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裂缝是长期暴露于反应堆腐蚀性环境和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能力，用于预测这种不稳定的裂纹扩展。EDF Energy将在未来的安全性案例中使用该方法。使用机器学习（ML）技术，统计分析和图论，学生将进一步改善这些基于物理的模型从真实的操作环境中获得的数据。这些技术还将使砖的客观选择能够进行进一步的分析，这是不确定性的主要来源。它也将是可能的分类砖的裂纹形态。同样，将用基于物理模型的数据补充运行数据，以改进寿命延长方面的决策。ML还将使模型参数的不确定性得以传播，并探索其影响。