Using multi-spectral imaging and advanced data analysis to improve the tokamak fusion concept.

利用多光谱成像和先进的数据分析来改进托卡马克聚变概念。

• 批准号: 2602602
• 金额: --
• 依托单位: University of York
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2602602
• 关键词: Using; multi; spectral; imaging; advanced

I studied for both my undergraduate and master degrees in Physics at the University of Padua, in Italy. My interest in nuclear fusion started during my bachelor thesis research on developing a convolutional neural network for the analysis of infrared imaging system data on the SPIDER experiment. It then continued during my masters degree through elective courses and it culminated in my masters thesis on the study of parametric decay instabilities during Electron Cyclotron Resonance (ECR) heating on the NORTH tokamak at the Technical University of Denmark. My PhD project, supervised by Prof. Bruce Lipschultz and Prof. Kieran Gibson at the University of York and Dr. James Harrison at CCFE, involves the study of the divertor region of the MAST-U tokamak through the new Multi Wavelength-Imaging (MWI) diagnostic. This diagnostic allows researchers to acquire eleven video movies corresponding to 2D brightness images of the divertor region, each filtered for a different wavelength. The profiles have different dependencies on the plasma parameters and by putting them together with the data from the other diagnostics, these parameters can be reconstructed. The MWI data will be integrated in the Bayesian framework currently being used to determine the divertor state, thereby improving its capability. Handling the large exhaust heat loads corresponding to that of a reactor is one of the main problems on the way to commercial fusion tokamaks providing a net power source. The magnetic topology of the divertor (where the exhaust power is carried towards surfaces) in MAST-U is flexible and the MWI diagnostic will help in determining the most appropriate divertor configuration to optimize the dissipation of power and thus reduce peak power loads.

我在意大利帕多瓦大学攻读物理学本科和硕士学位。我对核聚变的兴趣始于我的学士论文研究，该论文研究的是开发卷积神经网络，用于分析 SPIDER 实验中的红外成像系统数据。然后，在我攻读硕士学位期间，通过选修课程继续学习，最终形成了我的硕士论文，主题是研究丹麦技术大学北托卡马克电子回旋共振 (ECR) 加热过程中的参数衰变不稳定性。我的博士项目由约克大学的 Bruce Lipschultz 教授和 Kieran Gibson 教授以及 CCFE 的 James Harrison 博士指导，涉及通过新的多波长成像 (MWI) 对 MAST-U 托卡马克的偏滤器区域进行研究）诊断。这种诊断使研究人员能够获取与偏滤器区域的 2D 亮度图像相对应的 11 个视频电影，每个视频电影都针对不同的波长进行过滤。这些曲线对等离子体参数有不同的依赖性，通过将它们与其他诊断的数据放在一起，可以重建这些参数。 MWI 数据将集成到目前用于确定偏滤器状态的贝叶斯框架中，从而提高其能力。处理相当于反应堆的大量废热负荷是提供净电源的商业聚变托卡马克的主要问题之一。 MAST-U 中偏滤器的磁拓扑（其中废气功率被输送到表面）非常灵活，MWI 诊断将有助于确定最合适的偏滤器配置，以优化功率耗散，从而降低峰值功率负载。