Neural Network Model for Estimating Departure from Nucleate Boiling Performance of a Pressurized Water Reactor Core

Abstract

A new approach for estimating the departure from nucleate boiling (DNB) performance of a pressurized water reactor core is proposed in which a neural network model is introduced to predict the DNB ratios (DNBRs) for given reactor operating conditions. This model is trained against the detailed simulation results of DNBRs obtained from optimized random input vectors that are generated by Latin hypercube sampling on a wide range of parameters. The trained network is examined to verify the generalized prediction capability of the model. The test results show that a higher level of accuracy in predicting the DNBR can be achieved with the neural network model for both steady-state and transient operating conditions. The neural network model can be developed as a viable tool for on-line DNBR estimation in a nuclear power plant.