摘要: In this work, a priori analysis of machine learning strategies is carried out with the goal of developing data-driven wall models for large eddy simulation (LES) of gas turbine film cooling flows. High-fidelity flow datasets are e... 展开 In this work, a priori analysis of machine learning strategies is carried out with the goal of developing data-driven wall models for large eddy simulation (LES) of gas turbine film cooling flows. High-fidelity flow datasets are extracted from wall-resolved LES (WRLES) of flow over a flat plate interacting with the coolant flow supplied by a single row of 7-7-7 shaped cooling holes inclined at 30° with the flat plate, considering different blowing ratios. Light Gradient Boosting Machine (LGBM) is employed as the ML algorithm for data-driven wall modeling. Parametric tests are conducted to systematically assess the influence of a variety of input flow features (velocity components, velocity gradients, pressure gradients, and fluid properties) on the accuracy of the ML wall model with respect to prediction of wall shear stress. In addition, the use of spatial stencil and time delay is also explored within the ML wall modeling framework. It is shown that the streamwise and spanwise velocity gradient features have a major impact on the prediction fidelity of wall model, while the effect of wall-normal velocity gradients is found to be negligible. Moreover, adding flow feature information from an x-y-z spatial stencil significantly improves the ML model accuracy and generalizability compared to just using local flow features from the matching location. Overall, the best performance is achieved when both spatial stencil and time delay features are incorporated within the data-driven wall modeling paradigm. 收起