Tag Archives: Energy3D

Twelve Energy3D designs by Cormac Paterson

Cormac Paterson, a 17-years old student from Arlington High School in Massachusetts, has created yet another set of beautiful architectural designs using our Energy3D CAD software. The variety of his designs can be used to gauge the versatility of the software. His work is helping us push the boundary of the software and imagine what may be possible with the system.

This is the second year Cormac has worked with us as a summer intern. We are constantly impressed by his perseverance in working with the limitations of the software and around problems, as well as his ingenuity in coming up with new solutions and ideas. Working with Cormac has inspired us on how to improve our software so that it can support more students to do this kind of creative design. Our objective in the long run is to develop our software into a CAD system that is appropriate for children and yet capable of supporting authentic engineering design. Cormac's work might be an encouraging sign that we may actually be very close to realizing this goal.

Cormac also designed a building surrounded by solar trees. Solar tree is a concept that blends art and solar energy technology in a sculptural expression. An image of this post shows the result of the solar energy gains of these "trees" using the improved computational engine for solar simulation in Energy3D. 

Seeing student learning with visual analytics

Technology allows us to record almost everything happening in the classroom. The fact that students' interactions with learning environments can be logged in every detail raises the interesting question about whether or not there is any significant meaning and value in those data and how we can make use of them to help students and teachers, as pointed out in a report sponsored by the U.S. Department of Education:
New technologies thus bring the potential of transforming education from a data-poor to a data-rich enterprise. Yet while an abundance of data is an advantage, it is not a solution. Data do not interpret themselves and are often confusing — but data can provide evidence for making sound decisions when thoughtfully analyzed.” — Expanding Evidence Approaches for Learning in a Digital World, Office of Educational Technology, U.S. Department of Education, 2013
A radar chart of design space exploration.
A histogram of action intensity.
Here we are not talking about just analyzing students' answers to some multiple-choice questions, or their scores in quizzes and tests, or their frequencies of logging into a learning management system. We are talking about something much more fundamental, something that runs deep in cognition and learning, such as how students conduct a scientific experiment, solve a problem, or design a product. As learning goes deeper in those directions, data produced by students grows bigger. It is by no means an easy task to analyze large volumes of learner data, which contain a lot of noisy elements that cast uncertainty to assessment. The validity of an assessment inference rests on  the strength of evidence. Evidence construction often relies on the search for relations, patterns, and trends in student data.With a lot of data, this mandates some sophisticated computation similar to cognitive computing.

Data gathered from highly open-ended inquiry and design activities, key to authentic science and engineering practices that we want students to learn, are often intensive and “messy.” Without analytic tools that can discern systematic learning from random walk, what is provided to researchers and teachers is nothing but a DRIP (“data rich, information poor”) problem.

A scatter plot of action timeline.
Recognizing the difficulty in analyzing the sheer volume of messy student data, we turned to visual analytics, a whole category of techniques extensively used in cutting-edge business intelligence systems such as software developed by SAS, IBM, and others. We see interactive, visual process analytics key to accelerating the analysis procedures so that researchers can adjust mining rules easily, view results rapidly, and identify patterns clearly. This kind of visual analytics optimally combines the computational power of the computer, the graphical user interface of the software, and the pattern recognition power of the brain to support complex data analyses in data-intensive educational research.

A digraph of action transition.
So far, I have written four interactive graphs and charts that can be used to study four different aspects of the design action data that we collected from our Energy3D CAD software. Recording several weeks of student work on complex engineering design challenges, these datasets are high-dimensional, meaning that it is improper to treat them from a single point of view. For each question we are interested in getting answers from student data, we usually need a different representation to capture the outstanding features specific to the question. In many cases, multiple representations are needed to address a question.

In the long run, our objective is to add as many graphic representations as possible as we move along in answering more and more research questions based on our datasets. Given time, this growing library of visual analytics would develop sufficient power to the point that it may also become useful for teachers to monitor their students' work and thereby conduct formative assessment. To guarantee that our visual analytics runs on all devices, this library is written in JavaScript/HTML/CSS. A number of touch gestures are also supported for users to use the library on a multi-touch screen. A neat feature of this library is that multiple graphs and charts can be grouped together so that when you are interacting with one of them, the linked ones also change at the same time. As the datasets are temporal in nature, you can also animate these graphs to reconstruct and track exactly what students do throughout.

The National Science Foundation funds SmartCAD—an intelligent learning system for engineering design

We are pleased to announce that the National Science Foundation has awarded the Concord Consortium, Purdue University, and the University of Virginia a $3 million, four-year collaborative project to conduct research and development on SmartCAD, an intelligent learning system that informs engineering design of students with automatic feedback generated using computational analysis of their work.

Engineering design is one of the most complex learning processes because it builds on top of multiple layers of inquiry, involves creating products that meet multiple criteria and constraints, and requires the orchestration of mathematical thinking, scientific reasoning, systems thinking, and sometimes, computational thinking. Teaching and learning engineering design becomes important as it is now officially part of the Next Generation Science Standards in the United States. These new standards mandate every student to learn and practice engineering design in every science subject at every level of K-12 education.
Figure 1

In typical engineering projects, students are challenged to construct an artifact that performs specified functions under constraints. What makes engineering design different from other design practices such as art design is that engineering design must be guided by scientific principles and the end products must operate predictably based on science. A common problem observed in students' engineering design activities is that their design work is insufficiently informed by science, resulting in the reduction of engineering design to drawing or crafting. To circumvent this problem, engineering design curricula often encourage students to learn or review the related science concepts and practices before they try to put the design elements together to construct a product. After students create a prototype, they then test and evaluate it using the governing scientific principles, which, in turn, gives them a chance to deepen their understanding of the scientific principles. This common approach of learning is illustrated in the upper image of Figure 1.

There is a problem in the common approach, however. Exploring the form-function relationship is a critical inquiry step to understanding the underlying science. To determine whether a change of form can result in a desired function, students have to build and test a physical prototype or rely on the opinions of an instructor. This creates a delay in getting feedback at the most critical stage of the learning process, slowing down the iterative cycle of design and cutting short the exploration in the design space. As a result of this delay, experimenting and evaluating "micro ideas"--very small stepwise ideas such as those that investigate a design parameter at a time--through building, revising, and testing physical prototypes becomes impractical in many cases. From the perspective of learning, however, it is often at this level of granularity that foundational science and engineering design ultimately meet.

Figure 2
All these problems can be addressed by supporting engineering design with a computer-aided design (CAD) platform that embeds powerful science simulations to provide formative feedback to students in a timely manner. Simulations based on solving fundamental equations in science such as Newton’s Laws model the real world accurately and connect many science concepts coherently. Such simulations can computationally generate objective feedback about a design, allowing students to rapidly test a design idea on a scientific basis. Such simulations also allow the connections between design elements and science concepts to be explicitly established through fine-grained feedback, supporting students to make informed design decisions for each design element one at a time, as illustrated by the lower image of Figure 1. These scientific simulations give the CAD software tremendous disciplinary intelligence and instructional power, transforming it into a SmartCAD system that is capable of guiding student design towards a more scientific end.

Despite these advantages, there are very few developmentally appropriate CAD software available to K-12 students—most CAD software used in industry not only are science “black boxes” to students, but also require a cumbersome tool chaining of pre-processors, solvers, and post-processors, making them extremely challenging to use in secondary education. The SmartCAD project will fill in this gap with key educational features centered on guiding student design with feedback composed from simulations. For example, science simulations can be used to analyze student design artifacts and compute their distances to specific goals to detect whether students are zeroing in towards those goals or going astray. The development of these features will also draw upon decades of research on formative assessments of complex learning.

A stock-and-flow model for building thermal analysis

Figure 1. A stock-and-flow model of building energy.
Our Energy3D CAD software has two built-in simulation engines for performing solar energy analysis and building thermal analysis. I have extensively blogged about solar energy analysis using Energy3D. This article introduces building thermal analysis with Energy3D.

Figure 2. A colonial house.
The current version of the building energy simulation engine is based on a simple stock-and-flow model of building energy. Viewed from the perspective of system dynamics—a subject that studies the behavior of complex systems, the total thermal energy of a building is a stock and the energy gains or losses through its various components are flows. These gains or losses usually happen via the energy exchange between the building and the environment through the components. For instance, the solar radiation that shines into a building through its windows are inputs; the heat transfer through its walls may be inputs or outputs depending on the temperature difference between the inside and the outside.

Figure 3. The annual energy graph.
Figure1 illustrates how energy flows into and out of a building in the winter and summer, respectively. In order to maintain the temperature inside a building, the thermal energy it contains must remain constant—any shortage of thermal energy must be compensated and any excessive thermal energy must be removed. These are done through heating and air conditioning systems, which, together with ventilation systems, are commonly known as HVAC systems. Based on the stock-and-flow model, we can predict the energy cost of heating and air conditioning by summing up the energy flows in various processes of heat transfer, solar radiation, and energy generation over all the components of the building such as walls, windows, or roofs and over a certain period of time such as a day, a month, or a year.

Figure 2 shows the solar radiation heat map of a house and the distribution of the heat flux density over its building envelope. Figure 3 shows the results of the annual energy analysis for the house shown in Figure 2.

More information can be found in Chapter 3 of Energy3D's User Guide.

Common architectural styles supported by Energy3D


Energy3D supports the design of some basic architectural styles commonly seen in New England, such as Colonial and Cape Cod. Its simple 3D user interface allows users to quickly sketch up a house with an aesthetically pleasing look -- with only mouse clicks and drags (and, of course, some patience). This makes it easy for middle and high school students to create meaningful, realistic designs and learn science and engineering from these authentic experiences -- who wants to keep doing those cardboard houses that look nothing like a real house for another 100 years?

The true enabler of science learning in Energy3D is its analytic capability that can tell students the energy consequences of their designs while they are working on them. Without this analytical capability, learning would have been cut short at architectural design (which undeniably is the fun part of Energy3D that entices students to explore many different design options that entertain the eyes). With the analytical capability, the relationship between form and function becomes a major driving force for student design. It is at this point that an Energy3D project becomes an engineering design project.

Architectural design, which focuses on designing the form, and engineering design, which focuses on designing the function, are equally important in both educational and professional practices. Students need to learn both. After all, the purpose of design is to meet various people's needs, including their aesthetic needs. This principle of coupling architectural design and engineering design is of generic importance as it can be extended to the broader case of integrating industrial design and engineering design. It is this coupling that marries art, science, and usability.

We are working on providing a list of common architectural styles that can be designed using Energy3D. These styles, four of them are shown in this article, show only the basic form of each style. Each should only take less than an hour to sketch up for beginners. If you want, you can derive more complex and detailed designs for each style.

Visualizing the "thermal breathing" of a house in 24-hour cycle with Energy3D

The behavior of a house losing or gaining thermal energy from the outside in a 24-hour cycle, when visualized using Energy3D's heat flux view, resembles breathing, especially in the transition between seasons in which the midday can be hot and the midnight can be cold. We call this phenomenon the "thermal breathing" of a house. This embedded YouTube video in this blog post illustrates this effect. For the house shown in the video, the date was set to be May 1st and the location is set to Santa Fe, New Mexico.


This video only shows the daily thermal breathing of a house. Considering the seasonal change of temperature, we may also definite a concept "annual thermal breathing," which describes this behavior on an annual basis.

This breathing metaphor may help students build a more vivid mental picture of the dynamic heat exchange between a house and the environment. Interestingly, it was only after I realized this thermal visualization feature in Energy3D that this metaphor came to my mind. This experience reflects the importance of doing in science and engineering: Ideas often do not emerge until we get something concrete done. This process of externalization of thinking is critically important to the eventual internalization of ideas or concepts.

Simulating cool roofs with Energy3D

Fig. 1: Solar absorption of colors.
Cool roofs represent a simple solution that can save significant air-conditioning cost and help mitigate the urban heat island effect, especially in hot climates. Nobel Prize winner and former Secretary of Energy Steven Chu is a strong advocate of cool roofs. It was estimated that painting all the roofs and pavements around the world with reflective coatings would be "equivalent to getting 300 millions cars off the road!"

With Version 4.0 of Energy3D (BTW, this version supports 200+ worldwide locations -- with 150+ in the US), you can model cool roofs and evaluate how much energy you can save by switching from a dark-colored roof to a light-colored one. All you need to do is to set the colors of your roofs and other building blocks. Energy3D will automatically assign an albedo value to each building block according to the lightness of its color.

Figure 1 shows five rectangles in different gray colors (upper) and their thermal view (lower). In this thermal view, blue represents low energy absorption, red represents high energy absorption, and the colors in-between represents the energy absorption at the level in-between.

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

Fig. 2: Compare dark and white roofs.
How much energy can we save if we switch from a perfectly black roof (100% absorption) to a perfectly white roof (0% absorption)? We can run the Annual Energy Analysis Tool of Energy3D to figure this out in a matter of seconds. The results are shown in Figure 3. Overall, the total yearly energy cost is cut from 6876 kWh to 6217 kWh for this small cape code house, about 10% of saving.

Figure 3 shows that the majority of savings comes from the reduction of AC cost. The reason that the color has no effect on heating in the winter is because the passive solar heat gains through the windows in this well-insulated house is enough to keep it warm during the sunshine hours. So the additional heat absorbed by the black roof in the same period doesn't offset the heating cost (it took me quite a while to figure out that this was not a bug in our code but actually the case in the simulation).

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

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

Here is a video that shows the heating effect on roofs of different colors.

Visualization of heat flux in Energy3D using vector fields

Fig. 1: Winter in Boston
One of the strengths of our Energy3D CAD software is its 3D visualizations of energy transfer. These visualizations not only allow students to see science concepts in action in engineering design, but also provide informative feedback for students to make their design choices based on scientific analyses of their design artifacts.

Fig. 2: Summer in Boston
A new feature has been added to Energy3D to visualize heat transfer across the building envelope using arrays of arrows. Each arrow represents the heat flux at a point on the surface of the building envelope. Its direction represents the direction of the heat flux and its length represents the magnitude of the heat flux, calculated by using Fourier's Law of Heat Conduction. Quantitatively, the length is proportional to the difference between the temperatures inside and outside the building, as well as the U-value of the material.

Fig. 3: Winter in Miami
The figures in this post show the heat flux visualizations of the same house in the winter and summer in Boston and Miami, respectively. Like the solar radiation heat map shown in the figures, the heat flux is the daily average. The U-value of the windows is greater than those of the walls and roof. Hence, you can see that the heat flux vectors in the winter sticking out of the windows are much longer than those sticking out of the walls or roof. In the summer, the heat flux vectors point into the house but they are much shorter, agreeing with the fact that Boston's summer is not very hot.

Fig. 4: Summer in Miami
Now move the same house to Miami. You can see that even in the winter, the daily average heat flux points inside the house, agreeing with the fact that Miami doesn't really have a winter. In the summer, however, the heat flux into the house becomes significantly large.

These visualizations give students clear ideas about where a house loses or gains energy the most. They can then adjust the insulation values of those weak points and run simulations to check if they have been fixed or not. Compared with just giving students some formulas or numbers to figure out what they actually mean to science and engineering practices, experiential learning like this should help students develop a true understanding of thermal conduction and insulation in the context of building science and technology.

Here is a YouTube video of the heat flux view.

A 16-year-old’s designs with Energy3D

This post needs no explanation. The images say it all.

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

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

Accurate prediction of solar radiation using Energy3D: Part III

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

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

Go to Part I and Part II.