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:

Biology

Physics

Chemistry

Mathematics

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 mw.concord.org/nextgen/interactives.

Hello, world!

January 8th, 2013 by Chad Dorsey

For nearly 18 years, our logo has been a beautiful and complex sunflower, created by Senior Web Developer Noah Paessel. (He was Noah Fields back in 1994 when he worked at the Concord Consortium during his first stint with us, but that’s another blog post!)

With the former logo, our founder, Bob Tinker, wanted to showcase the Fibonacci sequence in nature, which represents a fascinating link between the sublime and the natural world and invites scientific inquiry and mathematical investigation. (Sunflower seeds exhibit many different Fibonacci spirals in their close-packed patterns, as do many other things in the natural world . Bob also thought Concord as a place evoked important concepts of revolution and free thinking and that the etymology of the name “Concord” linked with the sunflower expressed the ideas of “sharing one’s heart” and being “of the same mind,” both of which resonated with his pacifist and gentle nature.

We are now proud to announce our new logo, created by Derek Yesman of Daydream Design.

Concord Consortium Logo

This logo both simplifies and augments our original logo. It morphs the original sunflower while also referencing both technology and our core mission of generating, experimenting with and spreading important ideas.

The central star represents the initial spark of an idea, that “a-ha moment” of inspiration that can so quickly turn into extended experimentation – or possibly into a whole new research project. The light bulb surrounding it represents how we work to build these inspirational flashes into complete ideas and products and determine their potential to improve teaching and learning. The petals and radiating elements in the background represent our mission to spread the best of these ideas outward to transform learning for millions around the world.

We’ve recently modified our tag line to make this mission (and our ties to Concord’s location and history) even more explicit: Revolutionary digital learning for science, math and engineering.

By the way, for all you font geeks (don’t hide – we know you’re out there!) our logotype is rendered in Museo 500, part of Jos Buivenga’s excellent Museo family. We discovered this font when we worked with ISITE Design during our last website redesign – thanks Patrick! – and fell in love. Since then, we’ve explored the many weights of this font as well as its sans serif and slab variants. We’ve also had some early-adopter fun watching this font gain status and uptake in many print and Web locations on its way to becoming a modern classic.

We’re excited about this new logo and about how it represents an evolution we’re in the midst of as well. As we evolve toward a new phase as an organization while still embracing our legacy as pioneers in educational technology, we’re more committed every day to creating a bright future for STEM teaching and learning.

9 Highlights of 2012

January 3rd, 2013 by Cynthia McIntyre

It was a great year for the Concord Consortium!

  1. We won a Smaller Business Association of New England (SBANE) Innovation Award!
  2. Next-Generation Molecular Workbench interactives starred in the MIT MOOC (Massive Open Online Course) “Introduction to Solid State Chemistry” through a new collaboration with edX.
  3. Chad Dorsey described our vision of deeply digital education at the national Cyberlearning Research Summit.
  4. Six new projects were funded by the National Science Foundation: InquirySpace, Understanding Sub-Microscopic Interactions, High-Adventure Science: Earth’s Systems and Sustainability, GeniVille, Graph Literacy, and Sensing Science.
  5. The What Works Clearinghouse (WWC), a federally funded organization that scans educational research for high-quality studies, recognized our Technology Enhanced Elementary and Middle School Science (TEEMSS) software and materials.
  6. The Concord Consortium Collection was accessioned into the National Science Digital Library (NSDL).
  7. Our debut webcast featured Chad Dorsey, speaking about the scientific and engineering practices of the Next Generation Science Standards and our free, technology-based activities.
  8. We had two fabulous Google Summer of Code students.
  9. Our staff population increased by 10%, thanks to our new Software Portfolio and Project Manager Jen Goree, Web Developer Parker Morse, and Software Developer Tom Dyer, who just started (technically in 2013, but we’re so excited, we’ve included him on this 2012 list)!

2013 promises to be another great year! Follow us on Facebook, Twitter, Google+, and subscribe to our mailing list to receive print or email news updates.

Hitting the Wall

December 6th, 2012 by Amy Pallant

Gas laws are generally taught in high school chemistry. Students learn that Boyle’s law, for instance, can be expressed as P1V1=P2V2, where P is pressure and V is volume.

From the equation, it’s clear that there is an inverse relationship between the gas pressure and volume, but do students understand the molecular mechanism behind this relationship?

Since students are programmed to plug and chug, if you give them, say, P1, V1, and P2, they can find the numeric value of V2. Although students can get the correct answer, teachers have told us that their students don’t really understand the gas laws because they don’t have a mental model of what’s happening. Gases are, after all, invisible! Nor can students see volume or pressure.

Molecular Workbench makes the gases, volume, and pressure visible. With a new set of Next-Generation Molecular Workbench interactives, students can experiment with increasing the pressure on a gas to see why the gas volume decreases.

The “What is Pressure?” interactive (above) shows the inside (yellow atoms) and outside (pink atoms) of a balloon. (Even the velocities of the individual atoms are visible with vectors!) The green barrier represents the wall of the balloon.

Students learn that pressure is nothing more than molecular collisions with a barrier. In the beginning, atoms hitting the balloon wall on either side move it just a tiny bit—transferring some of their kinetic energy to the barrier. At equilibrium, the balloon wall remains (relatively) stationary. (Go ahead and run it to see!)

But if you add atoms to the balloon, the balloon wall moves out; more atoms means that there is increased pressure pushing outwards on the barrier. Since the number of atoms on the outside of the balloon hasn’t changed, the pressure pushing inwards is the same as it was before. With unbalanced forces, you get net movement.

With barriers, we can also measure the pressure caused by those molecular collisions.

In the “Volume-Pressure Relationship” interactive (above), students see a visual representation of Boyle’s law.

Other models allow students to investigate all the relationships of Charles’s law (V1T2=V2T1), Gay-Lussac’s law (P1/T1=P2/T2), and Avogadro’s law (V1/n1=V2/n2).

And, of course, all of these relationships together make up the Ideal Gas Law (PV=nRT). Explore gas laws today with some HTML5 molecular models!

Optimizing short-range and long-range atomic interactions

November 29th, 2012 by Piotr Janik

[Editor's note: Piotr Janik (janikpiotrek@gmail.com) was a Google Summer of Code 2012 student at the Concord Consortium and is now a consultant working on our Next-Generation Molecular Workbench.]

Some time ago we described the core engine used in Molecular Workbench and our attempts to speed it up. At that time we focused mainly on the low-level optimization connected with reducing the number of necessary multiplications. This promising early work encouraged us to think even more about performance.

We next reviewed existing algorithms in the core of the molecular dynamics engine. To make a long story short, atoms interact with each other using two kinds of forces:

  • Lennard-Jones forces (repulsion and short-range attraction)
  • Coulomb forces (electrostatic and long-range attraction)

Atomic interactions are pairwise, meaning that we have to calculate forces between each pair of atoms while using the basic, naive algorithm. Having n atoms, we must perform about n^2/2 calculations. “The Big O” notation can be used and the computational complexity can be described as O(N^2), which means that the execution time of calculations grows very fast as the number of atoms used in the simulation increases. This is definitely an unwanted effect, but fortunately there are ways to reduce the complexity.

Solutions are different for short-range and long-range forces, so let’s start with short-range. “Short-range” means that atoms interact only while they are quite close to each other. Let’s use rCut as a symbol for the interaction maximum distance. So, one obvious optimization would be to limit calculations to pairs of atoms that are closer to each other than rCut. How? There are two popular approaches—cell lists and Verlet (neighbor) list algorithms.

The cell lists algorithm is based on the concept that we can divide the simulation area into smaller boxes or cells. Each cell dimension is equal to the maximum range of interaction between atoms—rCut. So, while calculating interactions for a given atom, it’s enough to take into account only atoms from the same box and its closest neighbors. Atoms in other boxes are too far to interact with this atom. This is both simple and effective, reducing computational complexity to O(N)! Note that it’s C * O(N) with a pretty significant C, unfortunately.

However, while calculating interactions between atoms in neighboring cells, still only 16% of atoms that we take into account are interacting! This is a waste of resources and where we find room for further optimizations. So, what about creating a list for each atom, which contains only atoms actually interacting with it? This Verlet or neighbor list algorithm as it’s called works well. The only problem is that we have to be smart about updating these lists, as atoms constantly change their position and, thus, their “neighborhood.” We can slightly extend these lists to also include some atoms outside the area of interaction. So each list should include atoms closer than rCut + d from the given atom, where d defines a buffer area size. Because of that, lists need to be updated only when the maximum displacement of some atom, measured since the moment of the previous lists update, is bigger than d. If it’s smaller, neighbor lists are still valid. Lists can be updated using the normal, naive algorithm (which still leaves the complexity O(N^2)), or even better, using the cell lists algorithm presented above! This ensures complexity O(N) and greatly reduces inefficiencies of the cell lists approach.

We’re also working on long-range forces optimization. Since we can no longer use the assumption that atoms interact only when they are close to each other, we can’t rely on the optimization strategies above. The algorithms are now more complicated. The problem of the electrostatic interaction is akin to a problem of gravitational interactions (called N-body problem), popular in astrophysics. One of the most common algorithms for speed-up of such calculations is the Barnes-Hut algorithm. We tried to implement it, but the overhead connected with creating additional data structures was bigger than potential performance gains. The reason is that the number of charged atoms we use in our models is too small to see the advantage of such an approach. As a result, we left our naive algorithms for long-range interactions, which perform better due to their simplicity.

However, we successfully implemented both short-range optimizations in Next-Generation Molecular Workbench and the results are spectacular. The speed-up varies from 20% for really small models (where the number of atoms is less than 50) to 700% for bigger ones (where the number of atoms is about 250). This is the really significant improvement and made complex models usable. As you can see, conceptual, algorithmic optimizations really matter!

We’re still thinking about further optimizations, both low level and algorithmic. Stay tuned as the Next-Generation MW is getting more and more computational power!

Better than an Apple, a Gift for Teachers

November 27th, 2012 by Ethan McElroy

Thanks to everyone who entered our Suggest-a-Model contest. We always enjoy hearing from teachers and love to help with hard-to-teach science concepts. If you haven’t already, please vote for the model you’d most like us to build.

To Vote

1) Go to our Facebook page (you like us on Facebook already, right?)

2) Look for the poll pinned to the top left of the page’s wall

3) Click on the idea you like most to cast your vote

Our goal is to build a custom computer model to help teach a complex, science, math or engineering concept suggested by real teachers, like YOU! We know all too well the awkwardness of jumping up and down and waving your hands to model the behavior of molecules or dancing around the classroom to model photosynthesis.

We received a lot of great ideas and whittled the list down to three concepts.

One finalist told us that her students “are always making fun of me looking like I am doing a swim stroke in front of the class” when she tries to model convection! She’d love a new set of heat transfer models!

Another finalist is looking for a model of nutrient runoff into coastal waters and how that stimulates harmful algal bloom production. Concerned about the environment? Show your support for this model!

A model of meiosis and genetic recombination (known as crossing over, when exchanges of chromosome portions occurs) also made it to the top three. If you teach biology or know a student who’s taking Bio, this may be the one for you.

Voting ends on November 30th, so please go to our Facebook page and vote now.

After voting is over, we’ll announce the winner and get started on building the model. And once it’s done, it’ll be available for free to everybody. Win-win all around! If you want to know when it’s available, be sure to like us on Facebook, follow us on Twitter and subscribe to our mailing list and RSS feed. We’ll be posting about it through all those channels.

But don’t wait to use our models. Check out our Activity Finder and Classic MW. These free resources contain lots of great examples of the models we already have available for science, math and engineering teachers at all grades. You’re sure to find an activity (or two or three!) that covers other difficult-to-teach concepts. Enjoy!