Work

Data-driven turbulent heat flux modeling with inputs of multiple fidelity

Year: 2025

Type: article

Abstract: 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).

Source: Physical Review Fluids

DOI: 10.1103/physrevfluids.10.034606

Authors: Matilde Fiore, Enrico Saccaggi, Lilla Koloszar, Yann Bartosiewicz, Miguel A. Mendez

Institutions: Von Karman Institute for Fluid Dynamics, Politecnico di Torino, UCLouvain

Language: English

Topic: Fluid Dynamics and Turbulent Flows

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Fonds De La Recherche Scientifique - FNRS, Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture

Awards: Advanced Nuclear Safety Evaluation of Liquid Metal Using Systems, G3482316986