LES informed data-driven modelling of a spatially varying turbulent diffusivity coefficient in film cooling flows

A novel data-driven approach is used to describe a spatially varying turbulent diffusivity coefficient for the Higher Order Generalised Gradient Diffusion Hypothesis (HOGGDH) closure of the turbulent heat flux to improve upon RANS cooling predictions in film cooling flows. Machine learning algorithm...

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Bibliographic Details

Main Authors:	Christopher Ellis, Hao Xia, Gary Page
Format:	Default Conference proceeding
Published:	2021
Subjects:	Film cooling Flow (Dynamics) Modeling Turbulence Algorithms Cooling Diffusion (Physics) Heat flux Reynolds-averaged Navier–Stokes equations Adiabatic cooling Artificial neural networks Density Machine learning
Online Access:	https://hdl.handle.net/2134/22183000.v1
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