Critical heat flux prediction for narrow rectangular channels: a machine learning model based on the Kalman filter

It employs an Extended Kalman Filter (EKF) to dynamically correct and fuse CHF-LUT data, then trains a BP neural network to achieve high-precision prediction of CHF values in narrow rectangular channels. To validate the effectiveness of EKF-ML model, the prediction results are compared with those obtained directly from the CHF-LUT and the Sudo correlation. Results demonstrate that the EKF-ML model significantly enhances the prediction accuracy of CHF in narrow rectangular channels based on the CHF-LUT.

The mean relative error reaches 0.78%, and the relative root mean square error (rRMSE) decreases to 12.48% under specified operating conditions. This study provides new insights and methodologies for predicting CHF values in narrow flow channels of high-flux reactor cores.