Investigation of nuclear material accountancy produced by small modular reactors

小型模块化反应堆产生的核材料核算研究

• 批准号: 580455-2022
• 负责人: Hébert, AlainA
• 金额: $ 8.74万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=754307
• 关键词: Investigation; nuclear; material; accountancy; produced

Small modular reactors (SMR) feature unusual fuel compositions (e.g. plutonium, enriched uranium, reprocessed spent fuel), new physical fuel forms (hexagonal lattices, molten salts, TRISO particles, etc.) and different neutron energies in the core (fast spectra in Advanced Reactor Concept (ARC-100) and some molten salt reactors). A consequence of these differences is that unusual nuclear material accountancy is expected so that resulting spent fuel from SMR is expected to be very different when compared with spent fuel from Canada Deuterium Uranium (CANDU) reactors.Over the last 40 years, Polytechnique Montréal has developed the computer codes DRAGON5 (lattice code) and DONJON5 (full-core simulation code) for deterministic nuclear reactor physics simulations.This research proposal consists in extending and applying DRAGON5 and DONJON5 to new reactor physics applications specific to SMR. Specific contributions are expected:(1) Gather information related to geometries, materials and operational data used in selected SMR concepts (sodium fast reactor, high temperature fast reactor and molten salt reactor);(2) Adapt the resonance self-shielding methodology in DRAGON5 to the specific SMR fuel (molten salt, TRISO particles, etc.);(3) Apply the Bateman solver in DRAGON5 and DONJON5 to SMR fuel;(4) Include SMR characteristics in the CLASS (Core Library for Advanced Simulation Scenarios) scenario simulator. CLASS is a dynamic fuel cycle simulation code based on neural networks that can use DRAGON5/DONJON5 reactor models to produce nuclear data and physical quantities. CLASS allows the analysis of complex nuclear power plants and of their constituting components.The combined benefits of this research will enable the optimization of the SMR deployment in such a way as to minimize radiological risks, to reduce proliferation issues, to evaluate reprocessing capabilities and to better understand the outcome of an accident.

小型模块化反应堆（SMR）具有不寻常的燃料成分（例如钚，浓缩铀，后处理乏燃料），新的物理燃料形式（六角形晶格，熔盐，TRISO颗粒等）。以及堆芯中不同的中子能量（先进反应堆概念（ARC-100）和一些熔盐反应堆中的快谱）。这些差异的一个后果是，预计会有不寻常的核材料衡算，因此，与加拿大氘铀反应堆的乏燃料相比，SMR产生的乏燃料预计会有很大的不同。蒙特利尔理工学院开发了计算机代码DRAGON 5（点阵码）和DONJON 5（全内核仿真代码）确定性核反应堆物理模拟。这项研究计划包括扩展和应用DRAGON 5和DONJON 5到新的反应堆物理针对SMR的应用。预计将作出具体贡献：（1）收集与选定的SMR概念（钠快堆、高温快堆和熔盐堆）中使用的几何形状、材料和运行数据有关的信息;（2）调整DRAGON 5中的共振自屏蔽方法，使之适用于特定的SMR燃料（熔盐、TRISO粒子等）; (3)将DRAGON 5和DONJON 5中的贝特曼求解器应用于SMR燃料;（4）将SMR特性纳入CLASS（高级仿真情景核心库）情景模拟器。CLASS是一个基于神经网络的动态燃料循环模拟程序，可以使用DRAGON 5/DONJON 5反应堆模型来产生核数据和物理量。CLASS允许对复杂的核电站及其组成部件进行分析。这项研究的综合效益将使SMR部署能够优化，从而最大限度地减少放射性风险、减少扩散问题、评估后处理能力并更好地了解事故的结果。