This study proposes a data assimilation methodology for estimating the optimal parameter values of turbulence models. The proposed methodology was applied to the estimation of the parameter a1 in the modified Menter k-ω shear-stress transport turbulence model. For this purpose, a fundamental turbulent flow, namely, the flow over a two-dimensional backward-facing step, was employed. The estimated value of a1 (1.0) differed from its original value (i.e., 0.31). The modified Menter k-ω shear-stress transport turbulence model with a1=1.0 was validated on several turbulent flow calculations; flows over a two-dimensional backward-facing step and a two-dimensional flat-plate boundary layer, two-dimensional transonic flows around the RAE 2822 airfoil, and three-dimensional transonic flows around the ONERA M6 wing. In simulations, the modified Menter k-ω shear-stress transport turbulence model with a1=1.0 better modeled the separated and adverse pressure gradient flows than the original modified Menter k-ω shear-stress transport turbulence model with a1=0.31. Furthermore, in the absence of separation and adverse pressure gradient flows, the proposed and original modified Menter k-ω shear-stress transport turbulence models computed almost the same results. These observations suggest that the proposed data assimilation methodology effectively estimates the optimal parameter values of turbulence models and that the estimated a1 (1.0) improves the performance of the modified Menter k-ω shear-stress transport turbulence model over the original value (i.e., 0.31).
Optimization of Parameter Values in the Turbulence Model Aided by Data Assimilation
2016
Aufsatz (Zeitschrift)
Englisch
Optimization of Parameter Values in the Turbulence Model Aided by Data Assimilation
Online Contents | 2016
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