Archive for June 2012

Investigating the Kármán vortex street using Energy2D

June 30th, 2012 by Charles Xie
Run this simulation.
The Kármán vortex street is a repeating pattern of swirling vortices caused by the unsteady separation of flow of a fluid over bluff bodies. It is named after the great scientist Theodore von Kármán who co-founded NASA's JPL. This effect is observable in nature like in a stream, but you need some luck since it requires some picky conditions that are not always there for you.

Now, with our online simulation program Energy2D you can create and investigate the Kármán vortex street in your browser without depending on Mother Nature to give you an opportunity window.

For example, you can test how big an obstacle should be in order to produce this effect. You will find that an obstacle must be large enough to create a steady vortex street. If the shape of the obstacle is not streamlined, what will you see?

If you stick a thermometer in a thermal vortex street, you should see that the temperature will swing pretty regularly between a high value and a low value (see the image to the right). This means this effect could be used to warm and cool an array of things periodically. Could there be some engineering use of this?

Summer vacation for teachers: The perpetual search for new and improved ways to teach

June 26th, 2012 by Dan Damelin

For 14 years I was a teacher at Lincoln-Sudbury Regional High School. Today is the first day of summer vacation for my friends and colleagues there. Now I work at the Concord Consortium, but I remember fondly that day when all the grades had been turned in and “vacation” began. I usually took a couple of weeks to crash and recuperate, but contrary to the understanding of most non-teachers, many teachers spend a significant portion of their summer writing curricula, going to conferences, getting ready for the upcoming year and continuing that perpetual search for new and improved ways to teach.

I taught chemistry and one of the most difficult things I had to deal with was the fact that pretty much everything we explored had to do with atoms and molecules too small to see. Somehow chemistry students need to find a way to imagine a world full of uncountable, invisible particles flying around at blistering speeds, colliding, reacting, attracting and repelling. For the past 10 years we have been developing a piece of software to address this challenge–the Molecular Workbench. During that time I worked with the MW team to create simulation-based activities that give students a concrete handle for thinking about the atomic-level world. Using these activities, students do virtual experiments with atoms and molecules, push and prod them, change the parameters of various simulations and build their own mental model of the atomic foundation of the world around them.

Do you struggle with this issue or just want to “play” with some molecules? Below are a few of my favorites:

Check out the huge collections of models and activities found at and be sure to take a look at our latest work in making the Molecular Workbench run in the browser without the need for Java.

Have a great summer. May it be both relaxing and productive. :-)

A Window on Your Educational Soul

June 20th, 2012 by Dan Barstow

Music playlists are windows to the soul (or so the general wisdom goes). Your taste in music, including your personal favorites, reveals much about your personality, your lifestyle and your values. So too your favorite websites.

Do you bookmark your favorite websites in categories or sequence the most important ones first? Have hundreds of favorites? Or a top ten list? However you manage them, your Web, music and other playlists are an essential part of keeping your technical life efficient and effective.

Such a “playlist mentality” is becoming increasingly clear in education. And the teachers we’ve interviewed have requested personal playlists of Molecular Workbench activities and models.

Needless to say, each teacher has his or her own style of teaching, manifested in how they use MW. Some use it mostly for classroom demos, others focus more on direct student use. Some use MW just a few times during the year, others almost weekly. Some use it during class time, others assign MW activities for homework.

Teachers also vary in how they sequence topics during the year. One chemistry teacher might teach organic chemistry late in the year as a synthesis while another might teach it earlier as a way to whet the appetite and spark questions.

The MW development team confronted this diversity challenge (and opportunity) early on. It was clear we could not create, for example, a single master flow of MW activities for high school chemistry. Instead, we need to support differences among teachers by providing a rich variety of activities. Teachers can select, adapt and integrate models and activities into their own style and classroom flow.

As we’ve been documenting in videos and articles, we are now creating a Web-based Molecular Workbench system, which will give teachers more flexibility in how they select and sequence the activities. At the simplest level, they’ll be able to use standard browser tools to bookmark their favorites, organizing them in whatever flow they want. And our teacher interviews have pushed us even further in thinking about design.

Our goal is to include the ability for teachers to store personal MW playlists – with even more information than you get in a bookmark, such as level of difficulty and duration of activity. Teachers will be able to provide playlists to students or share them with colleagues.

We still have to work out the details, but we’ve heard from teachers and high on their wish list is a personal playlist with lots of flexibility to meet their needs and style. That’s the best way to make sure MW is effective and widely used!

Video: Under the Hood of Molecular Workbench

June 15th, 2012 by The Concord Consortium

It takes a lot of computation to model the atomic and molecular world! Fortunately, modern Web browsers have 10 times the computational capacity and speed compared with just 18 months ago. (That’s even faster than Moore’s Law!) We’re now taking advantage of HTML5 plus JavaScript to rebuild Molecular Workbench models to run on anything with a modern Web browser, including tablets and smartphones.

Director of Technology Stephen Bannasch describes the complex algorithms that he’s been programming behind the scenes to get virtual atoms to behave like real atoms, forming gases, liquids and solids while you manipulate temperature and the attractive forces between atoms. See salt crystallize and explore how the intermolecular attractions affect melting and boiling points. Imagine what chemistry class would have been like (or could be like today) if the foundation of your chemical knowledge started here.

Technology and Curriculum Developer Dan Damelin goes on to describe how open source programming opens up possibilities. For instance, Jmol is a Java-based 3D viewer for chemical structures that we were able to incorporate into Molecular Workbench to allow people to easily build activities around manipulation of large and small molecules, and to make connections between static 3D representations and the dynamic models of how molecules interact. We’re planning to build a chemical structure viewer that won’t require Java and will extend another open source project based on JavaScript and WebGL to visualize molecules in a browser.

Interested in this innovative programming? Great! We’re looking for software developers.

Embedding Next-Generation Molecular Workbench

June 7th, 2012 by Dan Barstow

The next-generation Molecular Workbench has a fundamental feature that is both simple and profound: MW models will be embeddable directly in Web pages. This simple statement means that anyone will be able to integrate these scientifically accurate models into their own work—without having to launch a separate application. Teachers will embed MW models and activities into their own Web pages. Textbook publishers will embed them in new e-books.  There is much room for creativity and partnerships here.

The significance of this advance struck me at a recent conference on educational technology sponsored by the Software & Information Industry Association. Many creative people and companies attended, from large publishers to innovative startups. Throughout the presentations and conversations, I envisioned ways these potential partners might use MW to enhance their products and services.

Ron Dunn, CEO of Cengage, gave a keynote describing their new digital textbooks and aligned homework helpers and other digital resources. He pointed out that 35% of their sales are “digitally driven,” and that technology is essential to their future. Other major publishers echoed those messages. When publishers embed Molecular Workbench models and activities throughout their e-books as a consistent modeling environment, students will be able to investigate fundamental principles of chemistry, physics and biology more deeply than the simple animations and videos now so typical in e-books.

SmartScience is a startup, developing supplemental science education activities. Their idea to link videos of science phenomena with corresponding graphing tools is clever. For example, in a time-lapse video of rising and falling tides, students mark the ocean height and automatically see their data in a graph in order to understand both the scientific phenomena and the graph output. Augmenting reality is great, and we love the idea of integrating videos of physical, chemical and biological processes at the macroscopic scale with MW models to show what happens at the microscopic scale.

Karen Cator, Director of the Office of Educational Technology at the U.S. Department of Education, discussed a new framework for evaluating the effectiveness of educational technology projects. Software can monitor how students work their way through online problems, providing teachers with deeper insights on student learning, especially in terms of scientific thinking and problem-solving skills. Teachers can focus on students’ higher-level thinking skills, and provide useful, real-time feedback to identify strengths, progress and areas in need of help. We agree whole-heartedly and have been working on ways to capture student data in real time and provide feedback loops for teachers. Our next-generation Molecular Workbench will record what students do as they explore the models and make that information available to teachers and researchers.

Partnerships with creative teachers, publishers, and software developers will help us ignite large-scale improvements in teaching and learning through technology. That’s our mission and our goal for Molecular Workbench. Thanks to Google funding, we’re working to increase access to the incredibly powerful next-generation Molecular Workbench.


A Datasheet for NextGen MW

June 4th, 2012 by Richard Klancer

The opposite of Thomas Dolby

I was terrible at the first four weeks of organic chemistry. I just couldn’t get the right pictures into my head.

The depictions of the chemical reaction mechanisms I was supposed to memorize seemed like just so many Cs (and Hs and Os and, alarmingly, Fs) laid out randomly as if I were playing Scrabble. And I swear the letters rearranged themselves every time I looked away, like a scene out of a movie about an art student’s science-class nightmares (minus the extended fantasy sequence in which the letters grow fangs and leap off the page to menace the poor protagonist – unless I’ve blocked that part out).

Fortunately, I knew exactly what to do: I had to start picturing molecules in 3D, and in motion, as soon as possible. That ability seemed to take its own sweet time to develop. But once things “clicked” and I could visualize molecules in motion, the reactions finally made sense, as did all the associated talk of electronegativity, nucleophilic attack, and inside-out umbrellas. I aced the final.

Now, our Molecular Workbench software isn’t specifically designed to help undergraduates get through organic chemistry. It is designed to help students at many levels by letting them interact with simulations of the molecular world so they get the right pictures into their heads, sooner. It’s here to help that future art student and movie director beginning to nurse a complex about the 10th grade science class he’s stuck in right now.

The weight of history

But the “Classic” Molecular Workbench we have now was built for a different world. It runs in desktop Java, for one thing, meaning (among other things) that it’ll never run on iPads. More fundamentally, it was built to be “Microsoft Word for molecules” in a time when Microsoft Word was the dominant model for thinking about how to use a computer:

“Hello, blank page! Let’s see, today I’ll make a diffusion simulation. I should write something about it … Let’s make that 12-point Comic Sans. No, my graphic designer brother-in-law keeps telling me not use that so much, so Verdana it is, then. Now how do I add that model again? Oh yeah, Tools -> Insert -> Molecular Model…”

This model is constraining even though it’s always been possible to download and open Molecular Workbench via the Web, and even though MW saves simulation-containing activities to special URLs.

We have somewhat different expectations these days because of the Web, social media, mobile apps, and casual games. If I build a great in-class “activity” based on a series of molecular models, then I should be able to share that activity with the world with minimum difficulty. And if you find one of the simulations I created particularly illustrative, you should be able to put that model in a blog you control, or include the model as part of your answer to a question on

Moreover you ought to be able to perturb the running simulation by reaching out and touching it with your fingers, or simply by shaking your tablet to see what effect that has on the simulation “inside” it. You shouldn’t be required to operate the simulation at one remove, via a mouse and keyboard, when it’s not necessary.

That’s why we’re excited about the Google-funded, next-generation Molecular Workbench we have started to build. The HTML5 + JavaScript technology we’re using to build the next generation of our MW software (hereafter called NextGen MW for short) will make it much more practical to enable these kinds of uses.

Boldly doing that thing you should never do

But designing NextGen MW to be a native of the real-time Web of 2012 rather than a visitor from the land of 1990s desktop computing means that we’re committed to rebuilding the capabilities of “Classic” Molecular Workbench from scratch. That is, we’re doing the very thing Joel Spolsky says you must never do! But ignoring platforms which run Java badly or not at all isn’t an option, and neither is trying to run Classic MW in a Google Web Toolkit-style compatibility layer that compiles Java to JavaScript. (With the latter option, we would almost surely be unable to optimize either the computational speed or the overall user experience well enough to make it practical to use NextGen MW on phones, inexpensive tablets, or even expensive tablets. But even that misses the point. We’re not a consumer products company trying to optimize the return on our past investment. We’re an R&D lab. We try new things.)

But writing things from scratch poses a challenge. We want the molecular dynamics simulations run by NextGen MW to run “the same” as the equivalent simulations run in Classic MW. But “the same” is a slippery concept. In traditional software development, asking two different implementations of a function or method to produce the “same” result often means simply that they return identical data given identical input, modulo a few unimportant differences.

It would be nice to extend this idea to the two-dimensional molecular dynamics simulations we are now implementing in NextGen MW. Classic MW doesn’t have a test suite that we can simply adapt and reuse. But, still, we might think to set up identical initial conditions in NextGen MW and Classic MW, let the simulations run for the same length of simulated time, and then check back to make sure that the atoms and molecules end up in approximately the same places, and the measurements (temperature, pressure, etc.) are sufficiently close. And, voilà, proof that at least this NextGen MW model works “the same” as the Classic MW model. (Or that it doesn’t, and NextGen MW needs to be fixed.)

Never the same thing twice?

Unfortunately, this won’t work. Not even a little bit, and the reason is kind of deep. The trajectories of the particles in a molecular dynamics simulation (and in reality) exhibit a phenomenon known as sensitive dependence on initial conditions. Think of two identical simulations with exactly the same initial conditions except a tiny difference. Now, pick a favorite particle and watch “the same” particle in each simulation as you let the simulations run. (And assume the simulations run in lockstep.) For a very short time, the particle will appear to follow the same trajectory in simulation 1 as in simulation 2. But as you let the simulation run a little longer, the trajectories of the two particles will grow farther and farther apart, until, very quickly, looking at simulation 1 tells you nothing about where to find the particle in simulation 2.

Very well, you say: maybe simulation 1 and simulation 2 started a little too far apart. So let’s make the difference in the initial conditions a little smaller. Sure enough, the trajectories stay correlated a little bit longer. But a very little bit. Here’s the rub: if you want to simulation 2 to match simulation 1 for twice as long, you need the initial conditions to be some number, let’s say 10, times closer. But if you need the simulations to match for 1 more “time” as long, that is, 3 times as long, you need the initial conditions to be 10 times closer still, or 100 times closer. And if you want simulation 1 to make a meaningful prediction about simulation 2 for ten times as long? Now you need the initial conditions to be a billion(109) times closer. In practice, this means that if there’s any difference at all between the two initial conditions, no matter how seemingly insignificant, then outside of a short window of time the two simulations will predict very different particle locations and velocities.

Perhaps you think this is a contrived situation having nothing to do with comparing Classic MW and NextGen MW. Can’t we start them with, not just similar, but identical initial conditions? Unfortunately, this escape hatch is barred, too. The tiniest and most seemingly insignificant difference between the algorithms NextGen MW runs and the algorithms Classic MW runs right away result in a small difference in the trajectories, and after that point, sensitive dependence on initial conditions takes over: the subsequent trajectories soon become totally different. Trying to run precisely the same algorithms in NextGen MW as in Classic, down to the exact order of operations, would not only intolerably constrain our ability to develop new capabilities in NextGen MW, but would be futile: the differing numerical approximations made by Java and JavaScript would result in yet another small difference which would in short order become a big difference.


So, wait a minute: You can’t test NextGen MW against Classic MW because even the tiniest difference between them makes them behave … totally differently? How do we trust either program, then? And how is this science again?

Well, notice that I didn’t say quite say the two programs behave totally differently. Yes, the exact trajectories of the molecules will quickly diverge, but the properties we can actually measure in the real world — temperature, pressure, and the like — unfold according to laws we understand, and should be the same in each (not counting minor, and predictable, statistical fluctuations.) After all, we can do beautifully repeatable experiments on “molecules in a box” in the real world without knowing the location of the molecules exactly. Indeed, when van der Waals improved on the ideal gas law by introducing his equation of state, which includes corrections for molecular volume and intermolecular attraction, the notion that molecules actually existed was not yet universally accepted.

So what we need are molecular models whose temperature, pressure, diffusion coefficient, heat capacity, or the like depend in some way on the correctness of the underlying physics. Ideally, we would like to be able to run a Classic MW model and have it reliably produce a single number which (whatever property it actually measures) is demonstrably different when the physics have been calculated incorrectly. Then we could really compare NextGen MW and Classic MW — and perhaps even find a few lingering errors in Classic MW!

Unfortunately for this dream, our library of models created for Classic MW tend to be complex interactives which require user input and aim to get across the “gestalt” of molecular phenomena (e.g., one model encourages students to recognize that water molecules diffusing across a membrane aren’t actively “aiming for” the partition with a higher solute concentrations but move randomly). The models are not intended to be part of numerical experiments designed carefully to produce estimates otherwise-difficult-to-measure properties of the real world. They require substantial rework if they are to generate single numbers that are known to reliably test the physics calculations. For that matter, there aren’t many Classic models at all that conveniently limit themselves to just the features we have working right now in NextGen MW, and we can’t just wait until we develop all the features before we begin testing.

Charts and graphs that should finally make it clear

Therefore, we have turned to making new Classic MW models that demonstrate the physics we want NextGen MW to calculate, and comparing the numbers generated in Classic MW to the numbers generated when the equivalent model is run in NextGen MW. I’ve begun to think of this process as creating the “datasheet” for Classic and NextGen MW, after the datasheets which contain charts and graphs detailing the performance characteristics of an electronics part, and which an engineer using the part can expect it to obey.

So far, we’ve just gotten started creating the MW datasheet. I’ve written a few ugly scripts in half-remembered Python to create models and plot the results and so far, sure enough, it looks like an issue with the NextGen MW physics engine that I knew needed fixing, needs fixing! (The issue is an overly clever, ad hoc correction I introduced to smooth out some of the peculiar behavior of our pre-existing “Simple Atoms Model.” But that’s good fodder for a future blog post.)

But we have ambitions for these characterization tests. Using the applet form of Classic MW, we hope to make it possible to run each of these “characterization tests” by visiting a page with equivalent Classic and NextGen MW models side by side, with output going to an interactive graph. But with or without this interactive form of the test, once characterization tests have been done they will help us to find appropriate parameters for automated tests that will run whenever we update NextGen MW, so that we can be sure that the physics remain reliable.

I’ll update you as we make progress.

YouTube Physics features our infrared videos

June 1st, 2012 by Charles Xie
AAPT's Physics Teacher runs a column called YouTube Physics edited by Diane Riendeau, an award-winning physics teacher. In May, the entire column featured five intriguing YouTube videos from our IR website and recommended instructional strategies to use them effectively in the classroom.

Diane recently wrote about the YouTube Physics Column: "Through the use of YouTube, we can show our students demos that we do not have the capability of doing in class. We can use these videos to inspire them and show them some of the cutting-edge discoveries in our field. We can also show them videos from around the world. Students need to realize that the physics community is global, not just national. They should learn to marvel in the discoveries made by physicists from all nations."

We resonate with her vision, which is why we are publishing our IR videos on YouTube to allow students from all over the world to learn thermodynamics, heat transfer, chemistry, and other science subjects in everyday phenomena through IR vision. In the long run, we hope this effort will give birth to an "IRTube" that collects IR views of many scientific phenomena. With the introduction of thermal imaging technology into the classroom, we hope students will begin to upload their own IR videos to the IRTube. Darren Binnema, a student from the King's University College in Edmonton, Canada, has contributed the first IR video to the "IRTube." His IR video visualizes the heat of solutions of NaOH and KCl (see the above image).

For more IR videos, please visit the IRTube website.