Assessment of Machine Learning Wall Modeling Approaches for Large Eddy Simulation of Gas Turbine Film Cooling Flows: An a Priori Study

Journal Article · 10 March 2024 · Journal of Engineering for Gas Turbines and Power

DOI: https://doi.org/10.1115/1.4064556 · OSTI ID:2536670

Kumar, Tadbhagya ^[1]; Pal, Pinaki ^[1]; Wu, Sicong ^[1]; Nunno, A. Cody ^[1]; Owoyele, Opeoluwa ^[1]; Joly, Michael M. ^[2]; Tretiak, Dima ^[2]

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1. Argonne National Laboratory (ANL), Argonne, IL (United States)
2. Raytheon Technologies Research Center, East Hartford, CT (United States)

Here, in this work, a priori analysis of machine learning (ML) strategies is carried out with the goal of data-driven wall modeling 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 degrees with the flat plate at different blowing ratios (BR). The WRLES are performed using the high-order Nek5000 spectral element computational fluid dynamics (CFD) solver. Light gradient boosting machine (LightGBM) is employed as the ML algorithm for the data-driven wall model. Parametric tests are conducted to systematically assess the influence of a wide range of input flow features (velocity components, velocity gradients, pressure gradients, and fluid properties) on the accuracy of 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 features associated with gradients of the streamwise and spanwise velocity components have a major impact on the prediction fidelity of wall model, while the effect of gradients of wall-normal velocity component 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, highest prediction accuracy is achieved when both spatial stencil and time delay features are incorporated within the data-driven wall modeling paradigm.

• Research Organization: Argonne National Laboratory (ANL), Argonne, IL (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Materials & Manufacturing Technologies Office (AMMTO); USDOE Office of Science (SC)
• Grant/Contract Number: AC02-06CH11357
• OSTI ID: 2536670
• Journal Information: Journal of Engineering for Gas Turbines and Power, Journal Name: Journal of Engineering for Gas Turbines and Power Journal Issue: 8 Vol. 146; ISSN 0742-4795
• Publisher: ASMECopyright Statement
• Country of Publication: United States
• Language: English