Field Sensitivity Analysis of Turbulence Model Parameters for Flow Over a Wing

Journal Article · 01 January 2022 · International Journal for Uncertainty Quantification

DOI: https://doi.org/10.1615/int.j.uncertaintyquantification.2021036467 · OSTI ID:1837974

Quick, Julian ^[1]; King, Ryan N. ^[2]; Henry de Frahan, Marc T. ^[2]; Ananthan, Shreyas ^[2]; Sprague, Michael A.  ^[2]; Hamlington, Peter E. ^[1]

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1. Univ. of Colorado, Boulder, CO (United States)
2. National Renewable Energy Lab. (NREL), Golden, CO (United States)

Reynolds-averaged-Navier-Stokes (RANS) turbulence models are a critical tool in computational-fluid-dynamics simulations of aerodynamic systems, but simulation results can be highly sensitive to RANS-model parameter choices. Sensitivity analysis can be used to quantify these impacts, and the objective of this study is to demonstrate field sensitivity analysis with respect to ten parameters in the 2003 Menter shear-stress-transport (SST) turbulence model. Here, the analysis is demonstrated for an application relevant to wind energy, namely, flow over a NACA 0015 wing at 12 degree angle of attack and a Reynolds number of 1.5 x 10⁶. We quantify sensitivity using Sobol indices and the mean-squared gradient, which are estimated using polynomial chaos and active subspace models, respectively. Our results indicate that there are substantial spatial variations in parameter sensitivities, with different sets of most-sensitive parameters near the wing, as well as in the downstream wake, consistent with the physical interpretations of the turbulence model inputs. We show that, for this particular turbulence model and flow, simultaneous dimension reduction is possible across all quantities of interest, enabling efficient exploration of model outcomes. Ultimately, this analysis provides new insights into turbulence model parameter sensitivities in incompressible flows, and also demonstrates the implementation of field sensitivity analysis for applications relevant to aerodynamics simulations.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind Energy Technologies Office (EE-4W); USDOE Exascale Computing Project (ECP)
• Grant/Contract Number: AC36-08GO28308; 17-SC-20-SC
• OSTI ID: 1837974
• Report Number(s): NREL/JA-2C00-80359; MainId:42562; UUID:1435fa36-9258-4771-9b19-2c6eb8eb1c40; MainAdminID:63495
• Journal Information: International Journal for Uncertainty Quantification, Vol. 12, Issue 1; ISSN 2152-5080
• Publisher: Begell HouseCopyright Statement
• Country of Publication: United States
• Language: English