Advanced Multi-Modeling of PWR Dynamics and Deep Learning based Computational Tool in SIMULINK and LabVIEW

Abstract

The reactivity monitoring, prediction, and investigation is the most important parameter to ensure the safety and reliable operation of a nuclear power plant. This parameter is gained further importance in Pressurized Water Reactor (PWR) due to more sophisticated reactivity insertion mechanisms and innovative reactor core fuel loading scheme. Based on deterministic internal and external dynamics and neutronics analysis of Advanced PWR, all the reactivity feedback effects such as Doppler effect, moderator effect, control rod effect, liquid boron effect and reactor poisons effect are investigated, modeled and stochastically optimized using deep artificial intelligence. Advance Pressurized Water Reactor (APWR) of 600 MWe rating (AP-600) is used as a reference reactor model and based on the dynamics of AP-600, an Advanced Pressurized Water Reactor Dynamics and Intelligent Stochastic Optimization based Deterministic Neutronics Simulation (APD-ISO-DNS) Code is developed in the hybrid SIMULINK andLabVIEW environments. AP-600 reactor model is fine-tuned and adjusted for 300 MWe PWR (P-300) and 1070 MWe Advanced Chinese PWR (ACP-1000) using neutronics parameters and operational dynamic data of operating PWR nuclear power plants in Pakistan. Various load reduction transient experiments are conducted and dynamic transient simulations of P-300, AP-600 and ACP-1000 are evaluated in SIMULINK and in LabVIEW environments and found as per design basis.