Lee Rannals for RedOrbit.com
New research from the Massachusetts Institute of Technology (MIT) may have an impact in a range of industries, from engineering to pharmaceutical manufacturing.
Ken Kamrin of MIT’s Department of Mechanical Engineering has created a model that predicts the flow of granular materials under a variety of conditions.
Sand’s behavior, which is part fluid, part solid, has made it difficult for researchers to predict how it and other granular materials flow under various conditions.
Having a better model of how sand flows could help optimize processes like pharmaceutical manufacturing and grain production.
When making pills, grains pour through industrial chutes and silos in great quantities, which can lead to blockages that can be costly and dangerous.
Now, Kamrin’s model is able to make predictions of where grains will flow, whether through chutes or in a circular trough, in a near-perfect match with actual results.
“This kind of versatility could enhance the modeling of soil and debris transport phenomena from rapid landslides all the way down to steady long-term soil creep, or intermediate cases where rapid and slow zones might occur simultaneously in different locations,” Kamrin told RedOrbit.
Ken said his “Eureka!” moment during the research was the realization that a simple nonlocal framework presented for flowing emulsions actually has the necessary form that allow scientists to see “several tricky phenomena that had been observed in granular flows.”
“The fact that emulsions and grains might behave similarly is intriguing but not that unexpected since they share some key microstructural similarities,” he said.
According to Kamrin, they are both amorphous, and are composed mostly of randomly packed objects.
He said one of the major contributions of this model was a nonlocal framework. This type of model becomes more and more important as the flow rate slows down, according to Kamrin.
“The nonlocal picture describes how the motion of a grain can be directly influenced by nearby grain movements rather than the local stresses alone, which could improve the modeling of various granular-geological phenomena especially on geological time-scales,” he told RedOrbit.
Ken modified equations for existing continuum models to factor in grain size, and then tested his model on several configurations.
The new model not only predicted areas of fast-flowing grains, but was also able to predict where grains would be slow moving, at the very edges of each configuration.
The new model’s predictions matched closely with particle-by-particle simulations in the same configurations.
He said engineers could test shapes of chutes and troughs in the model to help them find geometry that maximizes flow, or mitigates dangerous wall pressure, before designing or building equipment to process granular materials.
His model could help geologists understand landslides and avalanches, and help engineers come up with new ways to generate better traction in sand.
Ken said one industry that most may not think would benefit from this model would be the nuclear power industry.
“Recent disasters such as the Fukushima nuclear meltdown have highlighted international efforts toward the design of “meltdown-proof” pebble-bed reactors,” he told RedOrbit. “In pebble-bed reactors, the nuclear fuel is encased in many billiard-ball sized pebbles, and over the course of months, the packing of pebbles slowly drain through the reactor like a gumball machine.”
He said one of the engineering difficulties is trying to track the locations of all the pebbles within the reactor during the slow draining process.
“Continuum models like the one we are presenting could address this by predicting the average motion of the pebbles within the reactor, regardless of the shape, fill-level, or out-flow rate of the reactor vessel,” Kamrin said.
Ken, as well as colleagues who worked on the project with him, published the paper detailing the new model in the journal Physical Review Letters.
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