New machine-learning simulations reduce energy need for mask fabrics, other materials

Making the countless numbers of N95 masks that have protected millions of Americans from COVID requires a process that not only demands attention to detail but also requires lots of energy. Many of the materials in these masks are produced by a technique called melt blowing, in which tiny plastic fibers are spun at high temperatures that necessitate the use of a lot of energy. The process is also used for other products like furnace filters, coffee filters and diapers.

Thanks to a new computational effort being pioneered by the U.S. Department of Energy’s (DOE) Argonne National Laboratory in conjunction with 3M and supported by the DOE’S High Performance Computing for Energy Innovation (HPC4EI) program, researchers are finding new ways to dramatically reduce the amount of energy required for melt blowing the materials needed in N95 masks and other applications.

Currently, the process used to create a nozzle to spin nonwoven materials produces a very high-quality product, but it is quite energy intensive. Approximately 300,000 tons of melt-blown materials are produced annually worldwide, requiring roughly 245 gigawatt-hours per year of energy, approximately the amount generated by a large solar farm. By using Argonne supercomputing resources to pair computational fluid dynamics simulations and machine-learning techniques, the Argonne and 3M collaboration sought to reduce energy consumption by 20% without compromising material quality.

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