arXiv subjects

Jayson Barr

Publications and source records attributed to Jayson Barr.

4 recordsLinked to original sources

arXiv2026

Comparative qualification of advanced plasma-facing materials for fusion pilot plants through public- and private-sector experiments in DIII-D

A coordinated DIII-D campaign exposed and comparatively assessed 44 advanced plasma-facing materials from 12 institutions, including four public-private fusion partnerships, to support fusion pilot plant wall and divertor material down-selection. Samples were exposed using the Divertor Materials Evaluation System (DiMES) under Ohmic, L-mode, and H-mode conditions with edge-localized modes, at 0.2-2.5 MW m$^{-2}$ on flush geometries and 10-15 MW m$^{-2}$ on 10$^{\circ}$ angled geometries. Engineered tungsten architectures retained integrity; long-fiber Wf/W showed the clearest crack-arrest behavior. W-Re and K-doped W showed near-ITER-W-like responses, while additively manufactured W-Ta showed heat-flux-sensitive mass losses of 0.64 mg for the flat sample and 2.19-2.87 mg for angled samples. After irradiation to 0.3 dpa at 550$^{\circ}$C, neutron-irradiated ITER-grade W retained 2.8 times more deuterium than pristine W, while TiB$_2$ showed the lowest D$_2$ release in the Ohmic set. VTaHfMo was the most stable refractory multi-principal-element alloy. NbC and (Nb$_{0.5}$Ta$_{0.5}$)C retained integrity with 0.02-0.03 mg mass loss, whereas ZrC lost 7 mg. CVD SiC retained macroscopic integrity but exhibited an effective Si erosion yield of 0.5, about 5-10 times above prior DIII-D trends. Renewable boron pebble rods underwent controlled recession; 13% of released boron was ionized near the outer strike point and up to 50% was recovered locally. Initial in-situ chromium gross-erosion measurements yielded values of order $10^{-2}$. Together, these results provide cross-material benchmarks for fusion pilot plant down-selection and future AI/ML-assisted plasma-facing-material development.

physics.plasm-ph↗

arXiv2026

Spacecraft heat shield study in the DIII-D tokamak

We report a new experimental platform developed at the DIII-D National Fusion Facility to investigate carbon ablation and spallation under extreme heat fluxes relevant to fusion plasma-facing components and high-enthalpy atmospheric entry. Carbon samples were exposed to parallel heat fluxes of $30$--$40~\mathrm{MW\,m^{-2}}$ in the scrape-off layer using two complementary approaches: stationary carbon rods inserted near the divertor strike point and slow-launch carbon pellets injected vertically into the edge and core plasma. Pellets penetrating the core experienced heat fluxes approximately an order of magnitude higher. The conditions reproduce key aspects of the shock-layer environment encountered by the Galileo probe during entry into Jupiter's atmosphere. Fast visible imaging, divertor spectroscopy, infrared thermography, CO$_2$ interferometry, and post-exposure profilometry provided measurements of ablation rates, surface recession, and temperature evolution. Measured mass-loss rates of $(1$--$3)\times10^{-2}~\mathrm{g\,cm^{-2}\,s^{-1}}$ agree with semi-empirical aerospace ablation models, while wedge-shaped rods exhibited greater ablation than cylindrical and concave samples. UEDGE-DUSTT simulations incorporating parallel plasma flows, ${\bf j}\times{\bf B}$ forces, and ablation-cloud shielding reproduce the measured pellet trajectories and ablation timescales. These results establish tokamak plasma as a high-heat-flux environment for validating carbon ablation models and studying material response and impurity dynamics in reactor-relevant divertor plasmas.

physics.plasm-ph↗

arXiv2026

Resonant Pitch-Angle Scattering Of Runaway-Electrons by Externally-launched Helicon Waves in the DIII-D Tokamak

Resonant wave-particle interactions between externally launched helicon waves (also known as whistler waves) and runaway electrons (REs) have been demonstrated on the DIII-D tokamak. In this work we extend the initial results reported in Choudhury, H. et al. Phys. Rev. Lett. 136, 025101 (2026) by exploring the effects of antenna alignment with the edge magnetic field, toroidal wave propagation direction, and coupled power on RE scattering in the quiescent RE experimental scenario. Two distinct experimental configurations have been investigated: one in which the antenna aligns well with the edge background magnetic field, known as the ideal antenna configuration, and one with misalignment, known as the non-ideal case. Previously, it had been found that helicon power in the ideal antenna configuration prevented RE growth despite the normalized toroidal electric field remaining high enough to drive exponential RE growth in the absence of helicon power. In this paper, we show that scattering via the normal Doppler resonance (n=1) effectively limits the growth of the RE population in both the ideal and non-ideal antenna configurations, with evidence of a power threshold in the latter case. In contrast, launching waves that favour the anomalous Doppler resonance (n=-1) is observed to enhance rather than reduce the RE population. In addition, fast magnetic measurements reveal rising-tones in the 30-60 MHz range during helicon-off periods, which are not observed prior to helicon power. Finally, the challenges of using launched helicon waves to scatter post-disruption RE beams are discussed. Collisional damping and a large vacuum gap between the plasma and antenna on the outboard side present significant obstacles to helicon waves propagating into the plasma core.

physics.plasm-ph↗

arXiv2022

Implementation of AI/Deep Learning Disruption Predictor into a Plasma Control System

This paper reports on advances to the state-of-the-art deep-learning disruption prediction models based on the Fusion Recurrent Neural Network (FRNN) originally introduced a 2019 Nature publication. In particular, the predictor now features not only the disruption score, as an indicator of the probability of an imminent disruption, but also a sensitivity score in real-time to indicate the underlying reasons for the imminent disruption. This adds valuable physics-interpretability for the deep-learning model and can provide helpful guidance for control actuators now that it is fully implemented into a modern Plasma Control System (PCS). The advance is a significant step forward in moving from modern deep-learning disruption prediction to real-time control and brings novel AI-enabled capabilities relevant for application to the future burning plasma ITER system. Our analyses use large amounts of data from JET and DIII-D vetted in the earlier NATURE publication. In addition to when a shot is predicted to disrupt, this paper addresses reasons why by carrying out sensitivity studies. FRNN is accordingly extended to use many more channels of information, including measured DIII-D signals such as (i) the n1rms signal that is correlated with the n =1 modes with finite frequency, including neoclassical tearing mode and sawtooth dynamics, (ii) the bolometer data indicative of plasma impurity content, and (iii) q-min, the minimum value of the safety factor relevant to the key physics of kink modes. The additional channels and interpretability features expand the ability of the deep learning FRNN software to provide information about disruption subcategories as well as more precise and direct guidance for the actuators in a plasma control system.

physics.plasm-ph↗