![]() In this work, the regression process of the GEP instead incorporates RANS calculations to evaluate the fitness of theĬandidate closures. Regression, however, the resulting closure had no information of the temperature field during the optimisation process. Heat-flux from a high-fidelity database was used to evaluate the fitness of the candidate closures during the symbolic In the original use of GEP (“frozen” approach), the turbulent Turbulent heat-flux to improve upon this underprediction. ![]() Programming (GEP) is used to develop closures for the In this paper, a novel framework using a branch of machine learning, geneexpression Known underprediction of wall temperature. ![]() Because of their computationalĮfficiency, industry relies on low-fidelity tools like RANS for momentum and thermal field calculations, despite their Accurate prediction of the wall temperature downstream of the trailing-edge slot is crucial to designing turbineīlades that can withstand the harsh aerothermal environment in a modern gas turbine.
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