Skip to main content

Research Repository

Advanced Search

LES informed data-driven models for RANS simulations of single-hole cooling flows

Ellis, Christopher D.; Xia, Hao

LES informed data-driven models for RANS simulations of single-hole cooling flows Thumbnail


Authors

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

Hao Xia



Abstract

A LES-informed data-driven approach for improved predictions of the turbulent heat flux vector has been sought for film and effusion cooling flow applications. Random forest and shallow neural networks have been used to train a spatially varying coefficient for the Higher-Order Generalised Gradient Diffusion Hypothesis (HOGGDH) turbulent heat flux closure model. a priori results of the turbulent heat flux magnitude showed significant improvements over the standard HOGGDH model. The random forest model was implemented into OpenFOAM with a previously published data-driven turbulent anisotropy model. The random forest model provided modest improvements to both low and high-blowing ratio film cooling cases along centreline and spanwise distributions. Large cooling effectiveness improvements (up to 82%) were found when compared to the Gradient Diffusion Hypothesis (GDH) model and marginal improvements were shown when compared to the HOGGDH model with its standard coefficient of 0.6.

Citation

Ellis, C. D., & Xia, H. (2024). LES informed data-driven models for RANS simulations of single-hole cooling flows. International Journal of Heat and Mass Transfer, 235, Article 126150. https://doi.org/10.1016/j.ijheatmasstransfer.2024.126150

Journal Article Type Article
Acceptance Date Aug 29, 2024
Online Publication Date Sep 7, 2024
Publication Date Dec 15, 2024
Deposit Date Sep 9, 2024
Publicly Available Date Sep 9, 2024
Journal International Journal of Heat and Mass Transfer
Print ISSN 0017-9310
Electronic ISSN 0017-9310
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 235
Article Number 126150
DOI https://doi.org/10.1016/j.ijheatmasstransfer.2024.126150
Public URL https://nottingham-repository.worktribe.com/output/39445766
Publisher URL https://www.sciencedirect.com/science/article/pii/S0017931024009803

Files





You might also like



Downloadable Citations