Abstract

Particle accelerators are invaluable tools for research in the basic and applied sciences, such as materials science, chemistry, the biosciences, particle physics, nuclear physics and medicine. The design, commissioning, and operation of accelerator facilities is a nontrivial task, due to the large number of control parameters and the complex interplay of several conflicting design goals. The Argonne Wakefield Accelerator facility has some unique challenges resulting from its purpose to carry out advanced accelerator R Individual experiments often have challenging beam requirements, and the physical configuration of the beam lines is often changed to accommodate the variety of supported experiments. The need for rapid deployment of different operational settings further complicates the optimization work that must be done for multiple constraints and challenging operational regimes. One example of this is an independently staged two-beam acceleration experiment which requires the construction of an additional beam line (this is now in progress). The high charge drive beam, well into the space charge regime, must be threaded through small aperture (17.6 mm) decelerating structures. In addition, the bunch length must be sufficiently short to maximize power generation in the decelerator. We propose to tackle this problem by means of multiobjective optimization algorithms which also facilitate a parallel deployment. In order to compute solutions in a meaningful time frame, a fast and scalable software framework is required. In this paper, we present a general-purpose framework for simulation-based multiobjective optimization methods that allows the automatic investigation of optimal sets of machine parameters. Using evolutionary algorithms as the optimizer and opal as the forward solver, validation experiments and results of multiobjective optimization problems in the domain of beam dynamics are presented. Optimized solutions for the new high charge drive beam line found by the framework were used to finish the design of a two beam acceleration experiment. The selected solution along with the associated beam parameters is presented.

Highlights

  • Particle accelerators play a significant role in many aspects of science and technology

  • All simulations for this experiment were carried out on Bebop, a high performance computing (HPC) cluster provided by the Laboratory Computing Resource Center (LCRC) at Argonne National Laboratory (ANL)

  • Its modular design simplifies the application to simulation-based optimization problems for a wide range of problems and allows to exchange the optimization algorithm

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Summary

INTRODUCTION

Particle accelerators play a significant role in many aspects of science and technology. Finding optics solutions in this regime, especially when there are additional constraints such as the small aperture two-beam accelerating structures, is challenging even without the quick turnaround of the beam line configurations It has been an important research objective to develop a precise, e.g., 3D model embedded into a multiobjective optimization framework that may be used as a flexible platform for optimization of changing machine configurations operated at different charge levels. The framework used here, incorporates the following three contributions: (1) Implementation of a scalable optimization algorithm capable of approximating Pareto fronts in high dimensional spaces, (2) design and implementation of a modular framework that is simple to use and deploy on large scale computational resources, and (3) demonstration of the usefulness of the proposed framework on a real world application in the domain of particle accelerators This is done with the optimization problem set as the high charge photoinjector at the AWA. The implementation of the framework and forward-solver is discussed in Supplemental Material at [16]

MULTIOBJECTIVE OPTIMIZATION
Evolutionary algorithms
THE FRAMEWORK
Related work
EXPERIMENTS
AWA photoinjector optimization
Time step scan
Hyper parameter scan
TBA optimization problem
AWA optimization results
Design Variable
CONCLUSIONS
Full Text
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