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的开发和应用来应对激光 - 固定相互作用中优化，稳定和控制粒子和辐射的挑战的重大步骤。具体目标是： - 对机器学习驱动的优化技术（贝叶斯优化，神经网络）的应用来优化激光 - 血压辐射源。 - 开发和测试一个新的机器学习平台，基于激光 - 皮质相互作用物理学的细胞模拟中的粒子。实施并证明了使用350TW SCAPA LASER（Strathclyde内部的内部）以及在Rutherford Appleton Laboratory的Epac Laser设施的新方法，使用直接的实验反馈和一个模拟模型，以实验室形式进行了模型。该学生将首先获取有关激光 - 血浆相互作用物理，数值模拟，机器学习技术和实验实现技巧的知识。该项目将继续研究学生在细胞模拟中使用机器学习技术和粒子的组合研究激光驱动的辐射源的学生。确定和测试最佳使用方法将成为项目的一部分。这将有助于为实验演示增强控制和优化的可行性提供信息。然后，使用350TW SCAPA激光器将机器学习方法应用于实时实验，如果有的话，新的EPAC激光器。在该项目中，该学生计划在Scapa Beamlines bunklines Bunker B Target站进行几项实验活动，以优化高度重新重复的速率Laser-Laser-Laser-drevientirenivientirenivientirenivientirenion iniariven Ion-drecter-drecter-drecter-drecter。学生将在计划和交付实验中发挥关键作用。此外，该学生将与外部研究设施（例如中央激光设施）的DSTL研究人员进行合作实验。