期刊
JOURNAL OF TURBOMACHINERY-TRANSACTIONS OF THE ASME
卷 142, 期 1, 页码 -出版社
ASME
DOI: 10.1115/1.4045389
关键词
computational fluid dynamics (CFD); fluid dynamics and heat transfer phenomena in compressor and turbine components of gas turbine engines; heat transfer and film cooling; turbulence modeling; machine learning
资金
- Honeywell Aerospace
The design of film cooling systems relies heavily on Reynolds-averaged Navier-Stokes (RANS) simulations, which solve for mean quantities and model all turbulent scales. Most turbulent heat flux models, which are based on isotropic diffusion with a fixed turbulent Prandtl number (Prt), fail to accurately predict heat transfer in film cooling flows. In the present work, machine learning models are trained to predict a non-uniform Prt field using various datasets as training sets. The ability of these models to generalize beyond the flows on which they were trained is explored. Furthermore, visualization techniques are employed to compare distinct datasets and to help explain the cross-validation results.
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