Machine-Learning-Enhanced Simulation of Unsteady Turbulent Flows

An adjoint-based Field Inversion and Machine Learning (FIML) framework is applied to improve RANS predictions of unsteady flow around a sinusoidally pitching airfoil. First, steady-state model discrepancies are inferred via field inversion at multiple angles of attack at Reynolds number,

Authors

Anupam Sharma (ORCID: https://orcid.org/0000-0002-8198-3891)
K. Ahmed (ORCID: https://orcid.org/0000-0003-2910-3188)
Paul A. Durbin (ORCID: https://orcid.org/0000-0001-5586-0962)
S. Menon (ORCID: https://orcid.org/0000-0002-4816-9978)

Institutions

Iowa State University (US)

Publication Details

Published: 2025-07-16
DOI: https://doi.org/10.2514/6.2025-3179
Primary Topic: Model Reduction and Neural Networks
Type: article
Field-Weighted Citation Impact: 0.00

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article

Machine-Learning-Enhanced Simulation of Unsteady Turbulent Flows

Anupam Sharma, K. Ahmed, Paul A. Durbin, S. Menon

Jul 16, 2025

Model Reduction and Neural Networks

article

Machine-Learning-Enhanced Simulation of Unsteady Turbulent Flows

Anupam Sharma, K. Ahmed, Paul A. Durbin, S. Menon

July 16, 2025article en

Abstract

An adjoint-based Field Inversion and Machine Learning (FIML) framework is applied to improve RANS predictions of unsteady flow around a sinusoidally pitching airfoil. First, steady-state model discrepancies are inferred via field inversion at multiple angles of attack at Reynolds number,

Iowa State University (US)

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Model Reduction and Neural Networks

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Machine-Learning-Enhanced Simulation of Unsteady Turbulent Flows — Anupam Sharma, K. Ahmed, et al. · (2025) | TGRS Research Map | TGRS