it is connected with. If we look at the results from the heat flux sensors (Figure 4), the heat flux that goes through the rod is much higher in the lower setup. This once again shows that heat flux is a more reliable measure of heat transfer.

In Energy2D, we have implemented an Energy Field view to supplement the Temperature Field view to remedy this problem.

Building performance analyses in Energy3D

Energy3D (Tree image credit: SketchUp Warehouse and Ethan McElroy)
A zero-energy building is a building with zero net energy consumption over a year. In other words, the total amount of energy used by the building on an annual basis is equal to or even less than the amount of renewable energy it produces through solar panels or wind turbines. A building that produces more renewable energy than it consumes over the course of a year is sometimes also called an energy-plus building. Highly energy-efficient buildings hold a crucial key to a sustainable future.


One of the goals of our Energy3D software is to provide a powerful software environment that students can use to learn about how to build a sustainable world (or understand what it takes to build such a world). Energy3D is unique because it is based on computational building physics, done in real time to produce interesting heat map visualization resembling infrared thermography. The connections to basic science concepts such as heat and temperature make the tool widely applicable in schools. Furthermore, at a time when teachers are required by the new science standards to teach basic engineering concepts and skills in classrooms, this tool may be even more relevant and useful. The easy-to-use user interface enables students to rapidly sketch up buildings of various shapes, creating a deep design space that provides many opportunities of exploration, inquiry, and learning.


In the latest version of Energy3D (Version 3.0), students can compute the energy gains, losses, and usages of a building over the course of a year. These data can be used to analyze the energy performance of the building under design. These results can help students decide their next steps in a complex design project. Without these simulation data to rationalize design choices, students' design processes would be speculative or random.

A complex engineering design project usually has many elements and variables. Supporting students to investigate each individual element or variable is key to helping them develop an understanding of the related concept. Situating this investigation in a design project enables students to explore the role of each concept on system performance. With the analytic tools in Energy3D, students can pick an individual building component such as a window or a solar panel and then analyze its energy performance. This kind of analysis can help students determine, for example, where a solar panel should be installed and which direction it should face. The video in this post shows how these analytic tools in Energy3D work.

Spring is here, let there be trees!

Trees in Energy3D.
Trees around a house not only add natural beauty but also increase energy efficiency. Deciduous trees to the south of a house let sunlight shine into the house through south-facing windows in the winter while blocking sunlight in the summer, thus providing a simple but effective solution that attains both passive heating and passive cooling using the trees' shedding cycles. Trees to the west and east of a house can also create significant shading to help keep the house cool in the summer. All together, a well-planed landscape can reduce the temperature of a house in a hot day by up to 20°C.

The tree to the south side shades the house in the summer.
With the latest version of Energy3D, students can add trees in designs. As shown in the second image in this blog post, the Solar Irradiation Simulator in Energy3D can visualize how trees shade the house and provide passive cooling in the summer.

The Solar Irradiation Simulator also provides numeric results to help students make design decisions. The calculated data show that the tree to the south of the house is able to reduce the sunlight shined through the window on the first floor that is closest to it by almost 90%. Students can do this easily by adding and removing the tree, re-run the simulation, and then compare the numbers. They will be able to add trees of different heights and types (deciduous or evergreen). There will be a lot of design variables that students can choose and test.

A design challenge is to combine windows, solar panels, and trees to reduce the yearly cost of a building to nearly zero or even negative (meaning that the owner of the house actually makes money by giving unused energy produced by the solar panels to the utility company). This is no longer just a possibility -- it has been a reality, even in a northern state like Massachusetts!

Energy3D in France and Energy3D User’s Guide

Solar irradiation simulations of urban clusters in Energy3D.
More than four years ago, I blogged about our ideas to develop a computer-aided design (CAD) program for education that is different from SketchUp. We wanted a CAD program that allows students to easily and quickly perform physical analyses to test the functions of their 3D models while constructing them -- in contrast to typical industry practices that involve pre-processing, numerical simulation, and then post-processing. We thought closing the gap between construction and analysis is fundamentally important because students need instantaneous feedback from some authentic scientific computation to guide their next design steps. Without such a feedback loop, students will not be able to know whether their computer designs will function or not -- in the way permitted by science, even if they can design the forms well.

Four years after Saeid Nourian and I started to develop our Energy3D CAD program, we received the following comment from Sébastien Canet, a teacher from Académie de Nantes:
"I am a French STEM teacher and a trainer of technical education teachers in west France. Our teachers loved your software! We were working on an 'eco-quartier' with the goal to use as much passive solar energy as possible. Each student worked with SketchUp to model his/her house and then pasted the model on a map. Then we tested different solar orientations. Your software is a really good complementary tool to SketchUp, though the purposes are not the same. It is fast, easy to use, and perfect for constructing!!! I will use it instead of SketchUp in our activities."

Sébastien wrote that, if we can provide a French version, there would be hundreds of French STEM teachers who will adopt our software through his Académie. We are really happy to know that people have started to compare Energy3D with SketchUp and are even considering using Energy3D instead of SketchUp. This might be a small change to those users who make the switch but it is a big thing to us.

On  a separate note, we just finished the initial version of the User's Guide for Energy3D. We intend this to eventually grow into a book that will be useful to teachers who must, upon the requirement of the Next Generation Science Standards, teach some engineering design in K-12 schools. Our recent experiences working with high school teachers in Massachusetts show the lack of practical engineering materials tailor-made for high school students. As a result, one of the teachers with whom we are collaborating has to use a college textbook on architectural engineering. Perhaps we can provide a book that will fill this gap -- with a student-friendly CAD program to support it.

A high school student’s design work with Energy3D

Cormac Paterson is a student at Arlington High School. We ran into him last year while conducting research in the school. He quickly mastered our Energy3D CAD software. In as short as just five class periods, he came up with three different architectural designs that appear to be very sophisticated and impressive (see the second row in the image). After that, Mr. Paterson continued his creative work with Energy3D. In his latest projects, he designed a Mars colony and a solar tree. Many of his design elements surprised us: As the developers of the CAD software, we didn't even know that it could do those things until we saw his designs!

Thanks to the National Science Foundation, we obtained a bit more funding to deepen our research on engineering design. We are extremely interested in studying Mr. Paterson's gift in architectural design: What makes him such an extraordinary designer as a high school student? Since our Energy3D software can monitor every move of the designer, we may be able to find some clues from the data generated in his design processes.

Note: We are very serious in protecting the privacy of minors. In this case, we have obtained a permission from Mr. Paterson's parent to feature him and his work.

Getting sensor data out of Energy2D

Figure 1: Copy data from Energy2D.
Since a few users asked if the simulation data in Energy2D can be exported to other applications such as Excel, I have added a feature to the app for extracting virtual sensor data as multi-column time series data. For the user's convenience, there are three different ways of getting these data:
  1. When right-clicking on a sensor, the "View Data..." from the popup menu returns the data that has been recorded by the selected sensor.
  2. When right-clicking on a spot not occupied by a sensor, the "View Data..." from the popup menu returns a tabbed pane that contains all the sensor data -- different types of sensor are organized in different tabs.
  3. When the translucent graph is open, clicking the View Data button on the graph window's control panel returns the data recorded by all the sensors of the selected type, in consistent with the current display of the data in the graph window.
Figure 2: Paste data into Excel.
Regardless of which way you use, use the "Copy Data" button at the bottom of the data window to copy the data (Figure 1) and paste it into Excel. Once you get the data into Excel, you can process and plot them in any way you want (Figure 2). This feature is very handy if you need to combine data from multiple simulations into a single graph.

Note: This feature only works for the app. For security reason, the embedded applet is not allowed to access the System Clipboard (this is understandable, because people often copy and paste important information!)

Scoring explanation-certainty items in High-Adventure Science

One of the questions unique to the High-Adventure Science project is what we call the explanation-certainty item set. These item sets consist of four separate questions:

  1. Claim
  2. Explanation
  3. Rating of certainty
  4. Certainty rationale

In the first High-Adventure Science project, we developed these items as a reliable way to assess student argumentation and developed rubrics to score the items, which I’ll explain below. (You can also look at Exploring the Unknown, our first publication in The Science Teacher. Check out our Publications tab for a list of (and link to) all of the publications generated from High-Adventure Science.)

Scoring the Claim item:

The scoring for this portion of the explanation-certainty item set is fairly straight-forward. Where there is a correct answer, the correct answer gets a point, and the incorrect answer gets zero points. Where there is no correct answer (because the problem is so nuanced and/or there is not enough information to make a definitively correct answer), we score into categories. For instance, in this question from the water module, there is no definitively correct answer:

A farmer drills a well to irrigate some nearby fields.
Could the well supply a consistent supply of water for irrigation?

No-one knows if the answer is yes or no until the wells run dry!

Scoring the Explanation item:

Explain your answer.

The scoring for this portion of the explanation-certainty item set follows the generic rubric seen below. Basically, we’re assessing whether (and to what extent) the student is able to make scientific claims.

What’s a scientific claim?

Scientific claims are backed by evidence. The more links a student is able to make between the evidence and the argument, the higher on the scale s/he scores.

Screen shot 2013-09-19 at 12.13.35 PM

It’s helpful to look at a couple of examples to really understand how this works.

Here are some “student” responses to the explanation portion following the claim question about irrigation of fields.  (Note that I made these up to be illustrative; they are not actually from students.)

  • Student A: I don’t know.
  • Student B: The well could supply irrigation easily for many years.
  • Student C: The farmer might be drilling into a confined aquifer so the well wouldn’t last forever.
  • Student D: After the water is used, it will sink back into the ground and be ready to pump up again.
  • Student E: If the well is pumping from a confined aquifer, it won’t be recharged by precipitation. That means that the well won’t last forever.
  • Student F: If the farmer had a limited amount of crops to irrigate, and the well was drilled into an unconfined aquifer so that it could be recharged by the rainfall, then the well might last forever. But if the well went into a confined aquifer, it would eventually run out.
  • Student G: If the farmer drilled into an unconfined aquifer, the well might last forever. But that depends on how much water is being pumped out vs. how much can be recharged by precipitation. If the sediments above the aquifer are very permeable, then the aquifer will recharge quickly, but if they are not super-permeable, the aquifer will take some time to recharge, so it’s possible to pump the well dry, if only temporarily. If the farmer drills into a confined aquifer, the water might last a really long time (fi the aquifer is huge), but since it can’t be recharged because the sediments above it are impermeable, it would eventually run out of water.

How would you score these responses?

The first thing to think about is what the “best answer” looks like. Some of the sample answers are pretty good. But how do you distinguish between good, pretty good, and excellent?

The answer lies in the number of ideas in the answer and whether those ideas are linked. For instance, the main ideas to consider in the “best answer” are:

  • wells pump from aquifers
  • aquifers can be confined or unconfined
  • unconfined aquifers can be recharged by precipitation
  • confined aquifers are not recharged by precipitation
  • recharge happens more quickly when the sediments overlying the aquifer are more permeable (and more slowly when sediments are less permeable)
  • the amount of water in the aquifer is a limiting constraint (you can’t pump more than exists!)

Making links between these ideas is the key to a good scientific argument. The ideas for the “best answer” vary between explanation items, but the scoring idea is the same across all High-Adventure Science explanation items.

  • Score 0: no links, no scientific claim present
  • Score 1: no links (if a claim is present)
  • Score 2: any one idea
  • Score 3: one link between ideas
  • Score 4: two or more links between ideas

So, scoring the student responses:

  • Student A: This one is easy. The student did not make any claim or provide any evidence. This response scores a 0.
  • Student B: There’s a claim, but does it contain any key ideas? No, so this scores a 1.
  • Student C: This student brings up the idea of a confined aquifer. That’s one idea, so it’s a score of 2.
  • Student D: This student recognizes that water flows in a cycle and that water sprayed on the crops will percolate down through the soil. That’s only one of the main ideas, so this response also gets a score of 2.
  • Student E: This student makes the link between recharge and unconfined aquifers. This response scores a 3.
  • Student F: This student brings in three links: unconfined aquifers are recharged; confined aquifers are not recharged; and rainfall provides recharge. This response scores a 4.
  • Student G: This student brings in all of the ideas. There is a discussion of why unconfined aquifers are recharged by precipitation while confined aquifers are not. There is a discussion about how the permeability of sediments affects the rate of recharge. There is a discussion about the size of the aquifer. This response also scores a 4.

Scoring the Certainty Rating item:

How certain are you about your claim based on your explanation?

We use this as an indication of how strongly a student is confident in his/her argument. There are no right or wrong answers here.

Scoring the Certainty Rationale item:

Explain what influenced your certainty rating.

Like the Explanation item, the scoring for this item follows a rubric. Unlike the rubric for the Explanation item, however, this follows a rubric that’s very easy to generalize across all topic areas.

Screen shot 2013-09-19 at 12.19.38 PM

Students use many different reasons for their uncertainty, but they can be broadly categorized as personal, scientific within the investigation, and scientific beyond the investigation.

Personal reasons include:

  • My teacher told me.
  • I learned it from a science show.
  • I read it in a magazine.
  • I’m not really good at this topic.  (or conversely, I’m really good at this topic.)
  • I haven’t learned this yet. (or conversely, we learned this last year.)

Scientific within the investigation reasons include evidence from within the question or specific knowledge directly related to the question.

Scientific beyond the investigation reasons include:

  • questioning the data or evidence presented in the question
  • recognizing limitations in scientific knowledge about the topic
  • recognizing the inherent uncertainty in the phenomenon (in this case, the uncertainty of knowing the type of aquifer from which the well is pulling its water)

So there you have it – a nutshell view of how we score the certainty-explanation items in High-Adventure Science.

If you want to use parts of this rubric to score your students’ responses for your own grading, that’s great! Feel free to ask questions as they come up. The scoring is not always easy!  🙂