Data-driven turbulent heat flux modeling with inputs of multiple fidelity
The widespread application of data-driven turbulence models is currently limited by challenges in generalization and robustness to inconsistencies between input data of varying fidelity levels. This is especially true for thermal turbulent closures, which inherently depend on momentum statistics provided by low or high-fidelity turbulence momentum models. This work investigates the impact of momentum modeling inconsistencies on a data-driven thermal closure trained with a dataset with multiple fidelities (DNS and RANS).
Authors
- Lilla Koloszár (ORCID: https://orcid.org/0000-0002-5564-3834)
- Miguel Alfonso Mendez (ORCID: https://orcid.org/0000-0002-1115-2187)
- Yann Bartosiewicz (ORCID: https://orcid.org/0000-0003-1945-3934)
- Matilde Fiore (ORCID: https://orcid.org/0000-0002-6022-9574)
- Enrico Saccaggi (ORCID: https://orcid.org/0009-0009-1559-5856)
Institutions
- Von Karman Institute for Fluid Dynamics (BE)
- Politecnico di Torino (IT)
- UCLouvain (BE)
Publication Details
- Journal
- Physical Review Fluids
- Published
- 2025-03-17
- DOI
- https://doi.org/10.1103/physrevfluids.10.034606
- Citations
- 1
- Primary Topic
- Model Reduction and Neural Networks
- Type
- article
- Field-Weighted Citation Impact
- 1.32
Funders
- Fonds De La Recherche Scientifique - FNRS