Improved Beam Dynamics Through Control of Magnetic Fringe Fields
Improved Beam Dynamics Through Control of Magnetic Fringe Fields
批准号:
2601482
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2021
资助国家:
英国
项目状态:
未结题
起止时间:
2021 至 --
中文摘要
粒子加速器是在各种科学领域进行研究的必要工具。例如,哈威尔的钻石同步加速器光源通过让研究人员更好地了解SARS-CoV-2病毒的结构生物学,在英国开发有效的新冠肺炎疫苗的工作中发挥了重要作用。像钻石这样的加速器依靠磁铁来控制高能粒子束。偶极磁铁提供几乎均匀的磁场并负责波束控制,而四极磁铁(由四个磁极组成)产生一个更复杂的磁场来聚焦光束。高阶多极子用于其他目的,如色度校正和补偿低阶多极子中的场误差。在磁体的主体中,磁场相对简单,并且可以假定沿束轴线是恒定的。然而,在磁铁的入口处和出口处,磁场更复杂,因为它逐渐减小到零。这一中间区域被称为条纹场,其特征在于它依赖于沿光束轴的位置,以及依赖于远离光轴的距离。偶极子中的条纹场效应相对简单,可以在新加速器设计的早期阶段实现。然而,在四极和更高阶多极中,边缘场效应可能更难预测,而且通常在磁体本身的设计接近完成之前不会详细描述。在加速器设计过程中,通常太晚或太昂贵来修正条纹场的不利影响。这个项目的目标是开发技术来表征多极的条纹场,将条纹场的关键特征与磁体的设计联系起来。反向工程该过程将(原则上)允许磁体设计过程从一开始就考虑到条纹场,从而避免对束流的不利影响,并潜在地提供额外的手段来控制束流特性。该项目的一个重要部分将是开发工具,以便能够快速探索磁铁设计具体细节变化的影响。目前,磁体设计依赖于使用详细的建模代码,这使得设计过程计算量大、耗时长。使用机器学习中最新进展的替代方法将允许更快地表征边缘场。该项目的成功将有助于简化磁体设计流程,并允许更有效地控制粒子加速器中的束流,提高加速器性能并降低设计和建造成本。
英文摘要
Particle accelerators are essential tools for research in a wide variety of scientific fields. The Diamond synchrotron light source at Harwell, for example, played an important role in the UK's work to develop an effective COVID-19 vaccine, by providing researchers with a better understanding of the structural biology of the SARS-CoV-2 virus. Accelerators such as Diamond depend on magnets to control the beams of high-energy particles. Dipole magnets provide nearly uniform fields and are responsible for beam steering, and quadrupole magnets (consisting of four poles) create a more complex magnetic field that focuses the beam. Higher order multipoles serve other purposes such as chromatic correction and compensation for field errors in the lower order multipoles. In the main body of a magnet, the magnetic field is relatively simple, and can be assumed to be constant along the beam axis. At the entrance and exit of a magnet, however, the field is more complex as it tapers to zero. This intermediate region is known as the fringe field, and is characterised by its dependence on position along the beam axis, as well as a dependence on distance away from the axis. The effects of fringe fields in dipoles are relatively simple to describe, and can be implemented in the early stages of the design of a new accelerator. In quadrupoles and higher order multipoles, however, the fringe field effects can be more difficult to predict, and are often not described in detail until the design of the magnet itself is near completion. At that point in the accelerator design process, it is usually too late or too expensive to correct for adverse effects of the fringe fields.The goal of this project is to develop techniques for characterising fringe fields in multipoles, linking key features of the fringe field to the design of the magnet. Reverse engineering this process would (in principle) allow for the magnet design process to take account of the fringe fields from the beginning, avoiding adverse effects on the beam and potentially providing additional means to control the beam properties. An important part of the project will be to develop tools to allow rapid exploration of the effects of changes in specific details of the magnet design. At present, magnet design relies on the use of detailed modelling codes, which makes the design process computationally intensive and time-consuming. Alternative approaches that use recent advances in machine learning would allow more rapid characterisation of fringe fields. The success of this project would help to streamline the magnet design process, and allow more effective control of the beams in particle accelerators, improving accelerator performance and reducing design and construction costs.
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