Data Assimilation of Steam Flow Through a Control Valve Using Ensemble Kalman Filter
Abstract The present work concentrates on the simulation enhancement of steam flow through a control valve using novel data assimilation (DA) approach. Ensemble Kalman filter (EnKF) is applied to improve the performance of k-? shear stress transport (SST) model by optimizing its turbulence model constants. The selected measurement data at different operating conditions are used as observation, while the rest data are involved for validation. Firstly, four flow patterns, which arise on their respective operating conditions, are identified and analyzed to illustrate the basic characteristics of flow in the control valve. Then DA is performed based on the sample computation by perturbing the model constants and the EnKF process to determine the optimal constant matrix. This optimized constant matrix is subsequently used for the precomputation of the valve flow with significant improvement on the flow rate prediction. The velocity and turbulent kinetic energy fields with default and optimal model constants are also compared to illustrate the effect of DA. The results show that the DA enhanced model constants can significantly reduce the predicted volume flow rate error at all opening ratios presently concerned. With updated model constants, the velocity and turbulent kinetic energy distributions are greatly modified in the valve seat between main valve and control valve.