Category Archives: Projects

Teaching about water quality and the importance of fresh water

A new resolution may overturn the Interior Department’s “Stream Protection Rule,” which required coal mining companies to monitor and test the quality of local streams and rivers before, during, or after mining operations. There is no better time than the present to learn about the importance of water issues in our communities and environment. Three Concord Consortium projects focus on teaching middle and high school students about their local watersheds, careers in environmental conservation, and freshwater availability, and all of them offer free, high-quality resources ideal for classrooms or informal education settings.

The Teaching Environmental Sustainability: Model My Watershed project, a collaborative research project at the Concord Consortium, Millersville University, and the Stroud Water Research Center, has developed curricula for environmental/geoscience disciplines for high school classrooms, using the Model My Watershed (MMW) web-based application. The curricula also integrate low-cost environmental sensors, allowing students to collect and upload their own data and compare them to data visualized on the new MMW.

In Supporting Collaborative Inquiry, Engineering, and Career Exploration with Water (Water SCIENCE), middle school students from southern Arizona, central valley California, southeastern Pennsylvania, and eastern Massachusetts complete hands-on science and engineering activities, receive guidance and instruction from undergraduate and graduate student mentors, interact online with STEM professionals, and learn about careers in environmental conservation and engineering while investigating their community’s local water resources.

Melinda Daniels, Associate Research Scientist at the Stroud Water Research Center, describes her work. Watch additional videos about water scientists and environmental conservationists »

And in our High-Adventure Science project, we’ve developed a unit entitled “Will there be enough fresh water?” Students explore the distribution and uses of fresh water on Earth. They run experiments with computational models to explore the flow of groundwater, investigate the relationship of groundwater levels to rainfall and human impact, and hear from a hydrologist working on the same question. Students think about how to assess the sustainability of water usage locally and globally while considering their own water usage. Use these great resources today to help students understand critical water issues!


Students use computational models to explore water extraction from aquifers in urban and rural areas.

New features in CODAP

Our Common Online Data Analysis Platform (CODAP) software provides an easy-to-use web-based data analysis tool, geared toward middle and high school students, and aimed at teachers and curriculum developers. CODAP is already full of amazing features. We’re excited to announce several new features! Continue reading

High-Adventure Science Partnership with National Geographic Education

We are excited to announce that the Concord Consortium’s High-Adventure Science modules are now available on the National Geographic Education website, thanks to a National Science Foundation-funded partnership with National Geographic Education. High-Adventure Science modules have been used by thousands of students so far, and we welcome the opportunity to share our modules with a wider audience of middle and high school teachers and students. All modules will continue to be available on the High-Adventure Science website.

High-Adventure Science: Bringing contemporary science into the classroom

Each week-long High-Adventure Science module is built around an important unanswered question in Earth or environmental science; topics include fresh water availability, climate change, the future of our energy sources, air quality, land management, and the search for life in the universe.

Throughout each module, students learn about the framing question, experiment with interactive computer models, analyze real world data, and attempt to answer the same questions as research scientists. We don’t expect that students will be able to answer the framing questions at the end of the module (after all, scientists are still working to answer them!); rather, we want to engage students in the process of doing science, building arguments around evidence and data and realizing that not knowing the answers (uncertainty) drives scientific progress.

To that end, each module (and associated pre- and post-tests) contains several scientific argumentation item sets. The argumentation item set, with multiple-choice and open-ended questions, prompts students to consider the strengths and weaknesses of the provided data (graphs, models, tables, or text). Our research has shown that, after using High-Adventure Science modules, students improve both their understanding of the science content and their scientific argumentation skills. Register for a free account on the High-Adventure Science portal for access to pre- and post-tests.

Expanded teacher resources through National Geographic Education

Partnering with National Geographic Education has allowed us to provide more support for teachers. On the National Geographic Education website, you’ll find in-depth teaching tips, background information, vocabulary definitions, and links to the standards (NSES, Common Core, ISTE, and NGSS) to which our curricula are aligned. Additionally, each module is linked to related resources in the National Geographic catalog, greatly expanding the resources available to both teachers and students.

Teachers have been excited about the models, real world data, and the argumentation prompts that get students to focus on the evidence when making a scientific claim. (You can hear directly from one of the High-Adventure Science field test teachers at NSTA!)

Come see us at NSTA in Nashville, TN, this week! Stop by the National Geographic booth or come to a presentation about using High-Adventure Science modules in your classroom:

  • “High-Adventure Science: Free Simulations Exploring Earth’s Systems and Sustainability” on Thursday, March 31, from 12:30-1:00 PM in Music City Center, 106A
  • “Integrating Literacy Standards in Science” on Sunday, April 3, from 8:00-9:00 AM in Music City Center, 209A


Modeling solar thermal power using heliostats in Energy2D

An array of heliostats in Energy2D (online simulation)
A new class of objects was added in Energy2D to model what is called a heliostat, a device that can automatically turn a mirror to reflect sunlight to a target no matter where the sun is in the sky. Heliostats are often used in solar thermal power plants or solar furnaces that use mirrors. With an array of computer-controlled heliostats and mirrors, the energy from the sun can be concentrated on the target to heat it up to a very high temperature, enough to vaporize water to create steam that drives a turbine to generate electricity.

Image credit: Wikipedia
The Ivanpah Solar Power Facility in California's Mojave Desert, which went online on February 13, 2014, is currently the world's largest solar thermal power plant. With a gross capacity of 392 megawatts, it is enough to power 140,000 homes. It deploys 173,500 heliostats, each controlling two mirrors.

A heliostat in Energy2D contains a planar mirror mounted on a pillar. You can drop one in at any location. Once you specify its target, it will automatically reflect any sunlight beam hitting on it to the target.

Strictly speaking, heliostats are different from solar trackers that automatically face the sun like sunflowers. But in Energy2D, if no target is specified, as is the default case, a heliostat becomes a solar tracker. Unlike heliostats, solar trackers are often used with photovoltaic (PV) panels that absorb, instead of reflecting, sunlight that shine on them. A future version of Energy2D will include the capacity of modeling PV power plants as well.

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.

Using particle feeders in Energy2D for advection simulations

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

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

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

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.

Scanning radiation flux with moving sensors in Energy2D

Figure 1: Moving sensors facing a rectangular radiator.
The heat flux sensor in Energy2D can be used to measure radiative heat flux, as well as conductive and convective heat fluxes. Radiative heat flux depends on not only the temperature of the object the sensor measures but also the angle at which it faces the object. The latter is known as the view factor.

In radiative heat transfer, a view factor between two surfaces A and B is the proportion of the radiation which leaves surface A that strikes surface B. If the two surfaces face each other directly, the view factor is greater than the case in which they do not. If the two surfaces are closer, the view factor is greater.

Figure 2: Rotating sensors inside and outside a ring radiator.
To conveniently visualize the effect of a view factor, Energy2D allows you to attach a heat flux sensor to a moving or rotating particle, with a settable linear or angular velocity. In this way, we can set up sensors to automatically "scan" the field of radiation heat flux like a radar.

Figure 1 shows a moving sensor and a rotating sensor, as well as the data they record. A third sensor is also placed to the right of an object that is being heated by the radiator. This object has an emissivity of one so it also radiates. Its radiation flux is recorded by the third sensor whose data shows a slowly increasing heat flux as the object slowly warms up.

As an interesting test case, Figure 2 shows two rotating sensors, one placed precisely at the center of a ring radiator and the other outside. The almost steady line recorded by the first sensor suggests that the view factor at the center does not change, which makes sense. The small sawtooth shape is due to the limitation of discretization in our numerical simulation.