A highly accurate strategy for data-driven turbulence modeling
Author:
Funder
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computational Mathematics
Link
https://link.springer.com/content/pdf/10.1007/s40314-023-02547-9.pdf
Reference78 articles.
1. Amarloo A, Forooghi P, Abkar M (2022) Frozen propagation of Reynolds force vector from high-fidelity data into Reynolds-averaged simulations of secondary flows. Phys Fluids 34(11):115102
2. Amarloo A, Cinnella P, Iosifidis A, Forooghi P, Abkar M (2023) Data-driven Reynolds stress models based on the frozen treatment of Reynolds stress tensor and Reynolds force vector. Phys Fluids 35(7):075154
3. Andrade JR, Martins RS, Thompson RL, Mompean G, Neto AS (2018) Analysis of uncertainties and convergence of turbulent wall-bounded flows by means of a physically-based criterion. Phys Fluids 30:045106
4. Baars WJ, Marusic I (2020) Data-driven decomposition of the streamwise turbulence kinetic energy in boundary layers. Part 1. Energy spectra. J Fluid Mech 882:A25
5. Baars WJ, Marusic I (2020) Data-driven decomposition of the streamwise turbulence kinetic energy in boundary layers. Part 2. Integrated energy and data-driven decomposition of the streamwise turbulence kinetic energy in boundary layers. J Fluid Mech 882:A26
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