A POD reduced-order model for resolving the neutron transport problems of nuclear reactor

Sun, Yue; Yang, Junhe; Wang, Yahui; Li, Zhuo; Ma, Yu

doi:10.1016/j.anucene.2020.107799

Cited by 20 publications

(5 citation statements)

References 35 publications

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“…However, as the width increases, the variance is unimodal, and it is found that deeper models increase variance. Thus, the trend of the generalization error of the neural network with model complexity depends on the relative magnitude of bias and variance (Yang et al, 2020).…”

Section: Frontiers In Energy Researchmentioning

confidence: 99%

Numerical and Experimental Studies on Small/Micro Nuclear Reactors

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Section: Frontiers In Energy Researchmentioning

confidence: 99%

Numerical and Experimental Studies on Small/Micro Nuclear Reactors

2024

Frontiers Research Topics

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“…The POD-based ROM can be categorized into two methods: the inclusive and non-inclusive on the basis of whether they require the governing equation of the original system. ROM based on the POD and Galerkin projection is a representative intrusive method which projects the equation onto the subspace basis vector generated by the POD (Hazenberg et al, 2015;Zhang and Xiang, 2015;Gao et al, 2016;Stabile et al, 2017;German and Ragusa, 2019;Star et al, 2019;Sun et al, 2020). By contrast, the non-intrusive ROM is constructed by combining the POD with interpolation methods such as the RBF (Xiao et al, 2015b), Kriging (Chen et al, 2015), and Smolyak (Xiao et al, 2015a).…”

Section: Introductionmentioning

confidence: 99%

POD-based reduced-order modeling study for thermal analysis of gas-cooled microreactor core

2023

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Small modular reactors require multi-physics coupling calculations to balance economy and stability, due to their compact structures. Traditional tools used for light water reactors are not effective in addressing the several modeling challenges posed by these calculations. The lumped parameter method is commonly used in the thermal analysis for its high computational speed, but it lacks accuracy due to the thermal model is one-dimensional. While computational fluid dynamics software (CFD) can provide high-precision and high-resolution thermal analysis, its low calculation efficiency making it challenging to be coupled with other programs. Proper Orthogonal Decomposition (POD) is one of the Reduced Order Model (ROM) methods employed in this study to reduce the dimensionality of sample data and to improve the thermal modelling of gas-cooled microreactors. In this work, a non-inclusive POD with neural network method is proposed and verified using a transient heat conduction model for a two-dimensional plate. The method is then applied to build a reduced order model of the gas-cooled micro-reactor core for rapid thermal analysis. The results show that the root mean square error of the reactor core temperature is less than 1.02% and the absolute error is less than 8.2°C while the computational cost is reduced by several orders of magnitude, shortening the calculation time from 1.5-hour to real-time display. These findings proved the feasibility of using POD and neural network in the development of ROMs for gas-cooled microreactor, providing a novel approach for achieving precise thermal calculation with minimized computational costs.

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“…In addition, the POD method has also been applied to problems such as fin-tip vortex [13], VCT (vibration correlation technique) [14], rough plate transient flow [15] and other problems. Then, the POD-Galerkin method was proposed and applied in some research to improve the calculation accuracy [16][17][18][19]. In addition, combining with other interpolation methods or models, the POD method can improve the model and obtain more accurate results [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Rapid Prediction of the In Situ Pyrolysis Performance of Tar-Rich Coal Using the POD Method

Wang,

Ye,

et al. 2023

Processes

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In this paper, a POD reduced-order interpolation model for solving the in situ pyrolysis process of tar-rich coal is employed to predict the flow and heat transfer performance in the porous media region so as to save computational resources and realize fast calculations. Numerical simulation using the finite volume method (FVM) is firstly used to obtain sample data, based on the samples through the primary function and spectral coefficients of the solutions. The physical field information and parameter distribution under different conditions of inlet temperature, inlet velocity and permeability are predicted. The results are compared with those of FVM to verify the accuracy of the calculated results. The relative mean deviation (RME) of the results of the POD prediction of each parameter for each working condition was synthesized to be no more than 5%. The performance of in situ pyrolysis of tar-rich coal is then investigated, and the oil and gas production are predicted. As the inlet velocity increases from 0.3 m/s to 0.9 m/s, the fraction of high-quality oil and gas production reaches 0.47 and then decreases to 0.38. Increasing the inlet temperature and permeability has a negative effect on the fraction of high-quality hydrocarbon production, after which the quality fraction of high-quality oil and gas dropped sharply to about 0.22. Porosity has a positive impact on the oil and gas production. When the porosity reaches 0.3, the quality fraction of high-quality oil and gas can reach 0.27.

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A POD reduced-order model for resolving the neutron transport problems of nuclear reactor

Cited by 20 publications

References 35 publications

Numerical and Experimental Studies on Small/Micro Nuclear Reactors

Numerical and Experimental Studies on Small/Micro Nuclear Reactors

POD-based reduced-order modeling study for thermal analysis of gas-cooled microreactor core

Rapid Prediction of the In Situ Pyrolysis Performance of Tar-Rich Coal Using the POD Method

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