Host: The Japan Society of Mechanical Engineers
Name : [in Japanese]
Date : November 16, 2022 - November 18, 2022
Accuracy of estimation or forecast of states or parameters by data assimilation is highly depends on observation condition. Especially, for improving accuracy with limited number of observation sensors, optimization of sensor placement is important. In this study, we conducted the experiments of optimization of sensor position for Kalman Vortex reconstruction problem. We compare the results of assimilation using the optimal observation points (top 20) suggested based on the two different methodologies, Empirical Observability Gramian (EOG) and Ensemble Forecast Sensitivity to Observations (EFSO). EOG is based on concept of observability while EFSO is based on sensitivity to reduction of forecast error by ensemble-based data assimilation. Spatial distribution of optimized sensor position by each methodology looks different and the results of experiment showed that assimilated flow field with the observation optimized by EOG showed smaller RMSE than that optimized by EFSO. Further analysis must be conducted changing the number or spatial interval of sensors.
