Reliable prediction of peak cladding temperature during top reflooding processes: A neural network-based perception-interaction approach

Abstract

The reliable prediction of peek cladding temperature is crucial for heat transfer and reactor security when large break loss of coolant accident (LBLOCA) happens. This study proposes a neural network-based Perception-Interaction system to effectively predict and monitor the peak cladding temperature during reflooding phase after LOCA. The system employs a narrow rectangular channel for conducting top reflooding experiments, wherein the peak temperature is predicted by the Back Propagation Neural Network (BPNN) algorithm. The system’s primary objective is to accurately predict the peak cladding temperature of various test conditions. First, the special Counter-Current Flow Limitation (CCFL) phenomenon is identified based on the different heat exchange patterns inside the test section. Then the experimental data is collected by LabVIEW and calculated by MATLAB to accurately predict the quench velocity and peak temperature at a specific location by conducting various test conditions. The final experiment results indicate that the built Perception-Interaction system in this article has a high prediction accuracy, with a relative error within 2% between the predicted peak temperatures and actual temperatures.