ACCIDENT SEQUENCE SCREENING METHOD BASED ON COMBINATION OF FCNN AND PSO

Original (Chinese)

Provided is an accident sequence screening method based on a combination of a fully connected neural network (FCNN) and particle swarm optimization (PSO), relating to the technical field of accident sequence screening, comprising the following steps: Sdefining a research object and a target parameter, and completing deterministic and probabilistic modeling; Sconcurrently computing RELAP5 programs by using a concurrent computing method, to quickly construct a deep learning database; Sconstructing a deep learning surrogate model by using an FNCC analysis method, to replace RELAP5 for accident analysis; and Scalling the deep learning surrogate model for accident analysis by using a PSO approach, quickly capturing an optimal solution for each accident sequence, and screening out sequences that require Best Estimate Plus Uncertainty (BEPU) analysis. In this method, a surrogate model is constructed based on a fully connected neural network to replace a conventional system simulation program, which improves the efficiency of single accident analysis; optimization calculations are performed for the constructed surrogate model by using a PSO algorithm, which reduces the amount of analysis calculations.