Ming Lin

Ming Lin

*This is an algorithmically generated author record based on publicly available information.

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Ming Lin has a total of 6 co-authors. Here are the top co-authors by number of shared publications:


Latest Publications

  • Open Access

    A Parametric Proper Orthogonal Decomposition–Higher-Order Dynamic Mode Decomposition Framework for Reduced-Order Multiphysics Modeling of Molten Salt Reactors

    by Ke Xu, Ming Lin, Maosong Cheng

    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 L2 error for the inlet-temperature-step case is 2.112%, whereas all mean relative L2 errors for the inlet-velocity-step case remain below 0.177%. 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

    by Zhengyang Zhou, Ming Lin, Maosong Cheng, Yuqing Dai, Xiandi Zuo

    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.