Archive for the ‘Main Blog’ Category

Simulating cool roofs with Energy3D

August 20th, 2014 by Charles Xie
Fig. 1: Compare solar energy absorption of different colors.
Cool roofs represent a simple solution that can save significant air-conditioning cost and help mitigate the urban heat island effect, especially in hot climates. Nobel Prize winner and former Secretary of Energy, Steven Chu, is a strong advocate of cool roofs.

With Version 4.0 of Energy3D, you can model cool roofs and evaluate how much energy you can save by switching from a dark-colored roof to a light-colored roof. All you need to do is to set the colors of your building blocks. Energy3D will automatically assign an albedo value for each color.

Fig. 2: Compare dark and white roofs.
Figure 1 shows five blocks in different gray colors (upper) and their thermal view (lower). In this thermal view, blue represents low energy absorption, red represents high energy absorption, and the colors in-between represents the energy absorption at the level in-between.

Now let's compare the thermal views of a black roof and a white roof of a cape code house, as shown in Figure 2. To produce Figure 2, the date was set to July 1st, the hottest time of the year in northern hemisphere, and the location was set to Boston.

How much energy can we save if we switch from a perfectly black roof (100% absorption) to a perfectly white roof (0% absorption)? We can run the Annual Energy Analysis Tool of Energy3D to figure this out in a matter of seconds. The results are shown in Figure 3. Overall, the total yearly energy cost is cut from 6876 kWh to 6217 kWh for this small cape code house, about 10% of saving. Figure 3 shows that the majority of saving comes from the reduction of AC cost.

Fig. 3: Compare heating and AC costs (blue is white roof).
Of course, this result depends on other factors such as the U-value and thermal mass of the roof. The better the roof is insulated, the less its color impacts the energy cost. With Energy3D, students can easily explore these design variables.

This new feature, along with others such as the heat flux visualization that we have introduced earlier, represents the increased capacity of Energy3D for performing function design using scientific simulations.

A 16-year-old’s designs with Energy3D

August 13th, 2014 by Charles Xie
This post needs no explanation. The images say it all.

All these beautiful structures were designed from scratch (NOT imported from other sources) by Cormac Paterson using our Energy3D CAD software.

He is only 16 years old. (We have his parents' permission to reveal his name and his work.)

Using fans to create fluid flows in Energy2D

August 10th, 2014 by Charles Xie
Fig. 1: Swirling flows form between two opposite fans.
A new type of object, "fan", has been added to Energy2D to create and control fluid flows. This fan replaces the original implementation of fan that assigns a velocity to a solid part (which doesn't allow the fluid to flow through). For the CFD folks who are reading this post, this is equivalent to an internal velocity boundary.

To add a fan to the scene, use the Insert Menu to drop a fan to the last clicked location. You can then drag it anywhere and resize it any way. By default, the velocity of a fan is zero. You will need to set its velocity in the popup window that can be opened using the right-click popup menu. Currently, however, rotation has not been implemented, so a fan can only blow in four directions: left, right, up, or down -- the direction depends on the aspect ratio of the fan's shape and the value of the velocity.

Fig. 2: Eddy formation in a hole.
With this new feature, we can create a directional flow in Energy2D to simulate things such as a river or wind field. Then we can easily simulate various kinds of eddy flow and visualize them using the streamline feature of Energy2D.

For example, Figure 1 shows the continuous formation of swirling flows between two fans that blow wind in the opposite direction. If you move the fans further apart, you will find that the swirling pattern will not form. Could the mechanism shown in this simulation be related to the formation of certain types of twisters?


Fig. 3: Eddy formation behind a fin.
Figures 2 and 3 show the formation of an eddy in a hole and behind an obstacle, respectively. These eddies are common in fast-flowing rivers. Experienced fishermen know there is a higher chance to find fish in these eddies.

Accurate prediction of solar radiation using Energy3D: Part III

August 6th, 2014 by Charles Xie
Predicted and measured average daily insolation for 80 cities.
In Parts I and II, we have documented our progress on solar radiation modeling with our Energy3D CAD software. In the past few weeks, our summer interns Siobhan Bailey from Rensselaer Polytechnic Institute and Shiyan Jiang from University of Miami, and I have collected data for 167 worldwide locations. We analyzed 100 US locations among them and compared the insolation data calculated by Energy3D for a horizontal surface and a south-face vertical surface with 30 years of data collected by the US Department of Energy. The results show that, on average, the calculated mean daily insolation is within ±14% of error range compared with the measured results for a horizontal surface and ±10% of error range compared with the measure results for a south-facing vertical surface, respectively. The calculation of the average accuracy is based on both temporal data of 12 months over a year and spatial data of 100 locations in the US.

With this crystal ball in the hand to predict solar radiation anywhere anytime with a reasonable accuracy, Energy3D can be used by professional engineers for real-world applications related to solar energy, such as passive solar architecture, urban planning, solar park optimization, solar thermal power plants, and so on. Stay tuned for our future reports of those applications.

Go to Part I and Part II.

From conceptual design to detailed design with Energy3D

August 1st, 2014 by Charles Xie
Figure 1: Empire State Building
An important objective of our Energy3D software is to explore how to create CAD software that support students to practice the full cycle of engineering design from conceptual design to detailed design in a single piece of software. We believe that interactive 3D visualizations and simulations provided by CAD tools are cognitively important for K-12 students who have little prior knowledge about the subject of design or the process of design to develop some sense of them -- through practice. Instantaneous visualizations of the results of their actions within the CAD software can give students some concrete clues to develop, share, and refine their ideas directly within a visual design space.

Although there have been some cautions about the use of CAD software in design education, my take is that, in the very early stage of grassroots design education, the problem is not that students are handicapped by a design tool; the problem is that they lack ideas to start with or skills to put their ideas into actions and should be aided by an intelligent design tool (in addition to a teacher, of course). A good CAD tool will be very instructive in this stage. Only after students rise to a certain expert level will the limitations of the CAD software begin to emerge. Often in the K-12 settings, the time constraint does not allow the majority of students to reach that level through conventional instruction, however. Hence, it is likely that the positive effects of using CAD software in K-12 engineering education will outweigh the negative effects, letting alone that students will learn important computer design and modeling skills that will be extremely useful to their future STEM careers.
Figure 2: Freedom Tower

But not all CAD software were created equal. Many CAD software have been developed for professional engineers and are not appropriate for K-12 applications, even though many software vendors have managed to enter the K-12 market in recent years. Given the rise of engineering in K-12 schools, it is probably the right time to rethink how to develop a CAD platform that supports design, learning, and assessment from the ground up.

Our Energy3D CAD software has provided us a powerful platform to ponder about these questions. From the beginning of this project back in 2010, we had been envisioning a CAD platform that integrates conceptual design, detailed design, collaborative design, numerical analysis, designer modeling, machine learning, and digital fabrication. After four years' continuous work, the software can now do not only conceptual design like a sketch tool but also detailed design like a production CAD program (look at the details in Figure 3). In terms of education, this means that it has a very short learning curve that allows all students to translate their ideas into computer models in a short amount of time and, meanwhile, a very deep design space that allows some of the willing students to advance to an expert level. With these capacities, we are now conducting leading-edge data mining research to investigate how to facilitate the transition. The research will eventually translate into novel software features of the CAD program.
Figure 3: Kendall Square

Cormac Paterson, a brilliant high school student from Arlington, MA, has demonstrated these possibilities. He has created many designs with Energy3D that are showcased in our model repository, including all the designs shown in the figures of this post.

On the instructional sensitivity of computer-aided design logs

July 20th, 2014 by Charles Xie
Figure 1: Hypothetical student responses to an intervention.
In the fourth issue this year, the International Journal of Engineering Education published our 19-page-long paper on the instructional sensitivity of computer-aided design (CAD) logs. This study was based on our Energy3D software, which supports students to learn science and engineering concepts and skills through creating sustainable buildings using a variety of built-in design and analysis tools related to Earth science, heat transfer, and solar energy. This paper proposed an innovative approach of using response functions -- a concept borrowed from electrical engineering -- to measure instructional sensitivity from data logs (Figure 1).

Many researchers are interested in studying what students learn through complex engineering design projects. CAD logs provide fine-grained empirical data of student activities for assessing learning in engineering design projects. However, the instructional sensitivity of CAD logs, which describes how students respond to interventions with CAD actions, has never been examined, to the best of our knowledge.
Figure 2. An indicator of statistical reliability.

For the logs to be used as reliable data sources for assessments, they must be instructionally sensitive. Our paper reports the results of our systematic research on this important topic. To guide the research, we first propose a theoretical framework for computer-based assessments based on signal processing. This framework views assessments as detecting signals from the noisy background often present in large temporal learner datasets due to many uncontrollable factors and events in learning processes. To measure instructional sensitivity, we analyzed nearly 900 megabytes of process data logged by Energy3D as collections of time series. These time-varying data were gathered from 65 high school students who solved a solar urban design challenge using Energy3D in seven class periods, with an intervention occurred in the middle of their design projects.

Our analyses of these data show that the occurrence of the design actions unrelated to the intervention were not affected by it, whereas the occurrence of the design actions that the intervention targeted reveals a continuum of reactions ranging from no response to strong response (Figure 2). From the temporal patterns of these student responses, persistent effect and temporary effect (with different decay rates) were identified. Students’ electronic notes taken during the design processes were used to validate their learning trajectories. These results show that an intervention occurring outside a CAD tool can leave a detectable trace in the CAD logs, suggesting that the logs can be used to quantitatively determine how effective an intervention has been for each individual student during an engineering design project.

Simulating PTC and NTC heating elements with Energy2D

June 23rd, 2014 by Charles Xie
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

June 18th, 2014 by Charles Xie
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.

Psychology and Climate Change

May 28th, 2014 by Andy Zucker

The Nobel Prize winner Doris Lessing, author of more than 50 books, had a rare talent for writing in different styles about a variety of people, from adolescent boys, to lonely old women, to kings and queens living on another planet. She had a brilliant novelist’s intuitive understanding of other people’s minds.

In her slender 1987 non-fiction book, Prisons We Choose to Live Inside, Lessing wrote about growth in scientists’ understanding of psychology, and the need to apply that knowledge to public affairs. Ever since, more and more high-quality popular books on psychology have been published, including Thinking Fast and Slow, The Righteous Mind, The Black Swan, and many others. Yet in the case of climate change, Lessing’s plea that we pay closer attention to psychology has grown more imperative. Humanity cannot address species-threatening climate problems without better understanding the ways we think.

As one significant example, earlier this month the New York Times reported that the West Antarctic ice sheet is not only melting quickly, but, according to two scientific papers, the melting appears to be irreversible. Over time – fortunately, the time scale is likely to be centuries – sea level will probably rise ten feet or more due to the melting of this single ice sheet. Simultaneously, other climate-related changes will also contribute to rising sea levels. Sea level is about to increase three or four feet in this century alone.

This once-in-a-geological-epoch news item about Antarctic melting ought to have caught the attention of people everywhere. Earth’s population will be in for a rougher ride than expected – and climate scientists have been predicting a rough ride for years. Yet the news of irreversible Antarctic ice melting probably passed unnoticed by a majority of Americans.

At almost the same time, a likely contender for the Presidential nomination of a major political party said that he does not believe human activity is causing climate change, a statement that may seem less shocking when one realizes that fewer than half of Americans think human beings are the primary cause of climate change. What strategies – other than waiting for more climate disasters to strike and hoping that politicians will accept facts – will work best to persuade the public and its representatives that action is needed now? Are there perhaps key groups, such as religious leaders, who are not the typical audience for scientists’ press releases, who might accelerate public understanding and acceptance of climate change?

It is easy to call climate change skeptics ignorant, or worse. But name-calling is seldom the best strategy for changing people’s minds and, what is more, modern psychology has demonstrated that virtually everyone’s thinking is badly flawed in certain situations. To take one example, airplane pilots must be taught to trust their instruments instead of their senses when they cannot see the horizon, and yet every year some crash because they did not learn this lesson. To take another example, some scientists’ first reactions in 1980 to scientific papers about the extinction of the dinosaurs by a giant meteor striking earth – calling the authors “arrogant,” “ignorant,” and “all wrong” – demonstrated prejudices rather than open minds.

Developing a better understanding of the science behind climate change is essential. There is also some work under way to develop better communications strategies, and those efforts are laudable. But mankind is not likely to change its behavior rapidly enough to prevent disaster if we do not learn more about how people think about global warming and apply that knowledge to quicken public action.

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

May 4th, 2014 by Charles Xie
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.