Enhancing computational fluid dynamics with machine learning.

Autor: Vinuesa R; FLOW, Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden. rvinuesa@mech.kth.se.; Swedish e-Science Research Centre (SeRC), Stockholm, Sweden. rvinuesa@mech.kth.se., Brunton SL; Department of Mechanical Engineering, University of Washington, Seattle, WA, USA.
Jazyk: angličtina
Zdroj: Nature computational science [Nat Comput Sci] 2022 Jun; Vol. 2 (6), pp. 358-366. Date of Electronic Publication: 2022 Jun 27.
DOI: 10.1038/s43588-022-00264-7
Abstrakt: Machine learning is rapidly becoming a core technology for scientific computing, with numerous opportunities to advance the field of computational fluid dynamics. Here we highlight some of the areas of highest potential impact, including to accelerate direct numerical simulations, to improve turbulence closure modeling and to develop enhanced reduced-order models. We also discuss emerging areas of machine learning that are promising for computational fluid dynamics, as well as some potential limitations that should be taken into account.
(© 2022. Springer Nature America, Inc.)
Databáze: MEDLINE
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