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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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Fast ion diagnostics and physics at MAST-Upgrade (Final Report)

The main contribution of UCI personnel to MAST-U was installation of the solid-state neutral particle analyzer (SSNPA) diagnostic, which was documented in a 2022 Review of Scientific Instruments article by UCI PhD student Garrett Prechel. Another paper by PI William Heidbrink et al supplied a helpful tool for analysis of the data from the 3 MeV proton diagnostic [Plasma Phys. Cont. Fusion, 2021]. Useful advice was also given concerning the FIDA diagnostic.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Post-puff SOL broadening on MAST-U under high-recycling conditions: evidence consistent with cross-field transport changes

Transient broadening of the scrape-off layer (SOL) density profile can modify main-chamber first-wall particle fluxes and divertor loading, yet its control parameters remain debated between divertor-regime transitions, neutral dynamics and changes in cross-field transport. We investigate fueling-driven SOL density-profile evolution and post-fueling relaxation on MAST-Upgrade (MAST-U) in ohmic L-mode, using two otherwise similar double-null Conventional Divertor discharges (I p = 450 kA, B T = 0.33 T) with identical 50 ms low-field side gas puffs; the only intentional difference is the puff start time. Upstream Thomson scattering shows that both discharges develop a transient far-SOL density profile modification, expressed as an increased SOL-width metric λ n e and a far-SOL enhancement consistent with a shoulder-like signature. In the earlier-puff case, the SOL-width metric remains elevated after puff termination and the post-puff decay time scales are systematically longer across the analysed radii. Outer-target Langmuir probes indicate high-recycling conditions during the analysed window (few-eV T e with radially peaked j sat,∥ and q ∥ profiles, without signatures of deep detachment). The outer-divertor collisionality proxy Λ div is elevated in both cases and does not discriminate between the different post-puff persistence. The target-integrated ion flux proxy ∫ J sat dA evolves nearly identically in both discharges when aligned to the puff start. Taken together, these observations suggest that the late/post-puff upstream SOL evolution is not set by parallel exhaust to the outer targetalone and is more consistent with upstream cross-field redistribution, with a possible role for plasma–neutral coupling and fueling geometry.

MAST-U↗

Tokamak divertor plasma emulation with machine learning

Abstract Future tokamak devices that aim to create conditions relevant to power plant operations must consider strategies for mitigating damage to plasma facing components in the divertor. One of the goals of MAST-U tokamak operations is to inform these considerations by researching advanced divertor configurations that aid stable plasma detachment. Machine design, scenario planning and detachment control would all greatly benefit from tools that enable rapid calculation of scenario-relevant quantities given some input parameters. This paper presents a method for generating large, simulated scrape-off layer data sets, which was applied to generate a data set of steady-state Hermes-3 simulations of the MAST-U tokamak. A machine learning model was constructed using a Bayesian approach to hyperparameter optimisation to predict diagnosable output quantities given control-relevant input features. The resulting best-performing model, which is based on a feedforward neural network, achieves high accuracy when predicting electron temperature at the divertor target and carbon impurity radiation front position and runs in around 1 ms in inference mode. Techniques for interpreting the predictions made by the model were applied, and a high-resolution parameter scan of upstream conditions was performed to demonstrate the utility of rapidly generating accurate predictions using the emulator. This work represents a step forward in the design of machine learning-driven emulators of tokamak exhaust simulation codes in operational modes relevant to divertor detachment control and plasma scenario design.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗