H
Hongchun Wu
Researcher at Xi'an Jiaotong University
Publications - 269
Citations - 1854
Hongchun Wu is an academic researcher from Xi'an Jiaotong University. The author has contributed to research in topics: Neutron transport & Engineering. The author has an hindex of 17, co-authored 231 publications receiving 1297 citations.
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A new high-fidelity neutronics code NECP-X
TL;DR: A new high-fidelity deterministic neutronics code NECP-X is introduced being developed at Nuclear Engineering Computational Physics (NECP) lab at Xi’an Jiaotong University, which has its own separate features such as a pseudo-resonant-nuclide subgroup method, a new free-matrix CMFD acceleration method and an axial SN solver.
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A new three-dimensional method of characteristics for the neutron transport calculation
TL;DR: Numerical results demonstrate that the modular ray tracing technique can significantly reduce the amount of ray tracing data, and the CMFD acceleration method is effective in shorting the computing time.
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The pseudo-resonant-nuclide subgroup method based global–local self-shielding calculation scheme
TL;DR: The pseudo-resonant-nuclide subgroup method (PRNSM) based global-local self-shielding calculation scheme is proposed in this paper to simultaneously resolve the local selfshielding effects (including spatial self-Shielding effect and the resonance interference effect) for large-scale problems in reactor physics calculations.
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NECP-Atlas: A new nuclear data processing code
TL;DR: The methods used in the current version of NECP-Atlas are described and the performance and accuracy of the code is demonstrated on a variety of benchmarks.
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NECP-MCX: A hybrid Monte-Carlo-Deterministic particle-transport code for the simulation of deep-penetration problems
Qingming He,Qi Zheng,Jie Li,Hongchun Wu,Wei Shen,Wei Shen,Liangzhi Cao,Zhouyu Liu,Jialong Xu +8 more
TL;DR: NECP-MCX is able to simulate deep-penetration problems with higher efficiency compared to the conventional MC codes and utilizes the deterministic method to generate consistent mesh-based weight-window and source-biasing parameters for the MC method to reduce variance.