PACKAGE FLOW MODELS BY NEURAL NETWORK REPRESENTATION FOR UNDERSTANDING THE DYNAMIC BEHAVIOR OF NUCLEAR REACTOR SYSTEMS

摘要

''Package Flow Model'' (PFM) is a simple simulation model for intuitive understanding of various types of system dynamics. In the previous papers, the PFM was proposed and its application to the dynamic analysis of nuclear reactor systems was presented. In the present paper, the same model and same application are considered but a new representation method of the PFMs by a neural network is introduced, so that the dynamic simulation of the reactor subsystem can be performed through the calculation of corresponding neural network. Furthermore, the quasi optimum parameter values of each PFM are easily obtained by applying appropriate learning algorithm to get weight-values of the neural network. Some case studies show that the learning process and the obtained optimum values can give us new useful information on approximate understanding of the dynamic behavior of actual processes in the system. [References: 6]