An important area of research required for fusion reactor design is the study of materials under high energy neutron irradiation. Deuterium-Tritium (D-T) reactions release 14.1 MeV neutrons and material studies of such high energy neutrons focusing on transmutation and activation are paramount for fusion tokamak devices such as ITER and DEMO. In order to understand neutron damage and transmutation-induced radioactivity in fusion regime energies, a series of experimental campaigns were performed at the ASP facility based at Aldermaston in the UK, which uses a deuteron accelerator to bombard a tritiumloaded target and generate 14 MeV-neutron emission rates of up to 2.5 × 1011 s−1. In this work, a holistic treatment of the 11,000 gamma spectra (time series data) collected over five experimental campaigns is applied to identify radioisotopes and validate nuclear data and the inventory code, FISPACT-II. Whilst previous analysis has examined single spectra and foil irradiation’s using traditional, human-driven methods, this work applies novel methods using Artificial Neural Networks (ANN) and classification algorithms to allow a fully automated approach. Using such methods we show good broad agreement with FISPACT-II inventory simulations, and an overview of results are given as C/E values.

We propose a method for data-driven modelling of the temporal evolution of the plasma and neutral characteristics at the edge of a tokamak using neural networks. Our method proposes a novel fully convolutional network to serve as function approximators in modelling complex nonlinear phenomenon observed in the multi-physics representations of high energy physics. More specifically, we target the evolution of the temperatures, densities and parallel velocities of the electrons, ions and neutral particles at the edge. The central challenge in this context is in modelling together the different physics principles encapsulated in the evolution of plasma and the neutrals. We demonstrate that the inherent differences in nonlinear behaviour can be addressed by forking the network to process the plasma and neutral information individually before integrating as a holistic system. Our approach takes into account the spatial dependencies of the physics parameters across the grid while performing the temporal mappings, ensuring that the underlying physics is factored in and not lost to the blackbox. Having used the conventional edge plasma-neutral solver code SOLPS to build the synthetic dataset, our method demonstrates a computational gain of over 5 orders of magnitude over it without a considerable compromise on accuracy.

The tritium breeding ratio (TBR) is an essential quantity for the design of modern and next-generation D-T fueled nuclear fusion reactors. Representing the ratio between tritium fuel generated in breeding blankets and fuel consumed during reactor runtime, the TBR depends on reactor geometry and material properties in a complex manner. In this work, we explored the training of surrogate models to produce a cheap but high-quality approximation for a Monte Carlo TBR model in use at the UK Atomic Energy Authority. We investigated possibilities for dimensional reduction of its feature space, reviewed 9 families of surrogate models for potential applicability, and performed hyperparameter optimisation. Here we present the performance and scaling properties of these models, the fastest of which, an artificial neural network, demonstrated R2 = 0.985 and a mean prediction time of 0.898 μs, representing a relative speedup of 8 · 106 with respect to the expensive MC model. We further present a novel adaptive sampling algorithm, Quality-Adaptive Surrogate Sampling, capable of interfacing with any of the individually studied surrogates. Our preliminary testing on a toy TBR theory has demonstrated the efficacy of this algorithm for accelerating the surrogate modelling process.

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