Extension of the Active Model Split Hybrid Turbulence Model for Wind Energy Relevant Flows

ORAL

Abstract

In this talk, we discuss efforts to extend the Active Model Split (AMS) hybrid turbulence model to wind energy relevant flows. AMS, a recently developed Reynolds-Averaged Navier Stokes (RANS) / Large Eddy Simulation (LES) modeling framework (S. Haering et al., 2022), has many conceptual advantages over existing RANS/LES models. The main premise is the splitting of the modeled stress into two distinct terms, one in the role of RANS, to approximate the mean subgrid stress and the other in the role of LES, to provide energy transfer between resolved and unresolved scales. AMS has shown promising results in canonical turbulence validation cases when coupled with the $\overline{v}^2-f$ RANS model. However, for large scale wind farm simulations, models with less computational overhead are desired. To simulate these flows, we extend AMS by providing the mean subgrid stress through the Shear Stress Transport (SST) RANS model (Menter et al., 2003), commonly used in wind turbine simulations. We compare AMS-SST models with DES variants on high Reynolds number (Re) flows of airfoils, with comparisons to LES and/or experimental data where available. The advantages of the AMS hybrid models are discussed and areas where further AMS development is being pursued are emphasized.

*This research was supported by the Exascale Computing Project (17-SC-20-SC), a joint project of the U.S. Department of Energy's Office of Science and National Nuclear Security Administration, responsible for delivering a capable exascale ecosystem, including software, applications, and hardware technology, to support the nation's exascale computing imperative. A portion of the research was performed using computational resources sponsored by the Department of Energy's Office of Energy Efficiency and Renewable Energy and located at the National Renewable Energy Laboratory.

Presenters

  • Jeremy Melvin

    • University of Texas at Austin

Authors

  • Jeremy Melvin

    • University of Texas at Austin
  • Marc T Henry de Frahan

    • National Renewable Energy Laboratory
  • Robert D Moser

    • UT Austin
  • Michael A Sprague

    • National Renewable Energy Laboratory