Host: The Japan Society of Mechanical Engineers
Name : [in Japanese]
Date : November 23, 2018 - November 25, 2018
This paper proposes an approach for the study of complex turbulent transition flows that integrates sparse measurement and computational fluid dynamics (CFD) by using a data assimilation technique. The approach aims at representing complex turbulent transition flows more properly than measurements and computations. To this end, the ensemble transform Kalman filter (ETKF), a sequential advanced data assimilation method, is employed for the estimation and applied to turbulent transonic flows. In this paper, the effectiveness of the approach is shown through a numerical experiment. In the numerical experiment, turbulent viscosity around the NACA0012 airfoil is estimated by the surface temperature on the airfoil. The result shows that the turbulent viscosity is properly estimated, and the skin friction coefficient that is important to represent the turbulent transition flow is estimated more properly than the computation alone. These findings suggest the effectiveness of this approach using data assimilation to represent turbulent transition flows.
