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Parameter uncertainty quantification for a four-equation transition model using a data assimilation approach

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  • Yang, Muchen
  • Xiao, Zhixiang

Abstract

A majority of the simulation discrepancies for RANS models are due to parameter uncertainties. In this paper, a data assimilation approach called the ensemble Kalman filtering (EnKF), was used to investigate the parameter uncertainties of a four-equation k-ω-γ-Ar transition/turbulence integrated model. The transitional flows past two wind-turbine airfoils were analyzed, while the transition onset and end locations, as well as the skin friction coefficients, were taken as the observation variables. The results show that the posterior distributions of the parameters can be efficiently obtained through the filtering process, while the estimated parameter variations are observed among different angles of attack (AoAs), as well as different sides at the same AoA. After the EnKF analysis, the most sensitive parameter, C2, is changed into an adaptive parameter, which demonstrates the pressure gradients effects through a modified shape factor. More accurate transition locations are calculated by the adaptive C2 at different AoAs for NLF(1)-0416, and on both sides for NACA-0012 than the original model having constant C2.

Suggested Citation

  • Yang, Muchen & Xiao, Zhixiang, 2020. "Parameter uncertainty quantification for a four-equation transition model using a data assimilation approach," Renewable Energy, Elsevier, vol. 158(C), pages 215-226.
    • Handle: RePEc:eee:renene:v:158:y:2020:i:c:p:215-226
    DOI: 10.1016/j.renene.2020.05.139
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    References listed on IDEAS

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    1. Cheung, Sai Hung & Oliver, Todd A. & Prudencio, Ernesto E. & Prudhomme, Serge & Moser, Robert D., 2011. "Bayesian uncertainty analysis with applications to turbulence modeling," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1137-1149.
    2. Yang, Muchen & Xiao, Zhixiang, 2019. "Distributed roughness induced transition on wind-turbine airfoils simulated by four-equation k-ω-γ-Ar transition model," Renewable Energy, Elsevier, vol. 135(C), pages 1166-1177.
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