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MCSIMus: Monte Carlo Simulation with Inline Multiphysics

MCSIMus: Monte Carlo Simulation with Inline Multiphysics
MCSIMus:使用内联多物理场进行蒙特卡罗仿真
批准号:
EP/W037165/1
负责人:
Paul Cosgrove
金额:
$44.47万
依托单位:
依托单位国家:
英国
项目类别:
Fellowship
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --

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中文摘要
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英文摘要
Nuclear reactors in various forms are increasingly prominent in the context of net zero. However, stringent safety standards and advanced reactor designs necessitate ever-greater certainty and understanding in reactor physics and operation. As physical experimentation becomes more expensive, nuclear engineering relies increasingly on high-fidelity simulation of reactors. Traditionally, resolving different physical phenomena in a reactor (such as neutron transport or thermal-hydraulics) proceeded by assuming only a weak dependence upon other phenomena due to limits on computational power. Such approximations were allowable when additional conservatisms were included in reactor designs. However, more economical or sophisticated reactor designs render such approximations invalid, and reactor designers must be able to resolve the interplay between each physical phenomenon. This poses a challenge to reactor physicists due to vastly increased computational costs of multi-physics calculations, as well as the risks of numerical instabilities - these are essentially non-physical behaviours which are purely an artefact of simulation.This proposal aims to provide the basis of new computational approaches in nuclear engineering which are both substantially cheaper and more stable than present multi-physics approaches. Traditional methods tend to have one tool fully resolve one phenomenon, pass the information to another tool which resolves a second phenomenon, and then pass this updated information back to the first tool and repeat until (hopefully) the results converge. This proposal hopes to explore a slightly simpler approach, where information is exchanged between different solvers before each has fully resolved its own physics, extending this to many of the phenomena of interest to a reactor designer. Preliminary analysis suggests that this approach should be vastly more stable and computationally efficient than previous methods. The investigations will be carried out using home-grown numerical tools developed at the University of Cambridge which are designed for rapid prototyping of new ideas and algorithms. The final result is anticipated to transform the nuclear industry's approach to multi-physics calculations and greatly accelerate our ability to explore and design more advanced nuclear reactors.
期刊论文(3)
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科研奖励(0)
会议论文
The Random Ray Method Versus Multigroup Monte Carlo: The Method of Characteristics in OpenMC and SCONE
随机射线方法与多组蒙特卡罗:OpenMC 和 SCONE 中的特征方法
DOI: 10.1080/00295639.2023.2270618
发表时间: 2023
期刊: Nuclear Science and Engineering
影响因子: 1.2
作者: [Cosgrove P]
通讯作者: Cosgrove P
A memory-efficient neutron noise algorithm for reactor physics
用于反应堆物理的内存高效中子噪声算法
DOI: 10.1016/j.anucene.2024.110450
发表时间: 2024
期刊: Annals of Nuclear Energy
影响因子: 1.9
作者: [Cosgrove P]
通讯作者: Cosgrove P
国内基金
海外基金
DDH头臼匹配性三维空间形态表征及PAO 手术髋臼重定向Monte Carlo随机最优控 制
  • 批准号:
  • 项目类别:
    省市级项目
  • 资助金额:
    10.0万元
  • 批准年份:
    2025
  • 负责人:
    杨鹏
  • 依托单位:
复杂空间上具有特殊约束的Monte Carlo方法
  • 批准号:
    12371269
  • 项目类别:
    面上项目
  • 资助金额:
    43.5万元
  • 批准年份:
    2023
  • 负责人:
    邓柯
  • 依托单位:
基于鞘层Monte Carlo粒子仿真模型的非稳态真空弧等离子体羽流的内外流一体化数值模拟研究
基于格子Boltzmann和Monte Carlo方法的中子输运本构关系及低维控制方程研究
  • 批准号:
    --
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    30万元
  • 批准年份:
    2022
  • 负责人:
    王亚辉
  • 依托单位: