Advanced Multi-Modeling of PWR Dynamics and Deep Learning based Computational Tool in SIMULINK and LabVIEW | [Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences • 2022]

Author(s):

1. Arshad H. Malik: Department of Maintenance Training, Pakistan Atomic Energy Commission,A-104, Block-B, Kazimablad, Model Colony, Karachi,Pakistan

2. Aftab A. Memon: Department of Telecommunication Engineering, Mehran University of Engineering and Technology,Jamshoro, Sindh,Pakistan

3. Feroza Arshad: Department of Management Information System, Pakistan Atomic Energy Commission,B-63, Block-B, Kazimablad, Model Colony, Karachi,Pakistan

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 efects such as Doppler efect, moderator efect, control rod efect, liquid boron efect and reactor poisons efect 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 and LabVIEW 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.

Page(s): 71-81

DOI: DOI not available

Published: Journal: Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences, Volume: 59, Issue: 1, Year: 2022

Keywords:

Artificial Intelligence , Deterministic Reactor Neutronics , Deep Stochastic Optimization , MultiModel PWR Dynamics , Hybrid Simulation

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