Development and validation of reactor nuclear design code CORCA-3D

An, Ping; Ma, Yan; Xiao, Peng; Guo, Fengchen; Lü, Wei; Chai, Xiaoming

doi:10.1016/j.net.2019.05.015

Cited by 20 publications

(9 citation statements)

References 5 publications

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“…In a classical computational setting, numerical models discretize the governing equations, approximating the solution fields in different ways. For example, the finite element method builds the field using an expansion in a finite number of basis functions and the finite difference method builds the field at a set of discrete points in the space-time domain, while for the proprietary code packages such as CORCA-3D [23] and COCAGNE [24], different nodal methods are used which represent the field with node average value followed by pin power reconstruction in each node. Whichever numerical method is chosen, the result is a numerical model that embeds governing equations.…”

Section: Numerical Approximation Of Physical Fieldsmentioning

confidence: 99%

“…Whichever numerical method is chosen, the result is a numerical model that embeds governing equations. For practical nuclear engineering applications, the dimensionality of these numerical models is typically high, e.g., in the range of thousands to millions of unknowns, or even more in three-dimensional time-varying simulations [23,24,7]. This means that a large-scale system of equations has to be solved to evaluate the physical model, which presents computational challenge in simulations requiring i) many-queries e.g., optimal control, inverse problems, uncertainty quantification etc., ii) real-time evaluations e.g., online monitoring, parameter estimation, etc.…”

Section: Numerical Approximation Of Physical Fieldsmentioning

confidence: 99%

“…All the simulations of HPR1000 are based on the advanced node core code package CORCA-3D [23]. CORCA-3D is one of the independent developed codes of NPIC for the nuclear reactor core design.…”

Section: Neutronic Field Simulationmentioning

confidence: 99%

“…The most popular and the simplest choice is Galerkin projection, in which the reduced basis or POD basis is used as the test functions [20,21,22]. In most of the real engineering applications, access to the governing equations, discretization frame and solver generally are unavailable when working with proprietary code or commercial software, in which the full model is implemented a priori and is always viewed as a black-box solver by users [23,24]. Thus the intrusive nature limits the applications of traditional RB methods.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Data-Enabled Physics-Informed Machine Learning for Reduced-Order Modeling Digital Twin: Application to Nuclear Reactor Physics

Gong

Cheng

Chen

et al. 2022

Nuclear Science and Engineering

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This paper proposes an approach that combines reduced-order models with machine learning in order to create physics-informed digital twins to predict high-dimensional output quantities of interest, such as neutron flux and power distributions in nuclear reactor cores. The digital twin is designed to solve forward problems given input parameters, as well as to solve inverse problems given some extra measurements. Offline, we use reduced-order modeling, namely, the proper orthogonal decomposition (POD) to assemble physics-based computational models that are accurate enough for fast predictive digital twin. The machine learning techniques, namely, k-nearest-neighbors (KNN) and decision trees (DT) are used to formulate the input-parameterdependent coefficients of the reduced basis, whereafter the high-fidelity fields are able to be reconstructed. Online, we use the real time input parameters to rapidly reconstruct the neutron field in the core based on the adapted physics-based digital twin. The effectiveness of the framework is illustrated through a real engineering problem in nuclear reactor physics -reactor core simulation in the life cycle of HPR1000 governed by the two-group neutron diffusion equations affected by input parameters, i.e., burnup, control rod inserting step, power level and temperature of the coolant, which shows potential applications for on-line monitoring purpose.

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Section: Numerical Approximation Of Physical Fieldsmentioning

confidence: 99%

Section: Numerical Approximation Of Physical Fieldsmentioning

confidence: 99%

Section: Neutronic Field Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data-Enabled Physics-Informed Machine Learning for Reduced-Order Modeling Digital Twin: Application to Nuclear Reactor Physics

Gong

Cheng

Chen

et al. 2022

Nuclear Science and Engineering

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“…In a classical computational setting, numerical models discrete the governing equations, approximating the solution fields in different ways. The current mature industrial standard codes use different nodal methods to represent the field with node average value followed by pin power reconstruction in each node, such as CORCA-3D [2], COCAGNE [17], SIMULATE-5 [9], ANC9 [14], PARCS [30], DONJON4 [39], DYN3D [10], SCOPE2 [72], Bamboo-Core [77], DIF3D-VARIANT [68] etc. The typical computational time for one simulation of the whole core amounts to 30 seconds as reported in [4].…”

Section: Neutron Field Modeling and Simulation Toolsmentioning

confidence: 99%

An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics

Gong

Cheng

Chen

et al. 2022

Annals of Nuclear Energy

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Physics-constrained neural network for solving discontinuous interface K-eigenvalue problem with application to reactor physics

Yang,

Deng

et al. 2023

NUCL SCI TECH

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Machine learning-based modeling of reactor physics problems has attracted increasing interest in recent years. Despite some progress in one-dimensional problems, there is still a paucity of benchmark studies that are easy to solve using traditional numerical methods albeit still challenging using neural networks for a wide range of practical problems. We present two networks, namely the Generalized Inverse Power Method Neural Network (GIPMNN) and Physics-Constrained GIPMNN (PC-GIPIMNN) to solve K-eigenvalue problems in neutron diffusion theory. GIPMNN follows the main idea of the inverse power method and determines the lowest eigenvalue using an iterative method. The PC-GIPMNN additionally enforces conservative interface conditions for the neutron flux. Meanwhile, Deep Ritz Method (DRM) directly solves the smallest eigenvalue by minimizing the eigenvalue in Rayleigh quotient form. A comprehensive study was conducted using GIPMNN, PC-GIPMNN, and DRM to solve problems of complex spatial geometry with variant material domains from the field of nuclear reactor physics. The methods were compared with the standard finite element method. The applicability and accuracy of the methods are reported and indicate that PC-GIPMNN outperforms GIPMNN and DRM.

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Development and validation of reactor nuclear design code CORCA-3D

Cited by 20 publications

References 5 publications

Data-Enabled Physics-Informed Machine Learning for Reduced-Order Modeling Digital Twin: Application to Nuclear Reactor Physics

Data-Enabled Physics-Informed Machine Learning for Reduced-Order Modeling Digital Twin: Application to Nuclear Reactor Physics

An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics

Physics-constrained neural network for solving discontinuous interface K-eigenvalue problem with application to reactor physics

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