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Accurate prediction of the wall temperature downstream of the trailing-edge slot is crucial to designing turbineblades that can withstand the harsh aerothermal environment in a modern gas turbine. Because of their computationalefficiency, industry relies on low-fidelity tools like RANS for momentum and thermal field calculations, despite theirknown underprediction of wall temperature. In this paper, a novel framework using a branch of machine learning, geneexpressionprogramming (GEP) [Zhao et al. 2020, J. Comp. Physics, 411:109413] is used to develop closures for theturbulent heat-flux to improve upon this underprediction. In the original use of GEP (“frozen” approach), the turbulentheat-flux from a high-fidelity database was used to evaluate the fitness of the candidate closures during the symbolicregression, however, the resulting closure had no information of the temperature field during the optimisation process.In this work, the regression process of the GEP instead incorporates RANS calculations to evaluate the fitness of thecandidate closures. This allows the inclusion of the temperature field from RANS to advance the iterative regression,leading to a more integrated heat-flux closure development, and consequently more accurate and robust models. TheGEP-based CFD-driven framework is demonstrated on a trailing edge slot configuration with three blowing ratios. Fulla posteriori predictions from the new closures are compared to high-fidelity reference data and both conventional RANSclosures and closures obtained from the “frozen” approach.

RANS predictions of trailing-edge slot flows using heat-flux closures developed with CFD-driven machine learning