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• W3036340036 abstract "A machine-learning technique is used to develop data-driven models for turbulent heat flux prediction in wall jets with co-flow The training data are obtained by performing highly-resolved large-eddy simulations of nine cases covering various flow and geometry conditions. Robust Reynolds-averaged Navier-Stokes based heat-transfer closures are obtained, and a significant error reduction in predicting adiabatic wall effectiveness is achieved using the machine-learnt models." @default.
• W3036340036 created "2020-06-25" @default.
• W3036340036 creator A5021829090 @default.
• W3036340036 creator A5090299164 @default.
• W3036340036 date "2020-06-18" @default.
• W3036340036 modified "2023-10-14" @default.
• W3036340036 title "Large eddy simulations of wall jets with coflow for the study of turbulent Prandtl number variations and data-driven modeling" @default.
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• W3036340036 doi "https://doi.org/10.1103/physrevfluids.5.064501" @default.
• W3036340036 hasPublicationYear "2020" @default.
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