Data-driven computational modelling of fracture of nuclear graphite bricks

核石墨砖断裂的数据驱动计算建模

• 批准号: 2387903
• 金额: --
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2387903
• 关键词: Data; driven; computational; modelling; fracture

Cracks in the nuclear graphite core of the UK's advanced gas-cooled reactors are the result of long-term exposure to the reactor's aggressive environment, and degradation of material properties. The University of Glasgow has developed a unique physics-based modelling capability for predicting this unstable crack propagation. This will be used by EDF Energy for future safety cases. Using machine learning (ML) techniques, statistical analysis and graph theory the student will further improve these physics-based models from data obtained from the real operating environment. Such techniques will also enable the objective selection of bricks to undertake further analysis, which a key source of uncertainties. It will also be possible to classify bricks in terms of cracks morphologies. Similarly, operational data will be supplemented with data from the physics-based models for improved decision making in life extension. ML will also enable uncertainty in model parameters to be propagated, and their influence explored.

英国先进气冷反应堆的核石墨堆芯出现裂纹是由于长期暴露在反应堆的侵蚀性环境中以及材料性能退化的结果。格拉斯哥大学开发了一种独特的基于物理的建模功能，用于预测这种不稳定的裂纹扩展。 EDF Energy 将将此用于未来的安全案例。使用机器学习 (ML) 技术、统计分析和图论，学生将根据从真实操作环境获得的数据进一步改进这些基于物理的模型。此类技术还将使砖块的客观选择能够进行进一步的分析，这是不确定性的一个关键来源。还可以根据裂纹形态对砖进行分类。同样，操作数据将得到基于物理模型的数据的补充，以改进延长寿命的决策。机器学习还将使模型参数的不确定性得以传播，并探索它们的影响。