A Reduced Order Model Based on ANN-POD Algorithm for Steady-State Neutronics and Thermal-Hydraulics Coupling Problem

The neutronics and thermal-hydraulics (N/TH) coupling behavior analysis is a key issue for nuclear power plant design and safety analysis. Due to the high-dimensional partial differential equations (PDEs) derived from the N/TH system, it is usually time consuming to solve such a large-scale nonlinea...

Full description

Saved in:
Bibliographic Details
Main Authors: Hanxing Liu, Han Zhang
Format: Article
Language:English
Published: Wiley 2023-01-01
Series:Science and Technology of Nuclear Installations
Online Access:http://dx.doi.org/10.1155/2023/9385756
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1832554107949285376
author Hanxing Liu
Han Zhang
author_facet Hanxing Liu
Han Zhang
author_sort Hanxing Liu
collection DOAJ
description The neutronics and thermal-hydraulics (N/TH) coupling behavior analysis is a key issue for nuclear power plant design and safety analysis. Due to the high-dimensional partial differential equations (PDEs) derived from the N/TH system, it is usually time consuming to solve such a large-scale nonlinear equation by the traditional numerical solution method of PDEs. To solve this problem, this work develops a reduced order model based on the proper orthogonal decomposition (POD) and artificial neural networks (ANNs) to simulate the N/TH coupling system. In detail, the POD method is used to extract the POD modes and corresponding coefficients from a set of full-order model results under different boundary conditions. Then, the backpropagation neural network (BPNN) is utilized to map the relationship between the boundary conditions and POD coefficients. Therefore, the physical fields under the new boundary conditions could be calculated by the predicated POD coefficients from ANN and POD modes from snapshot. In order to assess the performance of an ANN-POD-based reduced order method, a simplified pressurized water reactor model under different inlet coolant temperatures and inlet coolant velocities is utilized. The results show that the new reduced order model can accurately predict the distribution of the physical fields, as well as the effective multiplication factor in the N/TH coupling nuclear system, whose relative errors are within 1%.
format Article
id doaj-art-aaac4a18a314471d93bcce7caf05f4c7
institution Kabale University
issn 1687-6083
language English
publishDate 2023-01-01
publisher Wiley
record_format Article
series Science and Technology of Nuclear Installations
spelling doaj-art-aaac4a18a314471d93bcce7caf05f4c72025-02-03T05:52:22ZengWileyScience and Technology of Nuclear Installations1687-60832023-01-01202310.1155/2023/9385756A Reduced Order Model Based on ANN-POD Algorithm for Steady-State Neutronics and Thermal-Hydraulics Coupling ProblemHanxing Liu0Han Zhang1Department of Engineering PhysicsInstitute of Nuclear and New Energy Technology (INET)The neutronics and thermal-hydraulics (N/TH) coupling behavior analysis is a key issue for nuclear power plant design and safety analysis. Due to the high-dimensional partial differential equations (PDEs) derived from the N/TH system, it is usually time consuming to solve such a large-scale nonlinear equation by the traditional numerical solution method of PDEs. To solve this problem, this work develops a reduced order model based on the proper orthogonal decomposition (POD) and artificial neural networks (ANNs) to simulate the N/TH coupling system. In detail, the POD method is used to extract the POD modes and corresponding coefficients from a set of full-order model results under different boundary conditions. Then, the backpropagation neural network (BPNN) is utilized to map the relationship between the boundary conditions and POD coefficients. Therefore, the physical fields under the new boundary conditions could be calculated by the predicated POD coefficients from ANN and POD modes from snapshot. In order to assess the performance of an ANN-POD-based reduced order method, a simplified pressurized water reactor model under different inlet coolant temperatures and inlet coolant velocities is utilized. The results show that the new reduced order model can accurately predict the distribution of the physical fields, as well as the effective multiplication factor in the N/TH coupling nuclear system, whose relative errors are within 1%.http://dx.doi.org/10.1155/2023/9385756
spellingShingle Hanxing Liu
Han Zhang
A Reduced Order Model Based on ANN-POD Algorithm for Steady-State Neutronics and Thermal-Hydraulics Coupling Problem
Science and Technology of Nuclear Installations
title A Reduced Order Model Based on ANN-POD Algorithm for Steady-State Neutronics and Thermal-Hydraulics Coupling Problem
title_full A Reduced Order Model Based on ANN-POD Algorithm for Steady-State Neutronics and Thermal-Hydraulics Coupling Problem
title_fullStr A Reduced Order Model Based on ANN-POD Algorithm for Steady-State Neutronics and Thermal-Hydraulics Coupling Problem
title_full_unstemmed A Reduced Order Model Based on ANN-POD Algorithm for Steady-State Neutronics and Thermal-Hydraulics Coupling Problem
title_short A Reduced Order Model Based on ANN-POD Algorithm for Steady-State Neutronics and Thermal-Hydraulics Coupling Problem
title_sort reduced order model based on ann pod algorithm for steady state neutronics and thermal hydraulics coupling problem
url http://dx.doi.org/10.1155/2023/9385756
work_keys_str_mv AT hanxingliu areducedordermodelbasedonannpodalgorithmforsteadystateneutronicsandthermalhydraulicscouplingproblem
AT hanzhang areducedordermodelbasedonannpodalgorithmforsteadystateneutronicsandthermalhydraulicscouplingproblem
AT hanxingliu reducedordermodelbasedonannpodalgorithmforsteadystateneutronicsandthermalhydraulicscouplingproblem
AT hanzhang reducedordermodelbasedonannpodalgorithmforsteadystateneutronicsandthermalhydraulicscouplingproblem