Estimation of flow field in natural convection with density stratification by local ensemble transform Kalman filter

石垣 将宏*; 廣瀬 意育; 安部 諭; 永井 亨*; 渡辺 正*

Fluids (Internet), 7(7), p.237_1 - 237_18, 2022/07

https://doi.org/10.3390/fluids7070237

To estimate thermal flow in a nuclear reactor during an accident, it is important to improve the accuracy of computational fluid dynamics simulation. Temperature and flow velocity are not homogeneous and have large variations in a reactor containment vessel because of its very large volume. In addition, Kelm et al (2016) pointed out that the influence of variations of initial and boundary conditions was important. Therefore, it is necessary to set the initial and boundary conditions taking into account the variations of these physical quantities. However, it is a difficult subject to set such complicated initial and boundary conditions. Then, we can obtain realistic initial and boundary conditions by the data assimilation technique, and we can improve the accuracy of the simulation result. In this study, we applied the data assimilation by local ensemble transform Kalman filter (Hunt et al., 2007) to the simulation of natural convection behavior in density stratification, and we performed a twin model experiment. We succeeded in the estimation of the flow fields and improving the simulation accuracy by the data assimilation, even if we applied the boundary condition with error for the true condition.