Posts Tagged ‘CAD’

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.

Accurate prediction of solar radiation using Energy3D: Part II

July 16th, 2014 by Charles Xie
About a week ago, I reported our progress in modeling worldwide solar radiation with our Energy3D software. While our calculated insolation data for a horizontal surface agreed quite well with the data provided by the National Solar Radiation Data Base, those for a south-facing vertical surface did not work out as well. I suspected that the discrepancy was partly caused by missing the reflection of short-wave radiation: not all sunlight is absorbed by the Earth. A certain portion is reflected. The ability of a material to reflect sunlight is known as albedo. For example, fresh snow can reflect up to 90% of solar energy. People who live in the northern part of the country often experience strong reflection from snow or ice in the winter.

Figure 1. Calculated and measured insolation on a south-facing surface.
In the summer, the Sun is high in the sky. A south-facing plate doesn't get as much energy as in other seasons, especially near the Equator where the Sun is just above your head (such as Honolulu as included in the figures above). However, the ambient reflection can be significant. After incorporating this component into our equations following the convention in the ASHRAE solar radiation model, the agreement between the calculated and measured results significantly improves -- you can see this big improvement by comparing Figure 1 (new algorithm) with Figure 2 (old algorithm).

Figure 2. Results without considering reflected short-wave radiation.
This degree of accuracy is critically important to supporting meaningful engineering design projects on renewable energy sources that might be conducted by students across the country. We are working to refine our computational algorithms further based on 50 years' research on solar science. This work will lend Energy3D the scientific integrity needed for rational design, be it about sustainable architecture, urban planning, or solar parks.

Revisit Part I.

Scanning radiation flux with moving sensors in Energy2D

July 13th, 2014 by Charles Xie
Figure 1: Moving sensors facing a rectangular radiator.
The heat flux sensor in Energy2D can be used to measure radiative heat flux, as well as conductive and convective heat fluxes. Radiative heat flux depends on not only the temperature of the object the sensor measures but also the angle at which it faces the object. The latter is known as the view factor.

In radiative heat transfer, a view factor between two surfaces A and B is the proportion of the radiation which leaves surface A that strikes surface B. If the two surfaces face each other directly, the view factor is greater than the case in which they do not. If the two surfaces are closer, the view factor is greater.

Figure 2: Rotating sensors inside and outside a ring radiator.
To conveniently visualize the effect of a view factor, Energy2D allows you to attach a heat flux sensor to a moving or rotating particle, with a settable linear or angular velocity. In this way, we can set up sensors to automatically "scan" the field of radiation heat flux like a radar.

Figure 1 shows a moving sensor and a rotating sensor, as well as the data they record. A third sensor is also placed to the right of an object that is being heated by the radiator. This object has an emissivity of one so it also radiates. Its radiation flux is recorded by the third sensor whose data shows a slowly increasing heat flux as the object slowly warms up.

As an interesting test case, Figure 2 shows two rotating sensors, one placed precisely at the center of a ring radiator and the other outside. The almost steady line recorded by the first sensor suggests that the view factor at the center does not change, which makes sense. The small sawtooth shape is due to the limitation of discretization in our numerical simulation.

Accurate prediction of solar radiation using Energy3D: Part I

July 8th, 2014 by Charles Xie
Solar engineering and building design rely on accurate prediction of solar radiation at any given location. This is a core functionality of our Energy3D CAD software. We are proud to announce that, through continuous improvements of our mathematical model, Energy3D is now capable of modeling solar radiation with an impressive precision.

Figure 1. Comparison of measured and calculated solar radiation on a horizontal plate at 10 US locations.
Figure 1 shows that Energy3D's calculated results of solar energy density on a horizontal plate agree remarkably well with, the National Solar Radiation Database that houses 30 years of data measured by the National Renewable Energy Laboratory of the U.S. Department of Energy -- for 10 cities across the US. One striking success is the prediction of a dip of solar radiation in June for Miami, FL (see the second image of the first row). Overall, the predicted results are slightly smaller than the measured ones. 

Note that these results are theoretical calculations, not numerical fits (such as using an artificial neural network to predict based on previous data). It is pretty amazing if you think about this: Through some complex calculations the number for each month and each city come very close to the data measured for three decades at those weather stations scattered around the country! This is the holy grail of computer simulation. This success lays a solid foundation for our Energy3D software to be scientifically and engineeringly relevant.

Figure 2. Comparison of measured and calculated solar radiation on a south-facing plate at 10 US locations.
The National Renewable Energy Laboratory also measured the solar radiation on surfaces that tilt at different angles. The predicted trends for the solar energy density on an upright south-facing plate agree reasonably well (Figure 2) with the measured data. For example, both measured and calculated data show that solar radiation on a south-facing plate peaks in the spring and fall for most northern locations and in the winter for tropical locations. It is amazing that Energy3D also correctly predicts the exception --  Anchorage in Alaska, where the solar data peak only in the spring!

Quantitatively, Energy3D seems to underestimate the solar radiation more than in the horizontal case shown in Figure 1, especially for the summer months. We suspect that this is because a vertical plate has a larger contribution from the ambient radiation and reflection than a horizontal plate (which faces the sky). We are now working towards a better model to correct this problem.

For Energy3D to serve a global audience, we have collected geographical and climate data of more than 150 domestic and foreign locations and integrated them into the software (Version 3.2). If you live in the US, you are guaranteed to find at least one location in your state.

Go to Part II.

Multiphysics simulations of inelastic collisions with Energy2D

July 4th, 2014 by Charles Xie
Figure 1. Mechano-thermal simulation of inelastic collision.
Many existing simulations of inelastic collisions show the changes of speeds and energy of the colliding objects without showing what happens to the lost energy, which is often converted into thermal energy that spreads out through heat transfer. With the new multiphysics modeling capabilities, the Energy2D software can show the complete picture of energy transfer from the mechanical form to the thermal form in a single simulation.

Figure 2. Thermal marks left by collisions.
Figure 1 shows the collisions of three identical balls (mass = 10 kg, speed = 1 m/s) with three fixed objects that have different elasticities (0, 0.5, and 1). The results show that, in the case of the completely inelastic collision, all the kinetic energy of the ball (5 J) is converted into thermal energy of the rectangular hit object (at this point, the particles in Energy2D do not hold thermal energy, but this will be changed in a future version), whereas in the case of completely elastic collision, the ball B1 does not lose any kinetic energy to the hit object. In the cases of inelastic collisions, you can see the thermal marks created by the collisions. The thermometers placed in the objects also register a rise of temperatures. This view resembles infrared images of floors taken immediately after being hit by tennis balls.

Figure 3. Collisions in Energy2D.
Energy2D supports particle collisions with all the 2D shapes that it provides: rectangles, ellipses, polygons, and blobs. Figure 2 shows the thermal marks on two blobs created by a few bouncing particles. And Figure 3 shows another simulation of collision dynamics with a lot of particles bouncing off complex shapes (boy, it took me quite a while in this July 4 weekend to hunt down most of the bugs in the collision code).

The multiphysics functionality of Energy2D is an exciting new feature as it allows more realistic modeling of natural phenomena. Even in science classrooms, realism of simulations is not just something that is nice to have. If computer simulations are to rival real experiments, it must produce not only the expected effects but also the unexpected side effects. Capable of achieving just that, a multiphysics simulation can create a deep and wide learning space just like real experiments. For engineering design, this depth and breadth are not options -- there is no open-endedness without this depth and breadth and there is no engineering without open-endedness.

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.

Towards a multiphysics Energy2D

June 14th, 2014 by Charles Xie
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.

Global pattern of insolation predicted by Energy3D

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

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!

More analytic capabilities added to Energy3D

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.