Posts Tagged ‘Computer-aided design’

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

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.

Building performance analyses in Energy3D

April 6th, 2014 by Charles Xie
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!

March 28th, 2014 by Charles Xie
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

February 24th, 2014 by Charles Xie
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

February 22nd, 2014 by Charles Xie
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.

The first paper on learning analytics for assessing engineering design?

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.

Computational process analytics: Compute-intensive educational research and assessment

October 5th, 2013 by Charles Xie
Trajectories of building movement (good)
Computational process analytics (CPA) differs from traditional research and assessment methods in that it is not only data-intensive, but also compute-intensive. A unique feature of CPA is that it automatically analyzes the performance of student artifacts (including all the intermediate products) using the same set of science-based computational engines that students used to solve problems. The computational engines encompass every single details in the artifacts and their complex interactions that are highly relevant to the nature of the problems students solved. They also recreate the scenarios and contexts of student learning (e.g., the calculated results in such a post-processing analysis are exactly the same as those presented as feedback to students while they were solving the problems). As such, the computational engines provide holistic, high-fidelity assessments of students' work that no human evaluator can ever beat -- while no one can track numerous variables students might have created in long and deep learning processes in a short evaluation time, a computer program can easily do the job. Utilizing disciplinarily intelligent computational engines to do performance assessment was a major breakthrough in CPA as this approach really has the potential to revolutionize computer-based assessment.

No building movement (bad)
To give an example, this weekend I am busy running all the analysis jobs on my computer to process 1 GB of data logged by our Energy3D CAD software. I am trying to reconstruct and visualize the learning and design trajectories of all the students, projected onto many
different axes and planes of the state space. To do that, an estimate of 30-40 hours of CPU time on my Lenovo X230 tablet, which is a pretty fast machine, is needed. Each step loads up a sequence of artifacts, runs a solar simulation for each artifact, and analyzes the results (since I have automated the entire process, this is actually not as bad as it sounds). Our assumption is that the time evolution of the performance of these artifacts would approximately reflect the time evolution of the performance of their designers. We should be able to tell how well a student was learning by examining if the performance of her artifacts shows a systematic trend of improvement, or is just random. This is way better than the performance assessment based on just looking at students' final products.

After all the intermediate performance data were retrieved through post-processing the artifacts, we can then analyze them using our Process Analyzer -- a visual mining tool being developed to show the analysis results in various visualizations (it is our hope that the Process Analyzer will eventually become a powerful assessment assistant to teachers as it would free teachers from having to deal with an enormous amount of raw data or complicated data mining algorithms). For example, the two images in this post show that one student went through a lot of optimization in her design and the other did not (there is no trajectory in the second image).

National Science Foundation funds research that puts engineering design processes under a big data "microscope"

September 20th, 2013 by Charles Xie
The National Science Foundation has awarded us $1.5 million to advance big data research on engineering design. In collaboration with Professors Şenay Purzer and Robin Adams at Purdue University, we will conduct a large-scale study involving over 3,000 students in Indiana and Massachusetts in the next five years.

This research will be based on our Energy3D CAD software that can automatically collect large process data behind the scenes while students are working on their designs. Fine-grained CAD logs possess all four characteristics of big data defined by IBM:
  1. High volume: Students can generate a large amount of process data in a complex open-ended engineering design project that involves many building blocks and variables; 
  2. High velocity: The data can be collected, processed, and visualized in real time to provide students and teachers with rapid feedback; 
  3. High variety: The data encompass any type of information provided by a rich CAD system such as all learner actions, events, components, properties, parameters, simulation data, and analysis results; 
  4. High veracity: The data must be accurate and comprehensive to ensure fair and trustworthy assessments of student performance.
These big data provide a powerful "microscope" that can reveal direct, measurable evidence of learning with extremely high resolution and at a statistically significant scale. Automation will make this research approach highly cost-effective and scalable. Automatic process analytics will also pave the road for building adaptive and predictive software systems for teaching and learning engineering design. Such systems, if successful, could become useful assistants to K-12 science teachers.

Why is big data needed in educational research and assessment? Because we all want students to learn more deeply and deep learning generates big data.

In the context of K-12 science education, engineering design is a complex cognitive process in which students learn and apply science concepts to solve open-ended problems with constraints to meet specified criteria. The complexity, open-endedness, and length of an engineering design process often create a large quantity of learner data that makes learning difficult to discern using traditional assessment methods. Engineering design assessment thus requires big data analytics that can track and analyze student learning trajectories over a significant period of time.
Deep learning generates big data.

This differs from research that does not require sophisticated computation to understand the data. For example, in typical pre/post-tests using multiple-choice assessment, the selection data of individual students are directly used as performance indices -- there is basically no depth in these self-evident data. I call this kind of data usage "data picking" -- analyzing them is just like picking up apples already fallen to the ground (as opposed to data mining that requires some computational efforts).

Process data, on the other hand, contain a lot of details that may be opaque to researchers at first glance. In the raw form, they often appear to be stochastic. But any seasoned teacher can tell you that they are able to judge learning by carefully watching how students solve problems. So here is the challenge: How can computer-based assessment accomplish what experienced teachers (human intelligence plus disciplinary knowledge plus some patience) can do based on observation data? This is the thesis of computational process analytics, an emerging subject that we are spearheading to transform educational research and assessment using computation. Thanks to NSF, we are now able to advance this subject.

Measuring the effects of an intervention using computational process analytics

September 15th, 2013 by Charles Xie
"At its core, scientific inquiry is the same in all fields. Scientific research, whether in education, physics, anthropology, molecular biology, or economics, is a continual process of rigorous reasoning supported by a dynamic interplay among methods, theories, and findings. It builds understanding in the form of models or theories that can be tested."  —— Scientific Research in Education, National Research Council, 2002
Actions caused by the intervention
Computational process analytics (CPA) is a research method that we are developing in the spirit of the above quote from the National Research Council report. It is a whole class of data mining methods for quantitatively studying the learning dynamics in complex scientific inquiry or engineering design projects that are digitally implemented. CPA views performance assessment as detecting signals from the noisy background often present in large learner datasets due to many uncontrollable and unpredictable factors in classrooms. It borrows many computational techniques from engineering fields such as signal processing and pattern recognition. Some of these analytics can be considered as the computational counterparts of traditional assessment methods based on student articulation, classroom observation, or video analysis.

Actions unaffected by the intervention
Computational process analytics has wide applications in education assessments. High-quality assessments of deep learning hold a critical key to improving learning and teaching. Their strategic importance has been highlighted in President Obama’s remarks in March 2009: “I am calling on our nation’s Governors and state education chiefs to develop standards and assessments that don’t simply measure whether students can fill in a bubble on a test, but whether they possess 21st century skills like problem-solving and critical thinking, entrepreneurship, and creativity.” However, the kinds of assessments the President wished for often require careful human scoring that is far more expensive to administer than multiple-choice tests. Computer-based assessments, which rely on the learning software to automatically collect and sift learner data through unobtrusive logging, are viewed as a promising solution to assessing increasingly prevalent digital learning.

While there have been a lot of work on computer-based assessments for STEM education, one foundational question has rarely been explored: How sensitive can the logged learner data be to instructions?

Actions caused by the intervention.
According to the assessment guru Popham, there are two main categories of evidence for determining the instructional sensitivity of an assessment tool: judgmental evidence and empirical evidence. Computer logs provide empirical evidence based on user data recording—the logs themselves provide empirical data for assessment and their differentials before and after instructions provide empirical data for evaluating the instructional sensitivity. Like any other assessment tools, computer logs must be instructionally sensitive if they are to provide reliable data sources for gauging student learning under intervention. 


Actions unaffected by the intervention.
Earlier studies have used CAD logs to capture the designer’s operational knowledge and reasoning processes. Those studies were not designed to understand the learning dynamics occurring within a CAD system and, therefore, did not need to assess students’ acquisition and application of knowledge and skills through CAD activities. Different from them, we are studying the instructional sensitivity of CAD logs, which describes how students react to interventions with CAD actions. Although interventions can be either carried out by human (such as teacher instruction or group discussion) or generated by the computer (such as adaptive feedback or intelligent tutoring), we have focused on human interventions in this phase of our research. Studying the instructional sensitivity to human interventions will enlighten the development of effective computer-generated interventions for teaching engineering design in the future (which is another reason, besides cost effectiveness, why research on automatic assessment using learning software logs is so promising).

The study of instructional effects on design behavior and performance is particularly important, viewing from the perspective of teaching science through engineering design, a practice now mandated by the newly established Next Generation Science Standards of the United States. A problem commonly observed in K-12 engineering projects, however, is that students often reduce engineering design challenges to construction or craft activities that may not truly involve the application of science. This suggests that other driving forces acting
Distribution of intervention effect across 65 students.
on learners, such as hunches and desires for how the design artifacts should look, may overwhelm the effects of instructions on how to use science in design work. Hence, the research on the sensitivity of design behavior to science instruction requires careful analyses using innovative data analytics such as CPA to detect the changes, however slight they might be. The insights obtained from studying this instructional sensitivity may result in the actionable knowledge for developing effective instructions that can reproduce or amplify those changes.

Our preliminary CPA results have shown that CAD logs created using our Energy3D CAD tool are instructionally sensitive. The first four figures embedded in this post show two pairs of opposite cases with one type of action sensitive to an instruction that occurred outside the CAD tool and the other not. This is because the instruction was related to one type of action and had nothing to do with the other type. The last figure shows that the distribution of instructional sensitivity across 65 students. In this figure, the largest number means higher instructional sensitivity. A number close to one means that the instruction has no effect. From the graph, you can see that the three types of actions that are not related to the instruction fluctuate around one whereas the fourth type of action is strongly sensitive to the instruction.

These results demonstrate that software logs can not only record what students do with the software but also capture the effects of what happen outside the software.

Fair asessment for engineering design?

July 31st, 2013 by Charles Xie
The student's design #1
In our June study on engineering design in a high school, one student's designs caught my eye. The design challenge required students to use Energy3D to design a cluster of buildings in a city block that takes solar radiation into consideration, but this particular student came up with two neat designs.

The student's design #2
The student didn't pay much attention to the solar design part, but both designs are, I would say, hmm, beautiful. I have to admit that I am not an architect and I am judging this mostly based on my appreciation of the mathematical beauty (see Design #1) expressed in these designs. But even so, I feel that this is something worth my writing, because -- considering that the student absolutely did not know anything about Energy3D before -- it is amazing to see that how quickly he mastered the tool and came up with pretty sophisticated designs that look pleasant to my picky eyes. Where did his talent come from? I wish I had a chance to ask him.

And then the interesting story is that when I showed these designs to a colleague, she actually had a different opinion about them (compared with other designs that I think are not great). This reflects how subjective and unreliable performance assessment based on product analysis could sometimes become. While I cannot assert that my assessment is more justified, I can imagine how much efforts and thoughts this student put into these extremely well-conceived and polished designs (look how perfectly symmetric they are). This cannot be possibly the results of some random actions. A negative assessment might not do justice to this student's designs.

Which is why I had to invent the process analytics, an assessment technique that aims to provide more comprehensive, more trustworthy evaluation of students' entire design processes, not just on the final looks of the products and the evaluator's personal taste.