HomeNanotechnologyNew machine-learning simulations scale back vitality want for masks materials, different supplies

New machine-learning simulations scale back vitality 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 Individuals from COVID requires a course of that not solely calls for consideration to element but in addition requires numerous vitality. Lots of the supplies in these masks are produced by a method referred to as soften blowing, wherein tiny plastic fibers are spun at excessive temperatures that necessitate using a number of vitality. The method can be used for different merchandise like furnace filters, espresso filters and diapers. Due to a brand new computational effort being pioneered by the U.S. Division of Power’s (DOE) Argonne Nationwide Laboratory at the side of 3M and supported by the DOE’S Excessive Efficiency Computing for Power Innovation (HPC4EI) program, researchers are discovering new methods to dramatically scale back the quantity of vitality required for soften blowing the supplies wanted in N95 masks and different functions. At the moment, the method used to create a nozzle to spin nonwoven supplies produces a really high-quality product, however it’s fairly vitality intensive. Roughly 300,000 tons of melt-blown supplies are produced yearly worldwide, requiring roughly 245 gigawatt-hours per 12 months of vitality, roughly the quantity generated by a big photo voltaic farm. Through the use of Argonne supercomputing sources to pair computational fluid dynamics simulations and machine-learning methods, the Argonne and 3M collaboration sought to scale back vitality 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 method to create similar plastic parts at decrease temperatures and pressures motivated the machine-learning search, mentioned Argonne computational scientist Benjamin Blaiszik, an creator of the examine. “It’s sort of like we are attempting to make a pizza in an oven — we’re looking for the fitting dimensions, supplies for our pizza stone, and cooking temperature utilizing an algorithm to attenuate the quantity of vitality used whereas conserving the style the identical,” he mentioned. Through the use of simulations and machine studying, Argonne researchers can run a whole lot and even 1000’s of use instances, an exponential enchancment on prior work. “We now have the power to tweak issues just like the parameters for the die geometry,” Blaiszik mentioned. “Our simulations will make it potential for somebody to make an merchandise at an precise industrial facility, and our laptop can inform you about its potential for real-world functions.” The simulations present key insights into the method, a technique to evaluate a mixture of parameters which might be used to generate knowledge for the machine-learning algorithm. The machine-learning mannequin can then be leveraged to in the end converge on a design that may ship the required vitality financial savings. As a result of the method of constructing a brand new nozzle may be very costly, the data gained from the machine-learning mannequin can equip materials producers with a method to slender right down to a set of optimum designs. “Machine-learning-enhanced simulation is one of the best ways of cheaply getting on the proper mixture of parameters like temperatures, materials composition, and pressures for creating these supplies at prime quality with much less vitality,” Blaiszik mentioned. The preliminary mannequin for the melt-blowing course of was developed by way of a sequence 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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