Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 16 June 2021 / Approved: 17 June 2021 / Online: 17 June 2021 (10:39:40 CEST)

How to cite: Akolekar, H.D.; Waschkowski, F.; Zhao, Y.; Pacciani, R.; Sandberg, R.D. Transition Modeling for Low Pressure Turbines Using Computational Fluid Dynamics Driven Machine Learning. Preprints 2021, 2021060457 (doi: 10.20944/preprints202106.0457.v1). Akolekar, H.D.; Waschkowski, F.; Zhao, Y.; Pacciani, R.; Sandberg, R.D. Transition Modeling for Low Pressure Turbines Using Computational Fluid Dynamics Driven Machine Learning. Preprints 2021, 2021060457 (doi: 10.20944/preprints202106.0457.v1).

Abstract

Existing Reynolds Averaged Navier-Stokes based transition models do not accurately predict separation induced transition for low pressure turbines. Therefore, in this study, a novel framework based on computational fluids dynamics 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 Re2is=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.

Subject Areas

Machine Learning; Multi-objective optimization; Low Pressure Turbine; Transition; Turbulence Modeling

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