Fixed field Alternating gradient (FFA) accelerator is an option as a proton driver for the next generation spallation neutron source (ISIS-II). To demonstrate FFA suitability for high intensity operation, a 3 to 12 MeV proton prototype ring is planned at RAL, called FETS-FFA. The main magnets are a critical part of the machine, and several characteristics of these magnets require development. First the doublet spiral structure has never been designed before, and the essential feature of operational flexibility in terms of machine optics requires a wide range of changes for the field gradient. Finally, control of the fringe field is a challenge both mechanically and from the nonlinear optics point of view. This paper will discuss the design of the prototype magnet for FETS-FFA ring.

Adiabatic capture of a coasting beam can be used to minimise the emittance of the resulting bunched beam – for example to capture the injected beam at the start of the acceleration cycle. In some cases, the voltage follows the so-called iso-adiabatic voltage law in order maintain the same adiabaticity throughout capture. Here we show that a linear evolution can result in a smaller final emittance than an iso-adiabatic scheme. This is shown by tracking a distribution through various capture schemes, taking as our example capture at injection in the FETS-FFA proton ring. We include preliminary results on the effects of longitudinal space charge which can be significant in this ring.

OPAL (Object Oriented Parallel Accelerator Library) is a C++ based massively parallel open-source program for tracking charged particles in large scale accelerator structures and beam lines, including 3D space charge, collisions, particle-matter-gas interaction, and 3D undulator radiation. The meticulous parallel architecture allows large and difficult problems, including one-to-one simulations with high resolution and no macro particles, to be tackled in a reasonable amount of time. The current code state as well as the most recent physics advancements and upgrades are discussed, including the unique feature of a sampler for creating massive, labeled data sets with tens of thousands of cores for machine learning. We also demonstrate scalability of our core algorithms up to 4600 GPUs and 32'000 CPUs, as part of our effort to make OPAL exascale ready.