CDS&E:Noise Reduction Methods for Particle Simulations of Intense Beams in Cyclotrons

CDS

• 批准号: 1820852
• 负责人: Antoine Cerfon
• 金额: $ 28.33万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1820852&HistoricalAwards=false
• 关键词: CDS&E; Noise; Reduction; Methods; Particle

A growing number of technologies rely on the production of energetic beams of charged particles in accelerators for their operation. Proton therapy for cancer treatment and food irradiation to eliminate pathogens are among the most remarkable applications. These successes are paving the way for new applications for particle accelerators, such as materials science experiments and nuclear waste transmutation, which would require much more intense beams than are currently available. A difficulty is that at such high beam intensities, beam induced damages on the accelerator structures cannot be tolerated. Achieving a high beam quality throughout the acceleration process thus becomes a critical issue. The dynamics of beams in accelerators is described by equations which can only be accurately solved with long and expensive simulations run on supercomputers. Accelerator design studies therefore involve time consuming, computer intensive simulations, limiting the range of design options which can be investigated. This project addresses this limitation by developing a numerical method that will significantly reduce the run times of existing numerical codes while maintaining a high level of accuracy. By accelerating beam dynamics simulations in accelerators, the new solver will help improve design optimization for the next generation of high intensity accelerators.The purpose of this project is to develop new methods to predict the dynamics of high intensity beams in cyclotrons. The beam dynamics is described by the Vlasov equation, a six-dimensional nonlinear partial differential equation, and quantitative results can only be obtained through numerical simulation. Particle codes based on the Particle-In-Cell (PIC) algorithm have been successfully used for this purpose. Still, a limitation of these codes is that a large number of particles need to be followed to limit statistical error, which leads to long run times. To reduce run times, the PI and graduate students will develop a novel noise reduction technique for the standard PIC algorithm, based on the use of sparse grids. In order to maximize the leverage from this new technique, they will implement high performance algorithms in their sparse-PIC solver, and optimize the sparse grids using model reduction techniques tailored for cyclotron simulations. At every stage of the development of the solver, they will compare the performance of the new code with the state-of-the-art code OPAL. The code will be available to collaborators at major cyclotron facilities to answer unresolved questions regarding the stability of high intensity beams in cyclotrons and to design new machines.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

越来越多的技术依赖于在加速器中产生高能带电粒子束来运行。用于癌症治疗的质子疗法和用于消除病原体的食品辐照是最引人注目的应用之一。这些成功正在为粒子加速器的新应用铺平道路，例如材料科学实验和核废料转化，这将需要比目前可用的更强的束流。一个困难是，在如此高的束流强度下，加速器结构上的束流损伤是不可容忍的。因此，在整个加速过程中实现高光束质量成为一个关键问题。加速器中光束的动力学由方程描述，只有在超级计算机上运行的长而昂贵的模拟才能准确地求解这些方程。因此，加速器设计研究涉及耗时的计算机密集模拟，限制了可调查的设计选择的范围。本项目通过开发一种数值方法来解决这一限制，该方法将显著减少现有数字代码的运行时间，同时保持高水平的精度。通过加速加速器中的束流动力学模拟，新的求解器将有助于改进下一代高强度加速器的设计优化。本项目的目的是开发新的方法来预测回旋加速器中的强流束动力学。光束动力学由Vlasov方程描述，这是一个六维的非线性偏微分方程组，只有通过数值模拟才能得到定量的结果。基于粒子单元(PIC)算法的粒子编码已经成功地用于这一目的。尽管如此，这些代码的一个限制是需要跟踪大量的粒子以限制统计误差，这会导致较长的运行时间。为了减少运行时间，PI和研究生将基于稀疏网格的使用，为标准PIC算法开发一种新的降噪技术。为了最大限度地利用这项新技术，他们将在他们的稀疏PIC解算器中实施高性能算法，并使用为回旋加速器模拟量身定做的模型简化技术来优化稀疏网格。在求解器开发的每个阶段，他们都会将新代码的性能与最先进的代码Opal进行比较。该代码将提供给主要回旋加速器设施的合作者，以回答有关回旋加速器中高强度束流稳定性的悬而未决的问题，并设计新的机器。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Stochastic and a posteriori optimization to mitigate coil manufacturing errors in stellarator design

随机和后验优化可减少仿星器设计中的线圈制造误差

• DOI: 10.1088/1361-6587/ac89ee
• 发表时间: 2022
• 期刊: Plasma Physics and Controlled Fusion
• 影响因子: 2.2
• 作者: Wechsung, Florian;Giuliani, Andrew;Landreman, Matt;Cerfon, Antoine;Stadler, Georg
• 通讯作者: Stadler, Georg

Direct computation of magnetic surfaces in Boozer coordinates and coil optimization for quasisymmetry

布泽坐标系中磁性表面的直接计算和准对称性的线圈优化

• DOI: 10.1017/s0022377822000563
• 发表时间: 2022
• 期刊: Journal of Plasma Physics
• 影响因子: 2.5
• 作者: Giuliani, Andrew;Wechsung, Florian;Stadler, Georg;Cerfon, Antoine;Landreman, Matt
• 通讯作者: Landreman, Matt