Nuclear Safety Uncertainty Development Project
Nuclear Safety Uncertainty Development Project
批准号:
567028-2021
负责人:
Novog, DavidDRN
金额:
$14.82万
依托单位:
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
项目状态:
已结题
起止时间:
2022-01-01 至 2023-12-31
中文摘要
在一个前所未有的能源政策信号中,联邦政府和四个省已表示打算利用低碳核能来满足现有和未来的电力需求。该联盟提案由Novog博士和核工程卓越大学网络(UNENE)共同开发,以生成现有CANDU反应堆所需的新实验数据和模型,以及与小型模块化反应堆(SMR)相关的新兴机会。长期存在的安全分析预测和不确定性评估的基本问题将在这个项目中得到解决。该提案旨在:(一)改进与核燃料完整性有关的测量和模型,包括开发用于测量空隙率和临界热通量现象的先进仪器;(二)开发下一代多物理场建模和不确定性分析方法;以及(三)开发新的风险知情工具,用于SMR事故评估和应急规划。该项目的实验阶段将使用先进的计算机断层扫描系统和新的单光子计数放射照相术,收集与燃料和燃料通道完整性有关的重要现象的计算机断层扫描和放射照相图像。工作的建模阶段检查数据同化,贝叶斯框架和机器学习方法的应用,以提高多物理场预测中不确定性的知识。最后,该项目将研究在了解风险的情况下描述SMR特征所需的整套物理模型,为此将根据蒙特-卡罗方法为这些评估制定一个新的框架。该提案重点考虑了核安全界的公平、多样性和包容性(EDI)问题,与Novog博士领导的现有EDI倡议的联系,以及高素质人员(HQP)的培训。
英文摘要
In an unprecedented energy policy signal, the federal government and four provinces have stated their intention to utilize low-carbon nuclear energy to meet existing and future electricity needs. This Alliance Proposal was developed jointly between Dr. Novog and University Network of Excellence in Nuclear Engineering (UNENE) to generate new experimental data and models needed for existing CANDU reactors as well as the emerging opportunities related to Small Modular Reactors (SMRs). Long standing fundamental issues on safety analysis predictions and uncertainty assessments will be addressed in this project. This proposal aims to i) improve measurements and models related to nuclear fuel integrity including development of advanced instrumentation for measurement of void fraction and Critical Heat Flux (CHF) phenomena, ii) develop the next generation of Multiphysics modelling and uncertainty analysis methodologies, and iii) develop new risk-informed tools for assessment of SMR accidents and emergency planning. The experimental phase of the project will collect Computerized Tomography (CT) and radiograph images of important phenomena related to fuel and fuel channel integrity using advanced CT systems and new single-photon counting based radiography. The modelling phases of the work examine the application of data assimilation, Bayesian frameworks, and machine learning methods for improved knowledge of uncertainties in Multiphysics predictions. Finally, the project will look at the entire suite of physical models needed for risk-informed characterization of SMRs by developing a new framework for these assessments based on Monte-Carlo methodologies. Significant consideration is given in this proposal to Equity, Diversity and Inclusion (EDI) issues within the nuclear safety community, the linkages to existing EDI initiatives led by Dr. Novog, and the training of Highly Qualified Personnel (HQP).
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会议论文
Small Modular Advanced Reactor Training (SMART) CREATE
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批准号:528176-2019
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项目类别:Collaborative Research and Training Experience
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资助金额:$21.84万
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财政年份:2022
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负责人:Novog, DavidDRN
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依托单位:
海外基金