New machine-learning simulations scale back power want for masks materials, different supplies







Nov 01, 2022 (Nanowerk Information) Making the numerous numbers of N95 masks which have protected hundreds of thousands of People from COVID requires a course of that not solely calls for consideration to element but additionally requires a lot of power. Lots of the supplies in these masks are produced by a way referred to as soften blowing, wherein tiny plastic fibers are spun at excessive temperatures that necessitate the usage of a variety of power. The method can also be used for different merchandise like furnace filters, espresso filters and diapers. Because of a brand new computational effort being pioneered by the U.S. Division of Vitality’s (DOE) Argonne Nationwide Laboratory together with 3M and supported by the DOE’S Excessive Efficiency Computing for Vitality Innovation (HPC4EI) program, researchers are discovering new methods to dramatically scale back the quantity of power required for soften blowing the supplies wanted in N95 masks and different purposes. At present, the method used to create a nozzle to spin nonwoven supplies produces a really high-quality product, however it’s fairly power intensive. Roughly 300,000 tons of melt-blown supplies are produced yearly worldwide, requiring roughly 245 gigawatt-hours per yr of power, roughly the quantity generated by a big photo voltaic farm. Through the use of Argonne supercomputing assets to pair computational fluid dynamics simulations and machine-learning methods, the Argonne and 3M collaboration sought to scale back power consumption by 20% with out compromising materials high quality. The soften blowing course of makes use of a die to extrude plastic at excessive temperatures. Discovering a approach to create an identical plastic elements at decrease temperatures and pressures motivated the machine-learning search, stated Argonne computational scientist Benjamin Blaiszik, an writer of the examine. “It’s sort of like we try to make a pizza in an oven — we’re looking for the correct dimensions, supplies for our pizza stone, and cooking temperature utilizing an algorithm to reduce the quantity of power used whereas conserving the style the identical,” he stated. Through the use of simulations and machine studying, Argonne researchers can run a whole lot and even hundreds of use instances, an exponential enchancment on prior work. “We have now the flexibility to tweak issues just like the parameters for the die geometry,” Blaiszik stated. “Our simulations will make it doable for somebody to make an merchandise at an precise industrial facility, and our pc can inform you about its potential for real-world purposes.” The simulations present key insights into the method, a way to evaluate a mixture of parameters which might be used to generate information for the machine-learning algorithm. The machine-learning mannequin can then be leveraged to finally converge on a design that may ship the required power financial savings. As a result of the method of constructing a brand new nozzle could be very costly, the data gained from the machine-learning mannequin can equip materials producers with a approach to slim right down to a set of optimum designs. “Machine-learning-enhanced simulation is one of the simplest ways of cheaply getting on the proper mixture of parameters like temperatures, materials composition, and pressures for creating these supplies at top quality with much less power,” Blaiszik stated. The preliminary mannequin for the melt-blowing course of was developed by a collection of simulation runs carried out on the Theta supercomputer on the Argonne Management Computing Facility (ALCF) with the computational fluid dynamics (CFD) software program OpenFOAM and CONVERGE. The ALCF is a DOE Workplace of Science person facility positioned at Argonne.


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