UC Irvine

This thesis presents advances in computational modelling, analysis and techniques that can be used to study mission critical topics in fusion. Energetic particles is an important field of research since information about the plasma state can be encoded in the fast-ion distribution function. Energetic ions can resonate with plasma waves in a fusion device, degrade plasma performance or confinement, and damage the inner walls of the vessel. There is a need in the community to better understand the fast-ion distribution function, improve plasma performance, and mitigate unwanted impacts from wave-particle interactions.

In chapter 2, the weight function for 3-MeV protons produced in d(d,p)t fusion reactions between a fast ion and a thermal deuteron is developed. The weight function W(X) is a diagnostic sensitivity to phase-space variables X that relates the measured signal C to the distribution function F(X) through the equation C = integral[ W(X) F(X) dX ]. The algorithm developed here accounts for the complications associated with the curved "sightline" trajectories of the escaping protons. Time-reversed orbits are initially calculated to get the effective solid angles and sightlines for the range of incident proton velocity vectors. Synthetic diagnostic code FIDASIM [Plasma Phys. Cont. Fusion 62 (2020) 105008] is upgraded to accept these inputs, then calculate the reactivity averaged over the thermal distribution of the "target" deuterons and the probability that a fast ion of specified energy and pitch has a gyroangle that is consistent with the kinematic equations along each of the sightlines. The outputs of FIDADSIM are verified using independent calculations on the Mega Amp Spherical Tokamak.

In chapter 3, a conceptual design to diagnose the lifetime of spin polarized fusion experiments using existing port geometries is developed at DIII-D. The cross sections for the D-T and D-3He fusion reactions are increased by as much as 50% if the fuel remains spin polarized parallel to the magnetic field in magnetically confined fusion experiments. The goal in this chapter is to assess the feasibility of lifetime measurements of spin polarization, in magnetic fusion relevant conditions, on the DIII-D tokamak using relative changes in charged fusion product (CFP) loss measurements that depend upon the differential fusion cross section. Relative measurements that capture changes in the escaping CFP pitch, poloidal, and energy distributions are studied in two realistic TRANSP calculated plasma scenarios (high Ti and beam-plasma).Ideal CFP detection, a realistic assessment of CFP signals and reduced chi-squared calculations show polarization lifetime measurements are feasible for the thermonuclear (high Ti) scenario.

In chapter 4, Machine Learning (ML) models are developed to automatically detect Alfvén eigenmodes (AE) and these models achieve high performance (True Positive Rate = 90% and False Positive Rate = 14%).ML-based models can be useful for real-time detection and control of AEs in steady-state plasma scenarios. These ML systems can be implemented into control algorithms that drive actuators for mitigation of unwanted AE impacts. Using labels created from a curated database [Heidbrink, et al., NF ‘20], Machine Learning-based systems are trained using single chord and crosspower spectrograms to predict the presence of 5 AEs (EAE, TAE, RSAE, BAE and LFM). The advantages of using the CO2 interferometer to detect AEs, and the results from a comparison between inputs (single chord and crosspower spectrograms) and another comparison between two different ML models (Reservoir Computing Network and Long Short-Term Memory Network) are covered here.