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Vortical flow fields are complex in nature and are prevalent in aircraft aerodynamics. Reynolds Averaged Navier Stokes simulation methodology along with eddy viscosity based Menter—Shear Stress Transport model is widely used in the research community for practical applications. Extensions to Menter—SST turbulence model based on flow physics are explored in the present work to improve the prediction capability for separated and vortical flow fields. A data driven calibration approach is employed based on Bayesian modeling to determine the coefficients of the extended turbulence model. Performance of the extended model is studied with complex delta wings under subsonic flow conditions.

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Bayesian Calibration of Extended Menter-SST Turbulence Model for Vortical Flows

  • 章节

  • 发表日期：

    2025年

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  • Gokul Subbian,
  • Thilo Moshagen,
  • Noemi Friedman,
  • Elmar Zander,
  • Rolf Radespiel

摘要

Vortical flow fields are complex in nature and are prevalent in aircraft aerodynamics. Reynolds Averaged Navier Stokes simulation methodology along with eddy viscosity based Menter—Shear Stress Transport model is widely used in the research community for practical applications. Extensions to Menter—SST turbulence model based on flow physics are explored in the present work to improve the prediction capability for separated and vortical flow fields. A data driven calibration approach is employed based on Bayesian modeling to determine the coefficients of the extended turbulence model. Performance of the extended model is studied with complex delta wings under subsonic flow conditions.