Transition Modeling for Low Pressure Turbines Using Computational Fluid Dynamics Driven Machine Learning

Harshal D. Akolekar, Fabian Waschkowski, Yaomin Zhao, Roberto Pacciani and Richard D. Sandberg
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Harshal D. Akolekar: Department of Mechanical Engineering, University of Melbourne, Parkville, VIC 3010, Australia
Fabian Waschkowski: Department of Mechanical Engineering, University of Melbourne, Parkville, VIC 3010, Australia
Yaomin Zhao: Center for Applied Physics and Technology, HEDPS, College of Engineering, Peking University, Beijing 100871, China
Roberto Pacciani: Department of Industrial Engineering, University of Florence, 50121 Firenze, Italy
Richard D. Sandberg: Department of Mechanical Engineering, University of Melbourne, Parkville, VIC 3010, Australia

Energies, 2021, vol. 14, issue 15, 1-17

Abstract: Existing Reynolds Averaged Navier–Stokes-based transition models do not accurately predict separation induced transition for low pressure turbines. Therefore, in this paper, a novel framework based on computational fluids dynamics (CFD) driven machine learning coupled with multi-expression and multi-objective optimization is explored to develop models which can improve the transition prediction for the T106A low pressure turbine at an isentropic exit Reynolds number of R e 2 i s = 100 , 000 . Model formulations are proposed for the transfer and laminar eddy viscosity terms of the laminar kinetic energy transition model using seven non-dimensional pi groups. The multi-objective optimization approach makes use of cost functions based on the suction-side wall-shear stress and the pressure coefficient. A family of solutions is thus developed, whose performance is assessed using Pareto analysis and in terms of physical characteristics of separated-flow transition. Two models are found which bring the wall-shear stress profile in the separated region at least two times closer to the reference high-fidelity data than the baseline transition model. As these models are able to accurately predict the flow coming off the blade trailing edge, they are also able to significantly enhance the wake-mixing prediction over the baseline model. This is the first known study which makes use of ‘CFD-driven’ machine learning to enhance the transition prediction for a non-canonical flow.

Keywords: machine learning; multi-objective optimization; low pressure turbine; transition; turbulence modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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