Approach for uncertainty of turbulence modeling based on data assimilation technique

Measurement integrated simulation which makes up for uncertainty of measurement techniques in experiment and makes up for uncertainty of modeling in simulation is expected as an innovative technology in future of aerospace science. The measurement integrated simulation has a deep relationship with “data assimilation” that has been developed in the field of earth science. In this paper, value of parameter which is one of uncertainty factors in simulation is investigated by using the ensemble Kalman filter that is an advanced technique of data assimilation. In this research, the values of parameters in the Spalart–Allmaras turbulence model are investigated. And, this paper shows that the values could be determined objectively or statistically by data assimilation techniques and not by subjective “calibration” as performed by the model proposers themselves.

► The parameter values of the SA turbulence model were investigated statistically.
► We employed the ensemble Kalman filter for the investigation.
► The original parameter values of Cν1, Cb1, Cb2, and Cω2 are statistically accurate.
► The parameter Cω3 have high degree of freedom for the value.