Posts Tagged ‘Engineering design’

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.

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.

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.

Solar urban design and data mining in the classroom

June 23rd, 2013 by Charles Xie
Image usage permitted by students.
Image usage permitted by students.
In the past two weeks, seventy ninth graders in three physics classes of Arlington High School (MA) each used our Energy3D CAD software to solve the Solar Urban Design Challenge (which I blogged earlier). I observed them for three days. I didn't have experience with these students before, but according to their teacher, they were exceptionally engaged. Most students hadn't "run out of steam" even after 4-5 days of continuous work on the design project. As anyone who works in schools knows, it is hard to keep students interested in serious science projects for that long, especially near the end of a semester. These students seemed to have enjoyed this learning experience. This is a good sign that we must have done something right. I suppose the colorful 3D solar visualization provides some eye candies to keep them curious for a while.

Image usage permitted by students.
CAD tools are probably not new things in classrooms these days, at least not for Arlington High School that uses SketchUp and AutoCAD for years. What is cool about our CAD tool is that all these students' actions were recorded behind the scene -- at a frequency of every two seconds! That is to say, the computer was "watching" every move of every student. This sounds like a little concerning if you have heard in the news about a secret governmental project called the Prism that is probably "watching" me writing this blog article at this time. But rest assured that we are using this data mining technology in a good way. Our mission is not to spy on students but to figure out how to help them learn science and engineering in a more fruitful way. This is probably equally important -- if not more -- to our national security if we are to maintain our global leadership in science and technology.

Solar urban design using Energy3D: Part IV

June 1st, 2013 by Charles Xie
In Part I, II, and III, we mainly explored the possible layouts of buildings in the city block and their solar energy outputs in different seasons. In those cases, the solar radiation on a new construction is mostly affected by other new constructions and existing buildings in the neighborhood. We haven't explored the effect of the shape of a building. The shape of a building is what makes architecture matter, but it also has solar implications. In this blog post, we will explore these implications.

Figure 1: Compare solar heating of three different shapes in two seasons.
Let's start with a square-shaped tall building and make two variations. The first one is a U-shaped building and the second is a T-shaped one. In both variations, the base areas and the heights are identical to those of the original square-shaped building. Let's save these buildings into separate files and don't put them into the city block. We just want to study the solar performance of each individual building before we put them in a city.

The U-shaped building has a larger surface area than the square-shaped and the T-shaped ones (which have an identical surface area). Having a larger surface means that the building can potentially receive more solar radiation. But the two wings of the U-shaped building also obstruct sunlight. So does the U-shaped building get more or less energy? It would have been very difficult to tell without running some solar simulations, which tell us that this particular U-shaped building gets more solar energy than the square-shaped one both in the winter and in the summer.

In comparison, the T-shaped building gets the least amount of solar energy in both seasons. This is not surprising because its surface is not larger than the square-shaped one but its shape obstructs sunlight to its western part in the morning and to its eastern part in the afternoon, resulting in a reduction of solar heating.

Links:

Solar urban design using Energy3D: Part III

May 22nd, 2013 by Charles Xie
Figure 1
In Part I and II, we discussed how solar simulations in Energy3D can be used to decide where to erect a new building in a city block surrounded by existing buildings. Now, what about putting multiple buildings in the block? The optimization problem becomes more complex because students will have to deal with more variables while searching for an optimal solution.
Figure 2
Suppose students have to decide the locations of two new constructions A and B that have identical shapes. Now they have six options to layout
the two new constructions. Figure 1 shows the results of the solar simulations for all these six layouts in the winter. Placing the buildings in the northeast and northwest parts (the first in the first row of Figure 1) seems to be the best solution for receiving solar heating in the winter. This is not surprising because this layout creates large south-facing areas for both buildings that will get a lot of solar energy in the winter and there are not shadowed very much by the surrounding buildings.

Switch the season to the summer.  Figure 2 shows the results of the solar simulations for all these six layouts in July. Placing the buildings in the southeast and southwest parts (the first in the second row of Figure 2) seems to be the best solution for avoiding solar heating in the summer.

To make a trade-off between winter heating and summer cooling, it seems the southeast and southwest locations are the optimal solution: In the winter the solar heating on the two buildings is the second best (which is not much lower than the highest) and in the summer the solar heating on them is the lowest (which is much lower than the contender).

Links: