Data assimilation for particle image velocimetry based on lattice Boltzmann method

Experimental and numerical approaches in PIV measurements have limitations in uncertainty that arise in experimental setups and mathematical models. In this regard, data assimilation for PIV that can complement experimental data and numerical model have received much attention in the fluid mechanics community. In the post-processing of PIV data, the most widely used approaches are linear interpolation, POD and Kalman filtering which are reduced order of modeling using general signal processing techniques. However, these techniques do not consider the flow physics. In this study, we propose a new data assimilation method for PIV using LBM. We solve the flow governing equations directly and non-iteratively using the PIV data as initial or boundary conditions to resolve finer temporal and spatial velocity vectors. In this study, GPGPU operation was performed using openCL for parallel data analysis. With a comparison of the proposed method to existing experimental and DNS data base, the performance of LBM based data assimilation method is verified more effective to recover missing data of PIV results.