Digital Twin Model Development for Through-Life Management of the Divertor in a Fusion Power Plant

我于2017年在约克大学完成了物理学本科学位。后来，我决定我想和那些有复杂需求的人一起工作，比如脑损伤，我发现这非常值得。2018年，我开始了数据方面的职业生涯，先是在一家临床试验测试实验室，然后在一家医疗和温控包装公司工作。2020年，我在牛津一家医学成像软件初创公司工作，在那里我帮助建立了深度学习神经网络模型，以描绘CT和MR扫描中的结构。从2022年开始，我一直在核工程行业担任数据科学家，之所以做出这种转变，是因为我从大学开始就一直对聚变能感兴趣。在我找到一个适合我的数据技能和物理知识的项目后，这使我走向了Fusion CDT。导流器是MCF中的一个关键部件，特别是在使用h模等离子体的球形托卡马克中。该分流器能够通过排出杂质和去除热量来处理超过x点的等离子体效应。因此，它将封装托卡马克的一个区域，其中可以找到关键的诊断。但目前，导流器的设计、建模和控制都非常复杂。因此，有必要开发一个转向器的数字孪生体来捕获模型和数据，以提供更完整的反应器图像。这将有助于了解材料和组件的状态，提醒用户进行预测性维护，并使用等离子体和面向等离子体的材料模型运行假设场景。混合建模方法的一个关键方面是对多物理场和数据驱动模型进行定量验证，并通过物理测试和量化不确定性来建立故障预测的可信度。这些对未来核聚变电站的设计、制造、实施和全寿命管理都是非常有用的技能。这个博士项目是由Assystem共同资助的，Assystem是一家全球性的核工程公司，专门从事工程、数字和项目管理。Assystem拥有核裂变和核聚变方面的知识和经验，例如支持英国原子能机构在Culham的核聚变实验，以及作为ITER的架构工程师。

I completed my undergraduate degree in Physics at the University of York in 2017. Afterwards, I decided I wanted to work with people with complex needs such as brain injuries and found this incredibly rewarding. In 2018, I started my career in data roles, first in a clinical trials testing laboratory and then at a medical and temperature-controlled packaging company. In 2020, I worked at a medical imaging software start-up in Oxford where I helped build deep learning neural network models to delineate structures within CT and MR scans. From 2022, I have worked as a data scientist within a nuclear engineering industry, making this transition because I have always been interested in fusion energy since university. This led me towards the Fusion CDT after I found a project which fit both my data skills and physics knowledge.Divertors are a key aspect within MCF, especially in spherical tokamaks using a H-mode plasma. The divertor is able to handle plasma effects beyond the X-point via exhausting impurities and removing heat. It will therefore encapsulate an area of the tokamak in which key diagnostics can be found. However, currently, the divertor is a very complex component to design, model and control. Therefore, there is a need to develop a digital twin of the divertor to capture models and data to provide a more complete picture of the reactor. This will help understand the state of the materials and components, alert users to predictive maintenance and run what-if scenarios using models of the plasma and plasma-facing materials. A key aspect of the hybrid modelling approach will be quantitative validation of multi-physics and data-driven models and establishing credibility in failure predictions through physical testing and quantifying uncertainty. These will be very useful skills for future design, manufacturing, implementation and through-life management of future fusion energy plants.This PhD project is being co-funded by Assystem, a global nuclear engineering company with specialities in engineering, digital and project management. Assystem has knowledge and experience in both nuclear fission and fusion such as supporting UKAEA's fusion experiments in Culham and in its role as architect engineer for ITER.