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Enhancing computational fluid dynamics with machine learning

Abstract

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.

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Fig. 1: Summary of some of the most relevant areas where ML can enhance CFD.
Fig. 2: An example of ML-accelerated direct numerical simulation.
Fig. 3: An example of LES modeling where the dissipation coefficient in the Smagorinski model is calculated by means of ML.
Fig. 4: Schematic of NN autoencoders for dimensionality reduction and model identification.

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Acknowledgements

R.V. acknowledges financial support from the Swedish Research Council (VR) and from ERC grant no. ‘2021-CoG-101043998, DEEPCONTROL’. S.L.B. acknowledges funding support from the Army Research Office (ARO W911NF-19-1-0045; programme manager M. Munson).

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Vinuesa, R., Brunton, S.L. Enhancing computational fluid dynamics with machine learning. Nat Comput Sci 2, 358–366 (2022). https://doi.org/10.1038/s43588-022-00264-7

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