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Cited by 737Model Reduction and Neural NetworksComputational Fluid Dynamics and AerodynamicsFluid Dynamics and Turbulent Flows

Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data

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Wang, Jian-Xun, Wu, Jin-Long, Xiao, Heng·2017·Physical Review Fluids
DOI 10.1103/physrevfluids.2.034603
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Abstract

We show that the discrepancies in Reynolds-averaged Navier-Stokes (RANS) modeled Reynolds stresses can be explained by mean flow features. A physics-informed machine learning framework is proposed to improve the predictive capabilities of RANS models by leveraging existing direct numerical simulations databases.

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Research works printed but not linked: 16

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Authors
Wang, J.-X., Wu, J.-L., & Xiao, H.
Affiliations
Virginia Tech (US)
ORCID
Xiao, Heng
Venue
Physical Review Fluids
Publisher
American Physical Society (APS)
Published
2017-03-16
Volume
2
Issue
3
ISSN
2469-990X (ISSN-L)
DOI
10.1103/physrevfluids.2.034603
OpenAlex
W2594632285
Cited by
737
References
37
Citations by year
2026: 81 · 2025: 84 · 2024: 90 · 2023: 83 · 2022: 115 · 2021: 105 · 2020: 82 · 2019: 53 · 2018: 35 · 2017: 8
FWCI
24.94
Type
journal article
Language
en
Open access
Licence
link.aps.org/licenses/aps-default-license (vor)
Locations
  • Physical Review Fluids (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
  • Physical Sciences › Engineering › Computational Mechanics › Computational Fluid Dynamics and Aerodynamics
  • Physical Sciences › Engineering › Computational Mechanics › Fluid Dynamics and Turbulent Flows
Keywords
Reynolds stress, Reynolds-averaged Navier-Stokes, physics-informed machine learning, model mismatch, direct numerical simulation data, turbulence modeling, model predictive capability

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