Archive for the ‘Main Blog’ Category

New CODAP Logo

November 17th, 2014 by The Concord Consortium

Our Common Online Data Analysis Platform (CODAP) allows you to dig deep into the data all around you. Whether you’re investigating data you’ve gathered yourself via probes and sensors, maps of the travel paths of elephant seals and sharks, data streams from a simulation of global climate change or wins and losses from an online game of rock-paper-scissors, CODAP can help! CODAP is about exploring and learning from data from any content area, so it’s perfect for math, science, social studies, physical education classes and more.

Today, we are proud to announce our elegant new CODAP logo, developed by Derek Yesman of Daydream Design*.


We think this logo expresses the many faces of CODAP both concisely and beautifully. Looking at the world of data through the four panes of a window, you might notice different elements. Link them together with CODAP’s tools to see the big picture or to focus in on one or more relationships in the data. Naturally, different windows in the CODAP software open to permit data analysis of different aspects, yet the views remain linked at the same time, providing deep insights into the connections across different perspectives.

Do you see a scatter plot? We do, too. You’ll see lots of circles in CODAP – they represent individual data points or points summarizing data across experimental runs. Our new logo continues that circle theme, bringing in a family of different colors that evoke the many diverse disciplines that CODAP can tackle, all linked through the common power of data exploration.

There are also linear elements in the design that evoke bar graphs and standard chart views – the bread and butter of everyday data analysis.

Our CODAP software and logo continue the legacy of both Fathom and TinkerPlots, and we are proud to be building on this tradition of amazing data exploration tools. Our new web-based platform was created with National Science Foundation funding by Senior Scientist Bill Finzer. CODAP is open source software, free to use, adapt and extend. Contact us for more information and to start analyzing the data around you today!

* Longtime fans may be familiar with Derek’s work—he also designed the Molecular Workbench logo and our company logo.

Two “Global Experiments” about Climate Change

September 10th, 2014 by Andy Zucker

In an earlier post on this blog I wrote about the need to increase our knowledge of how people think about climate change and then apply that knowledge to expedite policy changes. Subsequently I discovered that there is an active community of psychologists, experts in communications and other researchers who conduct valuable inquiries in this field.

Some of their findings are sobering, such as data from April 2014 showing that only one in three Americans discusses global warming with family and friends even occasionally. One of the many reasons this is true is that for more than five years, with little variation from year to year, only about one in three Americans have believed people in the U.S. are being harmed “right now” by global warming—despite Superstorm Sandy, Hurricane Katrina, extreme drought in the West and other once-rare climate events. One does not wish for more droughts, extreme heat waves, superstorms or wildfires; however, those are the kinds of events that may, slowly, change opinions about climate change.

People who realize how threatening climate change is believe that we are conducting a risky “experiment” with the sensitivity of Earth systems to increasing levels of carbon dioxide and other heat-trapping gases. For example, we still don’t understand climate “tipping points” well; however, our global experiment is already teaching us about tipping points, like it or not.

Professor Richard Somerville of the Scripps Institution of Oceanography at UC San Diego—who was Coordinating Lead Author of the Intergovernmental Panel on Climate Change’s fourth assessment report (2007)—wrote, in his 2006 paper called “Medical metaphors for climate change,”

“What is still not obvious to many is that all of us are now engaged in a second global experiment [emphasis added], this time an educational and geopolitical one. We are going to find out whether humanity is going to take climate science seriously enough to act meaningfully, rather than just procrastinating until nature ultimately proves that our climate predictions were right.”

This educational experiment—where education is broadly defined and includes more than what is taught in schools—could hardly be more important. It will take a concerted effort, over many years, by formal and informal institutions, political leaders and organizations, TV stations, museums, churches and others, to increase knowledge and a sense of urgency among policymakers and the public. The Concord Consortium’s High Adventure Science (HAS) project is one element in this long-term effort.

The Next Generation Science Standards include climate change as an important topic for instruction, which is significant because more people need some understanding of the science behind climate change. But the “educational experiment” the world is conducting requires policymakers and the public to learn far more than science. Better understanding the economics, political, regulatory, governance, diplomatic and technology issues needed to address climate change will be vital, as will an emphasis on ethics and values. It would be interesting to know how, and how often, climate change is a topic of study in other subjects taught in schools, colleges and universities (including social science classes, like Psychology), or whether it is addressed almost exclusively in “hard science” courses.

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

September 9th, 2014 by Charles Xie
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.

Using particle feeders in Energy2D for advection simulations

August 30th, 2014 by Charles Xie
Fig. 1: Particle advection behind two obstacles.
Advection is a transport mechanism in which a substance is carried by the flow of a fluid. An example is the transport of sand in a river or pollen in the air. Advection is different from diffusion, whereas the more commonly known term, convection, is the combination of advection and diffusion.

Our Energy2D can simulate advection as it integrates particle dynamics in the Lagrangian frame and fluid dynamics in the Eulerian frame. Particles in Energy2D do not spontaneously diffuse -- they are driven by gravity or fluid, though we can introduce Brownian particles in the future by incorporating the Langevin Equation into Energy2D.

Fig. 2: Blowing away particles.
Over this weekend, I added a new object, the particle feeder, for creating continuous particle flow in the presence of open mass boundary. A particle feeder can emit a specified type of particle at a specified frequency. All these settings can be adjusted in its property window, which can be opened by right-clicking on it and selecting the relevant menu.

Figure 1 shows a comparison of particle advection behind a turbulent flow and a streamlined flow. Have you ever seen these kinds of patterns in rivers?

Figure 2 shows how particles of different densities separate when you blow them with a fan. There are six particle feeders at the top that continually drop particles. A fan is placed not far below the feeders.

With these new additions to Energy2D, we hope to be able to simulate more complex atmospheric phenomena (such as pollutant transport through jet streams) in the future.

A 16-year-old’s designs with Energy3D

August 13th, 2014 by Charles Xie
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.)

Using fans to create fluid flows in Energy2D

August 10th, 2014 by Charles Xie
Fig. 1: Swirling flows form between two opposite fans.
A new type of object, "fan", has been added to Energy2D to create and control fluid flows. This fan replaces the original implementation of fan that assigns a velocity to a solid part (which doesn't allow the fluid to flow through). For the CFD folks who are reading this post, this is equivalent to an internal velocity boundary.

To add a fan to the scene, use the Insert Menu to drop a fan to the last clicked location. You can then drag it anywhere and resize it any way. By default, the velocity of a fan is zero. You will need to set its velocity in the popup window that can be opened using the right-click popup menu. Currently, however, rotation has not been implemented, so a fan can only blow in four directions: left, right, up, or down -- the direction depends on the aspect ratio of the fan's shape and the value of the velocity.

Fig. 2: Eddy formation in a hole.
With this new feature, we can create a directional flow in Energy2D to simulate things such as a river or wind field. Then we can easily simulate various kinds of eddy flow and visualize them using the streamline feature of Energy2D.

For example, Figure 1 shows the continuous formation of swirling flows between two fans that blow wind in the opposite direction. If you move the fans further apart, you will find that the swirling pattern will not form. Could the mechanism shown in this simulation be related to the formation of certain types of twisters?

Fig. 3: Eddy formation behind a fin.
Figures 2 and 3 show the formation of an eddy in a hole and behind an obstacle, respectively. These eddies are common in fast-flowing rivers. Experienced fishermen know there is a higher chance to find fish in these eddies.

Accurate prediction of solar radiation using Energy3D: Part III

August 6th, 2014 by Charles Xie
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.

From conceptual design to detailed design with Energy3D

August 1st, 2014 by Charles Xie
Figure 1: Empire State Building
An important objective of our Energy3D software is to explore how to create CAD software that support students to practice the full cycle of engineering design from conceptual design to detailed design in a single piece of software. We believe that interactive 3D visualizations and simulations provided by CAD tools are cognitively important for K-12 students who have little prior knowledge about the subject of design or the process of design to develop some sense of them -- through practice. Instantaneous visualizations of the results of their actions within the CAD software can give students some concrete clues to develop, share, and refine their ideas directly within a visual design space.

Although there have been some cautions about the use of CAD software in design education, my take is that, in the very early stage of grassroots design education, the problem is not that students are handicapped by a design tool; the problem is that they lack ideas to start with or skills to put their ideas into actions and should be aided by an intelligent design tool (in addition to a teacher, of course). A good CAD tool will be very instructive in this stage. Only after students rise to a certain expert level will the limitations of the CAD software begin to emerge. Often in the K-12 settings, the time constraint does not allow the majority of students to reach that level through conventional instruction, however. Hence, it is likely that the positive effects of using CAD software in K-12 engineering education will outweigh the negative effects, letting alone that students will learn important computer design and modeling skills that will be extremely useful to their future STEM careers.
Figure 2: Freedom Tower

But not all CAD software were created equal. Many CAD software have been developed for professional engineers and are not appropriate for K-12 applications, even though many software vendors have managed to enter the K-12 market in recent years. Given the rise of engineering in K-12 schools, it is probably the right time to rethink how to develop a CAD platform that supports design, learning, and assessment from the ground up.

Our Energy3D CAD software has provided us a powerful platform to ponder about these questions. From the beginning of this project back in 2010, we had been envisioning a CAD platform that integrates conceptual design, detailed design, collaborative design, numerical analysis, designer modeling, machine learning, and digital fabrication. After four years' continuous work, the software can now do not only conceptual design like a sketch tool but also detailed design like a production CAD program (look at the details in Figure 3). In terms of education, this means that it has a very short learning curve that allows all students to translate their ideas into computer models in a short amount of time and, meanwhile, a very deep design space that allows some of the willing students to advance to an expert level. With these capacities, we are now conducting leading-edge data mining research to investigate how to facilitate the transition. The research will eventually translate into novel software features of the CAD program.
Figure 3: Kendall Square

Cormac Paterson, a brilliant high school student from Arlington, MA, has demonstrated these possibilities. He has created many designs with Energy3D that are showcased in our model repository, including all the designs shown in the figures of this post.

On the instructional sensitivity of computer-aided design logs

July 20th, 2014 by Charles Xie
Figure 1: Hypothetical student responses to an intervention.
In the fourth issue this year, the International Journal of Engineering Education published our 19-page-long paper on the instructional sensitivity of computer-aided design (CAD) logs. This study was based on our Energy3D software, which supports students to learn science and engineering concepts and skills through creating sustainable buildings using a variety of built-in design and analysis tools related to Earth science, heat transfer, and solar energy. This paper proposed an innovative approach of using response functions -- a concept borrowed from electrical engineering -- to measure instructional sensitivity from data logs (Figure 1).

Many researchers are interested in studying what students learn through complex engineering design projects. CAD logs provide fine-grained empirical data of student activities for assessing learning in engineering design projects. However, the instructional sensitivity of CAD logs, which describes how students respond to interventions with CAD actions, has never been examined, to the best of our knowledge.
Figure 2. An indicator of statistical reliability.

For the logs to be used as reliable data sources for assessments, they must be instructionally sensitive. Our paper reports the results of our systematic research on this important topic. To guide the research, we first propose a theoretical framework for computer-based assessments based on signal processing. This framework views assessments as detecting signals from the noisy background often present in large temporal learner datasets due to many uncontrollable factors and events in learning processes. To measure instructional sensitivity, we analyzed nearly 900 megabytes of process data logged by Energy3D as collections of time series. These time-varying data were gathered from 65 high school students who solved a solar urban design challenge using Energy3D in seven class periods, with an intervention occurred in the middle of their design projects.

Our analyses of these data show that the occurrence of the design actions unrelated to the intervention were not affected by it, whereas the occurrence of the design actions that the intervention targeted reveals a continuum of reactions ranging from no response to strong response (Figure 2). From the temporal patterns of these student responses, persistent effect and temporary effect (with different decay rates) were identified. Students’ electronic notes taken during the design processes were used to validate their learning trajectories. These results show that an intervention occurring outside a CAD tool can leave a detectable trace in the CAD logs, suggesting that the logs can be used to quantitatively determine how effective an intervention has been for each individual student during an engineering design project.

Simulating PTC and NTC heating elements with Energy2D

June 23rd, 2014 by Charles Xie
Figure 1: A demo simulation.
A heating element converts electricity into heat through Joule heating: Electric current passing through the element encounters resistance, causing the temperature of the element to rise. A thermistor is a type of resistor whose resistance changes significantly with temperature. In a heating element that uses a thermistor with a positive temperature coefficient (PTC), called a PTC heating element, the temperature increases rapidly. In a heating element that uses a thermistor with a negative temperature coefficient (NTC), called a NTC heating element, the heating will gradually weaken when the temperature increases.

Figure 2: Setting the temperature coefficient.
Several Energy2D users have requested adding PTC/NTC controls to the software. So this was added last night. You can now set the temperature coefficient while defining a power source, as shown in Figure 2.

Figure 1 shows the comparison of the temperature increasing in a PTC heater, a constant-power heater, and a NTC heater, with the temperature coefficients being 0.1, 0, and -0.1, respectively. Note that in the case of constant power, the temperature increases linearly in time (as per the definition of constant power), whereas PTC and NTC exhibit nonlinear behaviors.

You can click the link under the image to run the simulation yourself.