The Concord Consortium

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:

• Part I
• Part II
• Part III

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:

• Part I
• Part II
• Part IV

Figure 1

The sun is lower in the winter and higher in the summer. How does the sun path affect the solar radiation on the city block in our urban design challenge? Is solar heating different in different seasons? Let's find out using Energy3D's solar simulator. Energy3D has a nice feature that allows us to look at the 3D view exactly from the top. This kind of reduces the 3D problem to a 2D one once you complete your 3D construction and want to do some solar analysis. The 2D view is clearer and the drag-and-drop of buildings is easier.

Figure 2

First, we added a rectangular building to the city block and moved it to four different places -- northwest, northeast, southeast, and southwest -- in the city block and set the month to be January and the location to be Boston, MA (which is where we are close to). Not surprisingly, the solar radiation on the building is the lowest at the southeast location (Figure 1). This is because to the southeast of the block, there are three tall buildings that shadow the southeast part of the block --- you can see in the heat map that the southeast part is deep blue. At the southwest location, the building receives the highest solar energy. The northwest location seconds it with a slightly smaller number.

Figure 3

Next we set the month to be July and repeated the solar simulation.This time, the solar heating on the building at all locations increases (Figure 2). However, the location that receives the lowest solar heating, surprisingly, is not southeast but southwest! The location that receives the highest solar heating is northwest. The reason could be that there is a tall building next to the southwest location that provides a lot of shadow (Figure 3). This shadowing effect seems to be more significant than the shadowing effect from the three tall buildings around the southeast corner.

Figure 4

The conclusion is that the building of this particular shape receives the highest solar energy in the winter and the lowest in the summer at the southwest spot.

Now, what about the orientation of the building? Let's rotate the building 90 degrees and redo the solar analysis in January (Figure 4). The results show that the building receives higher solar energy at all locations. This is because the building has a larger south-facing side in this orientation than in the previous one. The southeast location remains the coldest spot, but the difference between southwest and northwest is less.

Links:

• Part I
• Part III 
• Part IV

Figure 1

In sustainable architecture, passive solar design refers to searching for optimal strategies to maximize solar heating on a building in the winter and minimize solar heating in the summer in order to reduce heating and cooling costs of the building. A passive solar design challenge is a typical optimization problem that requires many steps of engineering design to solve, such as testing ideas, analyzing data, considering constraints, and making trade-offs.

Figure 2

For urban design, site layout has a big impact on passive solar heating in buildings as neighboring tall buildings can block low winter sun. Energy3D’s solar simulator can compute, visualize, and analyze solar radiation in obstructed situations commonly encountered in dense urban areas.

The solar urban design project we have developed challenges students to use Energy3D to construct a square city block surrounded by a number of existing buildings of different heights, with the goals to maximize solar access for new constructions and minimize obstruction of sunlight to existing buildings. The existing buildings, which cannot be modified by students, serve as constraints for the design challenge. This design challenge is an authentic engineering problem as it requires students to consider solar radiation as it varies over seasons and apply these math and science concepts to solve open-ended problems using a supporting analytic tool. This distinguishes it from common computer drafting activities in which students draw structures whose functions cannot or will not be verified or tested.

Figure 3

Energy3D can generate solar radiation heat maps on the walls of buildings and the ground (Figures 1 and 2). These heat maps show the cumulative heat of solar radiation on a surface over a certain period (a day or a month). They are calculated by summing up the solar energy projected onto each unit area of the surface while the sun moves cyclically in its path at the given location. The total solar heating result (in kWh), summing from all the unit areas of all the walls, is shown on top of each building. This number will go up and down as students move or reshape the building. This calculated result is more accurate than shadow and shading, which only reflects instantaneous solar heating at a particular moment.

The horizontal radiation heat map can be used to identify the hot and cold areas of the empty city block. With this heat map, students can find out where the new constructions should be in order to have maximal solar heating in the winter. Once they put in a new building, they can move the building around within the construction site to experiment how much solar energy the building will gain. As an example, Figure 3 shows that a rectangular high-rise building will receive the highest amount of solar radiation in January if it is placed at the southwestern part of the square and it will receive the lowest amount of solar radiation if it is placed at the southeastern part.

Such an analytic tool provides data for students to make their design decisions, creating plenty of opportunities of inquiry in design processes.

Links:

• Part II 
• Part III 
• Part IV

We are adding some new features to our Energy3D software that will allow the user to carry out passive solar design of one or multiple buildings (or even an entire city block). These new features will calculate the distribution of solar energy density over an area such as the vertical surface of a wall of a building or the horizontal surface area of open space. The results will be visualized as color heat maps overlaid to the surface. The information in these color maps can be used to help students make decisions when they are searching for optimal passive solar designs.

These new analytic tools will be used in our Passive Solar Urban Design Challenge that requires students to design a city block with new buildings that have maximal solar heating in the winter and minimal solar heating in the summer, without severely obstructing solar access of existing buildings in the neighborhood.

These new features are integral parts of the existing heliodon simulator in Energy3D, which allows the user to adjust the sun path. The video in this blog post demonstrates this.

We are proud to release Energy3D version 2.0, available for download from our website. Energy3D is a computer-aided design and fabrication tool for making small model green buildings. This version added new energy assessment features that allow students to evaluate the energy performances of their  designs and investigate the effect of passive solar heating. Currently however, this energy assessment tool is limited to only 12 selected cities around the world.

The next release will feature powerful passive solar heating simulation that can be applied to a wide variety of settings ranging from a single family house to a dense urban area.

Energy3D runs on both Windows and Mac OS X. Java 7 is required. It may also run on Linux (some of our users actually got it to run on Linux), but it has not been thoroughly tested on Linux.

A student design

In a pilot study conducted in December 2012, high school students in an engineering class used our Energy3D CAD tool to do an urban solar design project -- they must consider the sun path in four seasons and the existing buildings in the neighborhood as the design constraints to optimize solar penetration to the new buildings and minimize obstruction of sunlight to the existing buildings.

Energy3D can log any student actions and intermediate steps, which provide extremely detailed information about student design processes. With such a high-resolution lens, we could characterize student patterns and analyze how they solve the design challenge closely. For example, the CAD log allows us to reconstruct the entire design process of each student and show it in an unprecedentedly fine-grained timeline graph. A timeline graph may show how students went through different iterative steps while shaping their designs. For instance, did they consider the interactions among the buildings they designed? Did they go back to revise a previously erected building that may be affected by a newly added one? The timeline data we have collected show that the students' designs demonstrated more iterative features as they moved on to explore and design alternatives following the initial attempts (perhaps encouraged by the gained familiarity with and confidence in the CAD tool).

A design timeline (click to enlarge)

Our analyses also suggest that there appears to be a significant gender difference in both design products and processes. The main differences are: 1) The boys tended to push the limit of the software and produced unconventional designs that looked "cool" but did not necessarily meet the design specifications; and 2) The girls spent more time carefully revising their designs than building new structures. While these findings may not be surprising to some seasoned educators, the significance is that this may be the first time this kind of gender difference was revealed or confirmed by empirical data from CAD logs. Using CAD logs may provide a fairer basis of assessing student performance based on the entire learning process rather than just looking at their final products or self reports.

Summary of the results

The implication of this study is that if we can identify patterns in student design learning and understand their cognitive meanings, we could devise a software system that can provide real-time feedback to help students learn in the future. For example, could the software prompt students to consider the design criteria more when it detects that students are ignoring them? Could the software stimulate students to think out of the box more when it detects that students are underexploring the design space?

For more information about this research project, visit: http://energy.concord.org/research.html.

A house designed using our Energy3D CAD software.

Perhaps the most important change in the Next Generation Science Standards to be released in March 2013 is the elevation of engineering design to the same level of importance as of scientific inquiry (which was enshrined as a doctrine of science education in the 1996 science standards). But how much do we know about teaching engineering design in K-12 classrooms?

A house made using our Energy3D CAD software.

Surprisingly, our knowledge about students’ learning and ideation in engineering design is dismal. The Committee on Standards for K-12 Engineering Education assembled by the National Research Council in 2010 found “very little research by cognitive scientists that could inform the development of standards for engineering education in K–12.” Most educational engineering projects lacked data collection and analysis to provide reliable evidence of learning. Many simply replicated the “engineering science” model from higher education, which focuses on learning basic science for engineering rather than learning engineering design. Little was learned from these projects about students’ acquisition of design skills and development of design thinking. In the absence of in-depth knowledge about students’ design learning, it would be difficult to teach and assess engineering design.

In response to these problems, we have proposed a research initiative that will hopefully start to fill the gap. As in any scientific research, our approach is to first establish a theory of cognitive development for engineering design and then invent a variety of experimental techniques to verify research hypotheses based on the theory. This blog post introduces these ideas.

In order to study engineering design on a rigorous basis, we need a system that can automatically monitor student workflows to provide us all the fine-grain data we need to understand how they think and learn when they become expert designers from novice designers. This means we have no choice but to move the entire engineering design process onto the computer -- to be more exact, into computer-aided design (CAD) systems -- so that we can keep track of students’ workflows and extract information for inferring their learning. While some educators may be uncomfortable with the virtualization of engineering design, this actually complies with contemporary engineering practices that ubiquitously rely on CAD tools. If we have a CAD system, we can add some data mining mechanisms to turn it into a powerful experimental system for investigating student learning. Fortunately, we have created our own CAD software, Energy3D, from scratch (see the above images about it). So we can do anything we want with it. Since all the CAD tools are similar, the research results should be generalizable.

A cognitive theory of engineering design.

Next we need a cognitive theory of engineering design. Engineering design is interdisciplinary, dynamic, and complicated. It requires students to apply STEM knowledge to solve open-ended problems with a given set of criteria and constraints. It is such a complex process that I am almost certain that any cognitive theory will not be perfect. But without a cognitive theory our research would be aimless. So we must invent one.

Our cognitive theory assumes that engineering design is a process of “knitting” science and engineering. Inquiry and design are at the hearts of science and engineering practices. In an engineering project, both types of practices are needed. All engineering systems are tested during the development phase. A substantial part of engineering is to find problems through tests in order to build robust products. The diagnosis of a problem is, as a matter of fact, a process of scientific inquiry into an engineered system. The results of this inquiry process provide explanations of the problem, as well as feedback to revise the design and improve the system. The modified system with new designs is then put through further tests. Testing a new design can lead to more questions worth investigating, starting a new cycle of inquiry. This process of interwoven inquiry and design repeats itself until the system is determined to be a mature product. 

These elements in our cognitive theory all sound logical and necessary. Now the question is: If we agree on this theory, how are we going to make it happen in the classroom and how are we going to measure its degree of success? Formative assessment seems to be the key. So the next thing we need to invent is a method of formative assessment. But what should we assess in order not to miss the entire picture of learning? This requires us to develop a deep understanding of the fabric of engineering design.

A time series model of design assessment.

Engineering design is a complex process that involves multiple types of science and engineering tasks and subprocesses that occur iteratively. Along with the properties and attributes of the designed artifacts that can be calculated, the order, frequency, and duration learners handle the tasks provide invaluable insights into the fabric of engineering design. These data can be monitored and collected as time series. Formative assessment can then be viewed as the analysis of a set of time series, each representing an aspect of learning or performance. In other words, each time series logs a “fiber” of engineering design.

At first glance, the time series data may look stochastic, just like the Dow Jones index. But buried under the noisy data are students’ behavioral and cognitive patterns. Time series analysis, which has been widely used in signal processing and pattern recognition, will provide us the analytic power to detect learner behaviors from the seemingly random data and then generate adaptive feedback to steer learning to less arbitrary, more productive paths. For example, spectral or wavelet analysis can be used to calculate the frequency of using a design or test tool. Auto-correlation analysis can be used to find repeating patterns in a subprocess. Cross-correlation analysis can be used to examine if an activity or intervention in one subprocess has resulted in changes in another. Cross-correlation provides a potentially useful tool for tracking a designer’s activity with regard to knowledge integration and system thinking.

In the next six months, we will undertake this ambitious research project and post our findings in this blog as we move forward. Stay tuned!

Design a city block with Energy3D.

We were in a school these two weeks doing a project that aims to understand how students learn engineering design. This has been a difficult research topic as engineering design is an extremely complicated cognitive process that involves the application of science and mathematics -- another two sets of complicated subjects themselves.


Two types of problems are commonly encountered in the classroom. The first type is related to using a "cookbook" approach that confines students to step-by-step procedures to complete a "design" project. I added double quotes because this kind of project often leads to identical or similar products from students, violating the first principle of design that mandates alternatives and varieties. However, if we make the design project completely open-ended, we will run into the second type of problem: The arbitrariness and caprice in student designs often make it difficult for teachers and researchers to assess student thinking and learning reliably. As much as we want students to be creative and open-minded, we also want to ensure that they learn what is intended and we must provide an objective way to evaluate their learning outcomes.


To tackle these issues, we are taking a computer science-based approach. Computer-aided design (CAD) tools offer an opportunity for us to move the entire process of engineering design to the computer (this is what CAD tools are designed for in the first place for industry folks). What we need to do in our research is to add a few more things to support data mining.

A sample design of the city block.

This blog post reports a timeline tool that we have developed to measure student activity levels while engaged in using a CAD tool (our Energy3D CAD software in this case) to solve a design challenge. This timeline tool is basically a logger that records the number of the learner's design actions at a given frequency (say, 2-4 times a minute) during a design session. These design actions are defined to be the "atomic" actions stored in the Undo Manager of the CAD tool we are using. The timeline approximately describes the user's frequency of construction actions with the CAD tool. As the human-computer interaction is ultimately driven by the brain, this kind of timeline data could be regarded as a reflection of the user's "brain wave."

There are four things that characterize such a timeline graph:

A sample timeline graph.

• The height of a spike measures the action intensity at that moment, i.e., how many actions the user has taken since the last recording;
• The density of spikes measures the continuity and persistence of actions over a time period;
• A gap indicates an off-task time window: A short idling window may be an effect of instruction or discussion;
• The trend of height and density may be related to loss of interest or improvement of proficiency in the CAD tool: If the intensity (the combination of height and density of spikes) drops consistently over time, the student's interest may be fading away; if the intensity increases consistently over time, the student might be improving on using the design tool to explore design options.

Timeline graphs from six students.

Of course, this kind of timeline data is not perfect. It certainly has many limitations in measuring learning. We are still in the process of analyzing these timeline data and juxtaposing them with other artifacts we have gathered from the students to provide a more comprehensive picture of design learning. But the timeline analysis represents a rudimentary step towards a more rigorous methodology for performance assessment of engineering design.

The above six "brain wave" graphs were collected from six students in a 90-minute class period. Hopefully, these data will lead to a way to identify novice designers' behaviors and patterns when they are solving a design challenge.

Energy3D works just like Google's SketchUp: You can create a 3D structure by drag-and-drop -- no number crunching is required. But unlike SketchUp, it is tailor-made for building design, evaluation, and fabrication to support engineering design learning in K-12 schools. One of its great features is the "print-out" functionality, which allows students to print out the houses they designed using a regular printer and then cut out the 2D pieces for 3D assembly (see the second image in this blog post).

You can imagine how Energy3D may work for your students by looking at the houses designed by a class of high school students in the third image of this blog post. The tool is very easy to use and works well even for young kids. So if you are teaching in an elementary school, give it a try and tell us how it can be improved for younger students.


The development of Energy3D has been funded by the National Science Foundation under the Engineering Energy Efficiency Project. Dr. Saeid Nourian, a computer scientist with a Ph. D. from the University of Ottawa, has been the primary developer since joining the project in 2010. The software is based on the open-source scene graph game engine, Ardor3D, which requires Java to be installed.