Improving CFD simulations by local machine-learned correction
- URL: http://arxiv.org/abs/2305.00114v1
- Date: Fri, 28 Apr 2023 22:20:42 GMT
- Title: Improving CFD simulations by local machine-learned correction
- Authors: Peetak Mitra, Majid Haghshenas, Niccolo Dal Santo, Conor Daly, David
P. Schmidt
- Abstract summary: High-fidelity computational fluid dynamics (CFD) simulations for design space explorations can be exceedingly expensive.
This computational cost/accuracy trade-off is a major challenge for modern CFD simulations.
We propose a method that uses a trained machine learning model that has learned to predict the discretization error.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: High-fidelity computational fluid dynamics (CFD) simulations for design space
explorations can be exceedingly expensive due to the cost associated with
resolving the finer scales. This computational cost/accuracy trade-off is a
major challenge for modern CFD simulations. In the present study, we propose a
method that uses a trained machine learning model that has learned to predict
the discretization error as a function of largescale flow features to inversely
estimate the degree of lost information due to mesh coarsening. This
information is then added back to the low-resolution solution during runtime,
thereby enhancing the quality of the under-resolved coarse mesh simulation. The
use of a coarser mesh produces a non-linear benefit in speed while the cost of
inferring and correcting for the lost information has a linear cost. We
demonstrate the numerical stability of a problem of engineering interest, a 3D
turbulent channel flow. In addition to this demonstration, we further show the
potential for speedup without sacrificing solution accuracy using this method,
thereby making the cost/accuracy trade-off of CFD more favorable.
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