Fusing Experimental Measurements and ROM Predicted Data for Wind Load Prediction with Extended Kalman Filter

Wind tunnel test, Computational Fluid Dynamics (CFD) simulation, and Proper Orthogonal Decomposition (POD) based Reduced Order Model (ROM) are used for wind load prediction on a LNG carrier in this project. In order to train the model for high accurate predictions and provide wind load prediction for vessel operations with high confidence level, extended Kalman Filter (EKF) based data assimilation model is developed to fuse the experimental data and the ROM predicted data. Effects of sensor locations and sensor numbers on the data fusing are studied. Study results indicate that the single point data assimilation can help with sensor positioning and outlier data detection. Based on the standard errors at the sensor points, prediction accuracy with sorted multipoint data assimilation is higher than that with single point data assimilation.