Ming Lin
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Ming Lin has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Maosong Cheng
Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
3 shared publications - Ke Xu
Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
1 shared publication - Xiandi Zuo
Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
1 shared publication - Yuqing Dai
Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
1 shared publication - Zhengyang Zhou
Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
1 shared publication Zhi-Min Dai
Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China
1 shared publication
Latest Publications
- Open Access
A Parametric Proper Orthogonal Decomposition–Higher-Order Dynamic Mode Decomposition Framework for Reduced-Order Multiphysics Modeling of Molten Salt Reactors
Transient analyses of liquid-fueled molten salt reactors involve strong coupling among neutronics, delayed neutron precursor transport, thermal–hydraulics, and solid heat transfer, leading to high computational costs for repeated high-fidelity simulations. To enable fast multi-physics prediction at unseen operating conditions, a parametric non-intrusive reduced-order model (ROM) combining proper orthogonal decomposition (POD) and higher-order dynamic mode decomposition (HODMD) is developed. Coupled full-order snapshots generated from an OpenFOAM-based one-eighth symmetric core model based on a simplified MSRE benchmark configuration are used to construct reduced representations for 11 physical fields. The POD truncation rank, HODMD delay dimension, and interpolation model are selected using leave-one-out cross-validation, with polynomial, radial basis function, and Gaussian process regression models considered as interpolation candidates. For unseen parameter points, the model maintains high accuracy in both the interpolation stage and the temporal extrapolation stage. In the temporal extrapolation stage, the highest mean relative error for the inlet-temperature-step case is , whereas all mean relative errors for the inlet-velocity-step case remain below . The results indicate that, under the present cases and parameter settings, the proposed framework provides an accurate and rapid surrogate for multi-physics transient prediction.Open AccessA Parametric Proper Orthogonal Decomposition–Higher-Order Dynamic Mode Decomposition Framework for Reduced-Order Multiphysics Modeling of Molten Salt Reactors
Transient analyses of liquid-fueled molten salt reactors involve strong coupling among neutronics, delayed neutron precursor transport, thermal–hydraulics, and solid heat transfer, leading to high computational costs for repeated high-fidelity simulations. To enable fast multi-physics prediction at unseen operating conditions, a parametric non-intrusive reduced-order model (ROM) combining proper orthogonal decomposition (POD) and higher-order dynamic mode decomposition (HODMD) is developed. Coupled full-order snapshots generated from an OpenFOAM-based one-eighth symmetric core model based on a simplified MSRE benchmark configuration are used to construct reduced representations for 11 physical fields. The POD truncation rank, HODMD delay dimension, and interpolation model are selected using leave-one-out cross-validation, with polynomial, radial basis function, and Gaussian process regression models considered as interpolation candidates. For unseen parameter points, the model maintains high accuracy in both the interpolation stage and the temporal extrapolation stage. In the temporal extrapolation stage, the highest mean relative error for the inlet-temperature-step case is , whereas all mean relative errors for the inlet-velocity-step case remain below . The results indicate that, under the present cases and parameter settings, the proposed framework provides an accurate and rapid surrogate for multi-physics transient prediction. - Open Access
Applicability Analysis of Reduced-Order Methods with Proper Orthogonal Decomposition for Neutron Diffusion in Molten Salt Reactor
The high-dimensional integral–differential nature of the neutron transport equation and the complexity of nuclear reactors result in high computational costs. A set of reduced-order modeling frameworks based on Proper Orthogonal Decomposition (POD) is developed to improve the computational efficiency for neutron diffusion calculations while maintaining accuracy, especially for small samples. For modal coefficient calculations, three methods—Galerkin, radial basis function (RBF), and Deep Neural Network (DNN)—are introduced and analyzed for molten salt reactors. The results show that all three reduced-order models achieve sufficient accuracy, with neutron flux L2 errors below 1% and delayed neutron precursor (DNP) L2 errors below 2.4%, while the acceleration ratios exceed 800. Among these, the POD–Galerkin model demonstrates superior performance, achieving average L2 errors of less than 0.00658% for neutron flux and 1.01% for DNP concentration, with an acceleration ratio of approximately 1800 and excellent extrapolation ability. The POD–Galerkin reduced-order model significantly enhances the computational efficiency for solving neutron multi-group diffusion equations and DNP conservation equations in molten salt reactors while preserving the solution accuracy, making it ideal for a liquid fuel molten salt reactor in the case of small samples.
Open AccessApplicability Analysis of Reduced-Order Methods with Proper Orthogonal Decomposition for Neutron Diffusion in Molten Salt Reactor
The high-dimensional integral–differential nature of the neutron transport equation and the complexity of nuclear reactors result in high computational costs. A set of reduced-order modeling frameworks based on Proper Orthogonal Decomposition (POD) is developed to improve the computational efficiency for neutron diffusion calculations while maintaining accuracy, especially for small samples. For modal coefficient calculations, three methods—Galerkin, radial basis function (RBF), and Deep Neural Network (DNN)—are introduced and analyzed for molten salt reactors. The results show that all three reduced-order models achieve sufficient accuracy, with neutron flux L2 errors below 1% and delayed neutron precursor (DNP) L2 errors below 2.4%, while the acceleration ratios exceed 800. Among these, the POD–Galerkin model demonstrates superior performance, achieving average L2 errors of less than 0.00658% for neutron flux and 1.01% for DNP concentration, with an acceleration ratio of approximately 1800 and excellent extrapolation ability. The POD–Galerkin reduced-order model significantly enhances the computational efficiency for solving neutron multi-group diffusion equations and DNP conservation equations in molten salt reactors while preserving the solution accuracy, making it ideal for a liquid fuel molten salt reactor in the case of small samples.
- 14 March 2020
- Published by Springer Nature in Nuclear Science and Techniques
- Vol. 31 (4) , 1-15
Feasibility of an innovative long-life molten chloride-cooled reactor
- 14 March 2020
- Published by Springer Nature in Nuclear Science and Techniques
- Vol. 31 (4) , 1-15
Feasibility of an innovative long-life molten chloride-cooled reactor