School of Physics - Theses

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Towards Automating the Design and Optimisation of Particle Accelerators

Zhang, Xuanhao ( 2023-06)

The question of efficiency and optimality of accelerator lattice structures was investigated in this thesis. Within the context of circular accelerators for hadron therapy, an analysis on the design methodology of existing compact circular acceler-ators was carried out. This analysis prompted the design of a novel lattice based on two double bend achromat arcs as an alternative to conventional periodic cell struc-tures. The feasibility to perform slow extraction for hadron therapy purposes was demonstrated using the proposed lattice. The extraction efficiency was optimised by tuning the lattice optics. In the second half of this thesis, an automated design and optimisation algorithm was proposed. This algorithm was developed as a general purpose lattice design tool. The development process examined three optimisation routines including the Simulated Annealing algorithm, a simple genetic algorithm, and the Non-dominated Sorting Genetic Algorithm (NSGA). Three encoding methods were developed to represent the accelerator lattice for use with the optimisation routines. Namely, the finite slicing encoder, the neural network encoder, and the matrix encoder. It was found that the combination of NSGA-III algorithm and the matrix encoder was the most efficient method for exploring the feasible parameter space for a generalisable lattice design problem.