They modeled the transition from the current high-carbon emitting energy system to low-carbon or “green” technologies in an effort to see how that transition will affect climate change. The findings show that there’s no quick fix. Energy-induced climate change will get worse before it gets better and combating it will be a multi-generational process. Even if the world switched to a perfect technology with zero emissions, carbon already emitted into the atmosphere will still cause substantial temperature increases over the next 40 years before progress can be made. To minimize the damage, we will need a faster, larger-scale transition to extremely low-carbon energy sources like solar, wind and nuclear.

The paper has been several years in the making as the authors developed mathematical techniques sensitive enough to measure the variables involved. View the figures and watch Ken Caldeira discuss their method and findings in the video below.

Some mosquitoes are unfortunately highly anthropophilic, meaning they seek out humans for their blood meals rather than other animals. But how do these insects find us in the first place? How do they distinguish between a human and a shrub? There is evidence that odor may have something to do with it.

Some smells are stronger attractants than others. In order to test this idea, the guys in the machine shop built the olfactometer you see above. The device allows a cage of mosquitoes to be secured on the base of the Y, and receiving cages with stimuli on each arm. The swarm is released, presenting each insect with a Frostian decision. This puts each smell, or combination of smells in a head to head showdown for mosquito palatability.

Yet, determining the most popular stimulus is just one use of this device. The olfactometer can also filter groups of mosquitoes based on their olfactory preferences. We can separate those that are more strongly drawn to cow scent from those more drawn to human scent, and set up further experimentation for each specific population.

There are several other notable features of the olfactometer. Gates at the entrance/exit points (1st & 3rd photos) allow control over the release of both mosquitoes and scent. There is also hose attachment to anesthetize the insects with CO2 (3rd photo). A computer fan is attached to the cage at the base to provide air current, distributing the odor from the stimulus cages into the stock cage (4th photo).

Big thanks go to thank Tom Donaldson for his diligent work, and Ryan Smith for fabulous photography.

Here’s an example using simplified numbers. Imagine you have 4 cores and each can run 10 instructions per second (IPS). Potentially, your machine could carry out a set of 100 instructions in 2.5 seconds. (100 instructions divided evenly among 4 processors=25 each; divided by 10 IPS=2.5 sec) However, computers don’t intuitively know how to divvy up tasks. Worst case scenario, all the instructions get sent to one core while the others are left idle. This would take the job 10 seconds to process, which is 4 times as long. The practice of optimizing the distribution of the work load is known as load balancing. When epidemiological simulations are taking hours, days and weeks to process across thousands of cores, effective load balancing becomes crucial.

Applying Load Balancing to Epidemiological Modeling

A few months ago, we received the results of the static load balancing script, which makes sure that each core does an equal amount of work over the epidemiological simulation of Madagascar . While doing further analysis, we found that each core was also spending considerable time waiting for other cores, even though the static load balancing did provide substantial efficiency improvements. This waiting time is due to dynamic load imbalances that average to zero.

Below is an estimation of how we initially split up Madagascar along with the figure of the corresponding relative load by core over time, with no load balancing other than giving equal land areas to each processor.

Equal land area load distribution

Notice the big gap between cores 30 and 50. In this approach, where the core/processor number corresponds to a specific block region on the map, 30-50 are doing a lot less work than the cores. This is due to the island’s central plateau, which has very little malaria and thus is an inexpensive simulation. In addition, there are also varying dips along the time axis depending on the location. These are caused by seasonal differences across the country. The north is always fairly expensive, while the southern arid region has deep troughs during dry season. In a sense, the spatio-temporal patterns of malaria from the north to south of Madagascar are visible in processor loading.

These inconsistencies informed our static load balancing strategy where we divided Madagascar by average malaria prominence. This approach improves matters, and all core rows add up to the same value, but the columns differ at each point in time.

Static load balancing by average malaria prominence

Much better, and the overall job runs much faster, since the more even processor loading reduces the waiting time.

“Loading” is a relatively smooth function of location, however, also dependent on population density, temperature and rainfall. These factors vary over distances that are much larger than our simulation resolution. So if we grid off the country and divvy up “pixels” between cores, then every core has an equal sample of every area in Madagascar. This results in automatic static and dynamic load balancing.

Checkerboard balancing

However, there is an overhead cost to checkerboard balancing. With large blocked regions, such as in static load balancing, almost all migration of individuals happens within one of these large blocks and thus within a single core, being handled in local memory with pointers. With a checkerboard, almost all migration is to other cores, and individuals and their infections must be packed, sent in a message, and unpacked. Over the course of a 90 minute simulation, this adds about 6 minutes of overhead. Yet the elimination of waiting time at synchronization gains time, and the even balancing of the load improves efficiency as well.

At the moment, both static load balancing which avoids the migration overhead, and checkerboard which eliminates processor waiting time both provide good improvements over the basic simulation efficiency.

We are working on dynamic load balancing that keeps areas connected and dynamically balanced. This has the potential to achieve both types of efficiency improvements with less overhead. We’ll report back when we have the results.

Physicists and engineers often use dimensionless numbers to help talk about and characterize complex phenomena. When it comes to turbulence, at the heart of every discussion is the Reynolds number, named after the British fluid dynamicist Osborne Reynolds. The Reynolds number measures the relative importance of inertial forces to viscous forces in any flow, so an increased Reynolds number corresponds to dominating inertial forces, which results in more chaotic behavior.

Another important factor is the patterns or texture of the surface over which the flow takes place. Shark skin, with its small riblet structures aligned with the motion direction, interacts with the turbulent flow of water slipping past in ways that reduce drag on the animal, enabling it to be a fast swimmer. Dimpled surfaces also interact with turbulent flows in interesting ways. The surfaces of golf balls work to decrease drag and increase the flight distance.

Usually, any feature that results in increased turbulence, improves mixing at the expense of a significant increase in fluidic drag. A ceiling fan stirs up the air in a room. If you want more air mixing you need to crank up the speed, but this will make the motor work harder. How hard the motor is working is a measure of the fluidic drag. Thus, increased drag is usually undesirable because it requires more power to drive the motion.

Under certain conditions however, dimples on the walls of a conduit can be arranged in such a way that they dramatically improve mixing with only little increase in drag. Such designs find applications in heat exchangers, where improved mixing results in higher heat exchanger efficiencies without paying a proportionally large penalty on the pumping costs.

Above, we show results of high performance computational fluid dynamics simulations of turbulent air flow over dimpled surfaces. Even though the flow would be turbulent in the absence of dimples, presence of dimples gives rise to the formation of additional structures in the flow. For example, rolling vortices shedding off the dimpled cavities are evident in the 1st clip (moderate Reynolds number), which help improve mixing. The second clip examines the variation of flow velocity in just a vertical slice of the total flow, that also occurs at a lower speed. The last video shows a very similar flow but at a faster flow speed (high Reynolds number).

TED2010 has long past. The presentation went off without a hitch, and that can be attributed to oodles of preparation. A dozen people were tapped to ready the Photonic Fence for its first public demo. There was a ton to do: finalizing the software with a handsome interface, constructing custom casing and mounts for the hardware, breeding hoards of backup mosquitoes, testing, tweaking, testing, tweaking…you get the idea. With so many scrambling to cross off hundreds of tasks, this easily could have turned into a formidable debacle.

In order to streamline workflow, 3ric Johanson pulled a tool from the extreme programmer‘s handbag. Story cards are a visual way to organize tasks. Each item is placed on its own card and clustered based on topic, delegation, sequence, or whatever is appropriate in the moment. Dependencies between tasks are then shown using green arrows. The approach is flexible as items are easily edited or moved, while providing an always up to date big picture for all involved.

The board was hung in a prominent place in the Lab. As to-do’s were completed, challenges revealed and priorities shifted, the board underwent constant evolution: a tapestry of note cards fluctuating, receding, diverging, with old worn cards giving way to sprightly, fresh, new ones. Though more impressive was the team’s ability to stay focused and agile. Ultimately the demonstration garnered a standing ovation at one of the most popular conferences today. So the next time you’re facing down a beastly project, give story carding a try and report back with your findings.

If you want to see a video summary of all the prep, check out Getting Ready for TED

However, not all vortexes are born free. Forced vortexes, on the other hand, consist of fluid rotating together as a single mass. There’s no shearing, at least in theory*, and therefore no mixing.

*…but only under perfect conditions: complete uniformity in the fluid material, consistant rotation, no air friction, etc.

Thanks to Torley for the improvised piano.

So this week I set out to see what I could do with Rheinberg illumination. I was transported back to 3rd grade as I drew, cut, colored and taped together little pieces of transparent things found around the lab: folders, transparencies, left over birthday decorations, you name it. To the left, are some of the filters I came up with.

The next task was to see what kind of images I could make. Here is a piece of something organic illuminated with normal bright-field and a few different Rheinberg filters. Can you match the images to their corresponding filters?

The next target was blood cells. Here, the image on the left uses a purple center filter, while the right one uses a dark field-like patch stop in the center with a colored outer filter. Notice the differences in highlighting.

Even though I got some good images, and enjoyed the exploration, it turns out Rheinberg illumination isn’t exactly what I’m looking for. Back to the drawing board =)

Grant Crilly in the kitchen mixed up a batch of oobleck (cornstarch and water) the other day. Being a shear thickening fluid, the material was runny, and you could easily run your hand through a vat of the stuff as long as you moved slowly. However, a quick jolt will turn the oobleck into a near solid, bringing the mixing hand to a stop. Stress or agitation increases the viscosity. If this stress is applied in a uniform manner, under certain frequencies, otherworldly behavior results. For our demonstration, a subwoofer hooked up to a frequency generator did the trick.

music by niteffect

YouTube goodness:
Running on a pool of oobleck
Creeping oobleck
More dancing oobleck