High repetition rate optimisation and control of laser-driven radiation sources

激光驱动辐射源的高重复率优化和控制

• 批准号: 2755548
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2755548
• 关键词: repetition; rate; optimisation; control; laser

The project aims to take major steps towards addressing the challenge to optimise, stabilise and control particle and radiation generation in laser-solid interactions via the development and application of a new machine-learning platform. The specific objectives are to: -Investigate the application of machine learning driven optimisation techniques (Bayesian optimisation, Neural Networks) to the optimisation of laser-plasma radiation sources. -Develop and test a new machine learning platform based on particle in cell simulations of the laser-plasma interaction physics.-Implement and demonstrate the new approach using the 350TW SCAPA laser (in-house at Strathclyde) and at EPAC laser facility at the Rutherford Appleton Laboratory, using direct experimental feedback and a simulation generated surrogate model to optimise laser-driven radiation sources. The student will begin by acquiring knowledge of laser-plasma interaction physics, numerical simulations, machine learning techniques and experimental implementation skills. The project will continue with the student investigating the optimisation of laser-driven radiation sources using a combination of machine learning techniques and particle in cell simulations. Identifying and testing the best approach to use will be part of the project. This will help inform the feasibility of enhanced control and optimisation for experimental demonstration. The machine learning approach will then be applied to a live experiment using the 350TW SCAPA laser, and if available, the new EPAC laser.During the project, the student to contribute to be several experimental campaigns planned to be conducted at the SCAPA beamlines Bunker B target station, on the optimisation of a high repetition rate laser-driven ion source. The student will take a key role in the planning and delivery of the experiment. Additionally, the student will participate in collaborative experiments with DSTL researchers at external research facilities, such as the Central Laser Facility.

该项目旨在通过开发和应用新的机器学习平台，解决优化、稳定和控制激光-固体相互作用中粒子和辐射产生的挑战。具体目标是：-研究机器学习驱动的优化技术（贝叶斯优化，神经网络）在激光等离子体辐射源优化中的应用。- 开发和测试一个新的机器学习平台，该平台基于激光-等离子体相互作用物理学的粒子模拟。使用350 TW SCAPA激光器（斯特拉斯克莱德内部）和卢瑟福阿普尔顿实验室的EPAC激光设施实施和演示新方法，使用直接实验反馈和模拟生成的替代模型来优化激光驱动的辐射源。学生将开始获得激光等离子体相互作用物理学，数值模拟，机器学习技术和实验实施技能的知识。该项目将继续与学生研究激光驱动的辐射源的优化使用机器学习技术和粒子在细胞模拟的组合。确定和测试最佳使用方法将是该项目的一部分。这将有助于为实验演示提供增强控制和优化的可行性信息。机器学习方法将应用于使用350 TW SCAPA激光器的现场实验中，如果可用，新的EPAC激光器。在项目期间，学生将参与计划在SCAPA光束线掩体B靶站进行的几项实验活动，优化高重复率激光驱动离子源。学生将在实验的规划和实施中发挥关键作用。此外，学生将参加与外部研究机构（如中央激光设施）的DSTL研究人员的合作实验。