Year: 2025
Type: article
Abstract: We improve upon two key aspects of the Menter shear stress transport (SST) turbulence model: (1) We propose a more robust adverse pressure gradient sensor based on the strength of the pressure gradient in the direction of the local mean flow; (2) We propose two alternative eddy viscosity models to be used in the adverse pressure gradient regions identified by our sensor. Direct numerical simulations of the Boeing Gaussian bump are used to identify the terms in the baseline SST model that need correction, and a posteriori Reynolds-averaged Navier-Stokes calculations are used to calibrate coefficient values, leading to a model that is both physics driven and data informed. The two sensor-equipped models are applied to two thick airfoils representative of modern wind turbine applications, the FFA-W3-301 and the DU00-W-212. The proposed models improve the prediction of stall (onset of separation) with respect to the prediction of the baseline SST model.
Source: Journal of Turbulence
Institution: National Laboratory of the Rockies
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funders: Language Literacy and Culture, University of Maryland, Baltimore County, U.S. Department of Energy, Office of Science, Office of Energy Efficiency and Renewable Energy, National Nuclear Security Administration, Wind Energy Technologies Office, Office of Energy Efficiency, National Renewable Energy Laboratory
OpenAlex ID: W4409770366