HUANG Yu, LIU Junqiang, LIU Le. Application of BP neural network in DNBR prediction[J]. Nuclear techniques, 2015, 38(7): 070606 DOI: 10.11889/j.0253-3219.2015.hjs.38.070606.
Background: In safety analysis of pressurized water reactor (PWR)
departure from nucleate boiling ratio (DNBR) is usually calculated by three codes: a system transient analysis code
a heat flux calculation code and a subchannel analysis code
or by simplified model through a partial derivative approximation of the core DNB limit lines
but either procedure has problems of cumbersome or low accuracy. Purpose: The aim of this study is to gain a simple DNBR calculation method with high accuracy. Methods: A 3-layers back propagation (BP) neural network was proposed with a training data set to quickly predict DNBR using four variables of reactor coolant system (nuclear power
core inlet temperature
mass flow rate and pressure). Results: The error of the developed BP network is very small
and has similar results compared with the subchannel code calculations in two typical events. Conclusion: The trained BP network is accurate enough to be used in predicting DNBR