Parameter optimization for turbulent boundary layer generation using ensemble Kalman filter

Onodera, Naoyuki   ; Idomura, Yasuhiro   ; Hasegawa, Yuta   ; Nakayama, Hiromasa   

We have developed a wind simulation code named CityLBM to realize wind digital twins. Mesoscale wind conditions are given as boundary conditions in CityLBM by using a nudging data assimilation method. It is found that conventional approaches with constant nudging coefficients fail to reproduce turbulent intensity in long time simulations, where atmospheric stability conditions change significantly. We propose a dynamic parameter optimization method for the nudging coefficient based on an ensemble Kalman filter. CityLBM was validated against plume dispersion experiments in the complex urban environment of Oklahoma City. The nudging coefficient was updated to reduce the error of the turbulent intensity between the simulation and the observation. The mean error of velocity variance is reduced by 10% compared to the conventional nudging method with a constant nudging coefficient.