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Data-driven turbulence anisotropy in film and effusion cooling flows

Ellis, Christopher; Xia, Hao

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Authors

Dr CHRIS ELLIS Chris.Ellis1@nottingham.ac.uk
Assistant Professor in Aerospacepropulsion

Hao Xia



Abstract

Film and effusion cooling flows contain complex flow that classical Reynolds-averaged Navier–Stokes (RANS) models struggle to capture. A tensor-basis neural network is employed to provide an anisotropic model that can reproduce the Reynolds stress fields of large-eddy simulations (LES). High-quality LES datasets are used to train, validate, and test a neural network model. A priori results show the model can reproduce the Reynolds stress field on a cooling case not present in the model's training. The neural networks are employed directly into RANS solver, augmenting a k-ω shear stress transport (SST) model, with conditioning applied. The model provided improvements to Reynolds stress, velocity, and temperature fields in cases not used to train the model, including a multi-hole case that differs from the single-hole geometry used to train the case. Underpredictions of the turbulent kinetic energy field, modeled with the SST transport equation, were found to lead to underpredictions in the neural network produced Reynolds stresses. Correcting this with the LES, resolved turbulent kinetic energy provided further agreement. The method found significant improvements to the surface cooling results that advance the current state-of-the-art in RANS modeling of film and effusion cooling flows.

Citation

Ellis, C., & Xia, H. (2023). Data-driven turbulence anisotropy in film and effusion cooling flows. Physics of Fluids, 35(10), Article 105114. https://doi.org/10.1063/5.0166685

Journal Article Type Article
Acceptance Date Sep 8, 2023
Online Publication Date Oct 5, 2023
Publication Date Oct 1, 2023
Deposit Date Sep 16, 2024
Publicly Available Date Oct 4, 2024
Journal Physics of Fluids
Print ISSN 1070-6631
Electronic ISSN 1089-7666
Publisher American Institute of Physics
Peer Reviewed Peer Reviewed
Volume 35
Issue 10
Article Number 105114
DOI https://doi.org/10.1063/5.0166685
Public URL https://nottingham-repository.worktribe.com/output/34871547
Publisher URL https://pubs.aip.org/aip/pof/article/35/10/105114/2915101/Data-driven-turbulence-anisotropy-in-film-and

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