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!

Learning analytics is the "crystallography" for educational research

March 24th, 2014 by Charles Xie
To celebrate 100 years of dazzling history of crystallography, the year of 2014 has been declared by UNESCO as the International Year of Crystallography. To this date, 29 Nobel Prizes have been awarded to scientific achievements related to crystallography. On March 7th, the Science Magazine honored crystallographers with a special issue.

Why is crystallography such a big deal? Because it enables scientists to "see" atoms and molecules and discover the molecular structures of substances. One of the most famous examples is the discovery of the DNA helix by Rosalind Franklin in 1952, followed by Crick, Watson, and Wilkins' double helix model. Enough ink has been spilled on the importance of this discovery.

Science fundamentally relies on techniques such as crystallography for detecting and visualizing invisible things. Educational research needs this kind of techniques, too, to decode students' minds that are opaque to researchers. Up to this point, educational researchers depend on methods such as pre/post-tests, observations, and interviews. But these traditional methods are either insufficient or inefficient for measuring learning in complex processes such as scientific inquiry and engineering design. To achieve a level of truly "no child left behind," we will need to develop a research technique that can monitor every student for every minute in the classroom.

Such a technique has to be based on an integrated informatics system that can engage students with meaningful learning tasks, tease out what are in their minds, and capture every bit of information that may be indicative of learning. This involves development in all areas of learning sciences, including technology, curriculum, pedagogy, and assessment. Eventually, what we have is a comprehensive set of data through which we will sift to find patterns of learning or evaluate the effectiveness of an intervention.

The whole process is not unlike crystallography. At the end, it is the learning analytics that concludes the research. Today we are seeing a lot of learner data, but we probably have no idea what they actually mean. We can either say there is no significance in those data and shrug off, or we can try to figure out the right kind of data analytics to decipher them. Which attitude to choose probably depends on which universe we live in. But the history of crystallography can give us a clue. It was Max von Laue who created the first X-ray diffraction pattern in 1912. He couldn't interpret it, however. It wasn't until William Henry Bragg and William Lawrence Bragg's groundbreaking work later in the same year that scientists became able to infer molecular structures from those patterns. In educational research, the equivalent of this is the learning analytics -- a critical piece that will give data meaning.

For more information, read my new article "Visualizing Student Learning."

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.

Getting sensor data out of Energy2D

February 9th, 2014 by Charles Xie
Figure 1: Copy data from Energy2D.
Since a few users asked if the simulation data in Energy2D can be exported to other applications such as Excel, I have added a feature to the app for extracting virtual sensor data as multi-column time series data. For the user's convenience, there are three different ways of getting these data:
  1. When right-clicking on a sensor, the "View Data..." from the popup menu returns the data that has been recorded by the selected sensor.
  2. When right-clicking on a spot not occupied by a sensor, the "View Data..." from the popup menu returns a tabbed pane that contains all the sensor data -- different types of sensor are organized in different tabs.
  3. When the translucent graph is open, clicking the View Data button on the graph window's control panel returns the data recorded by all the sensors of the selected type, in consistent with the current display of the data in the graph window.
Figure 2: Paste data into Excel.
Regardless of which way you use, use the "Copy Data" button at the bottom of the data window to copy the data (Figure 1) and paste it into Excel. Once you get the data into Excel, you can process and plot them in any way you want (Figure 2). This feature is very handy if you need to combine data from multiple simulations into a single graph.

Note: This feature only works for the app. For security reason, the embedded applet is not allowed to access the System Clipboard (this is understandable, because people often copy and paste important information!)

Tablet-friendly STEM Resources

January 24th, 2014 by Jen Goree

Is your New Year’s resolution to find more interactive STEM resources that are tablet-ready? (We understand — we make similar technology-related resolutions, too!) We’ve optimized many of our browser-based interactive resources to run on popular tablets. By tuning our code, we’re able to make the power of our models available for your students!

For example, this Phase Change interactive runs 60% faster than it did before our recent code improvements:

And Metal Forces runs 33% faster:

Here’s a few to try now:





For even more, check out a complete list of our tablet-friendly STEM resources.

Welcome to our three Google Summer of Code students

May 28th, 2013 by Cynthia McIntyre

Google Summer of Code 2013Three international students will spend the summer coding for our open source projects. Through Google Summer of Code (GSoC), they’ll earn stipends from Google, plus get a coveted GSoC t-shirt and certificate.

Expansion of SPARKS HTML5 circuit simulator

Our HTML5 breadboard simulator allows students to experiment with basic DC and AC circuits using linear components (resistors, capacitors, inductors) and to perform measurements with a function generator, a digital multimeter and an oscilloscope.

Sabareesh Nikhil C, from Hyderabad, India, will extend our existing circuit-solving code to handle non-linear components such as diodes, op amps and transistors. Instead of treating each circuit as a lumped impedance and computing its response to a single frequency, the new code will perform a more realistic time-based computation, which will enable it to model the behavior of more complex circuits. Sabareesh also plans to implement a communication protocol that will enable circuits on different computers to communicate with each other.

Sabareesh will work with Concord Consortium mentors Paul Horwitz, Sam Fentress and Richard Klancer.

Probe your browser!

Science classrooms use probes and sensors to enable real-time data collection by students. Currently we use Java applets to support communication between sensors and web-based applications in the browser. Increasingly limited support for Java is making it difficult to integrate probes and sensors that use Java software for use in the classroom.

Lingliang Zhang from New York, NY, and Abu Dhabi, United Arab Emirates, will design a native application for desktops, which will make the data from probes and sensor hardware available to our browser-based JavaScript applications. The native application will use an embedded webserver to connect to our existing sensor library. This approach will enable browsers on desktops and laptops to use our currently supported Pasco and Vernier sensor devices without a Java applet.

He will work under the mentorship of our Senior Software Engineer Scott Cytacki.

Port HTML5 interactives to phones and tablets

Our HTML5 interactives are rendered using a semantic layout system. With a modified UI, they could work on phones, allowing students to interact with them on multiple devices. Additionally, with an iOS and Android application created using Cordova, users could install the interactives and use them offline. This app could also allow parts of the engine behind the interactives to run natively in order to get better performance on these devices.

Apoorv Narang from New Delhi, India, will measure performance on various devices to determine which of our HTML5 interactives can be run on these devices. He will improve our lab framework, which is the system that displays and runs interactives, with the goal of making our interactives look—and run!—better on phones.

Director of Technology Stephen Bannasch will mentor Apoorv.


During summer 2012, we were fortunate to have two fabulous GSoC students, including Piotr Janik, who continues coding for us as a consultant. Watch Piotr describe his experience with Google Summer of Code.

We can’t wait to see the code that our three new GSoC students will develop this summer!


Classrooms on fire…with dragon genetics!

May 15th, 2013 by Frieda Reichsman

No smoke and mirrors here: dragons are getting kids all fired up about genetics. Geniverse software engages students with compelling reasons to solve genetics problems. As they rise through the ranks of the Drake Breeders Guild, students win stars and quills for efficient experimentation and for using their own experimental results as evidence for their scientific claims. Watch how students are learning genetics while having fun—using Geniverse! Want to get your students fired up about genetics, too? Sign up to use Geniverse in your classroom next year.

Modeling Physical Behavior with an Atomic Engine

May 13th, 2013 by Sara Remsen

Our Next-Generation Molecular Workbench (MW) software usually models molecular dynamics—from states of matter and phase changes to diffusion and gas laws. Recently, we adapted the Molecular Dynamics 2D engine to model macroscale physics mechanics as well, including pendulums and springs.

In order to scale up the models from microscopic to macroscopic, we employ specific unit-scaling conventions. The Next-Generation Molecular Workbench (MW) engine simulates molecular behavior by treating atoms as particles that obey Newton’s laws. For example, the bond between two atoms is treated as a spring that obeys Hooke’s law, and electrostatic interactions between charged ions follow Coulomb’s Law.

Dipole-dipole interactions simulated using Coulomb’s Law.

At the microscale, the Next-Generation MW engine calculates the forces between molecules or atoms using atomic mass units (amu), nanometers (10−9 meters) and femtoseconds (10-15 seconds), and depicts their motion. To simulate macroscopic particles that follow the same laws, we can imagine them as microscopic particles with masses in amu, distance in nanometers, and timescales measured in femtoseconds. Once the Next-Generation MW engine calculates the movement of these atomic-scale particles, we simply multiply the length, mass and time units by the correct scaling factors. This motion satisfies the same physical laws as the atomic motion but is now measured in meters, kilograms and seconds.

In the pendulum simulation below, the Next-Generation MW engine models the behavior of a pendulum by treating it as two atoms connected by a very stiff bond with a very long equilibrium length. The topmost atom is restrained to become a “pivot” while the bottom atom “swings” because of the stiff bond. Once the engine has calculated the force using the atomic-scale units, it converts the mass, velocity and acceleration to the appropriate units for large, physical objects like the pendulum.

Large-scale physical behavior simulated with a molecular dynamics engine.

In order to appropriately model the physical behavior of a pendulum or a spring, we use specific scaling constants. Independent scaling constants for mass, distance and time enable us to convert nanometers to meters, atomic mass units to kilograms and femtoseconds to model seconds. Using the same scaling constants, we can derive other physical conversions, such as elementary charge unit to Coulomb. In order to make one model second pass for every real second, we adjusted the amount of model time between each page refresh. We also chose to simulate a gravitation field—a feature usually absent in molecular dynamics simulators—because it is relevant to macroscopic phenomena.

From microscale to macroscale, the Next-Generation Molecular Workbench engine is a powerful modeling tool that we can use to simulate a wide variety of biological, chemical, and physical phenomena.  Find more simulations at