arXiv subjects

Robert Wilcox

Publications and source records attributed to Robert Wilcox.

3 recordsLinked to original sources

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↗

arXiv2024

Highest Fusion Performance without Harmful Edge Energy Bursts in Tokamak

The path of tokamak fusion and ITER is maintaining high-performance plasma to produce sufficient fusion power. This effort is hindered by the transient energy burst arising from the instabilities at the boundary of high-confinement plasmas. The application of 3D magnetic perturbations is the method in ITER and possibly in future fusion power plants to suppress this instability and avoid energy busts damaging the device. Unfortunately, the conventional use of the 3D field in tokamaks typically leads to degraded fusion performance and an increased risk of other plasma instabilities, two severe issues for reactor implementation. In this work, we present an innovative 3D field optimization, exploiting machine learning, real-time adaptability, and multi-device capabilities to overcome these limitations. This integrated scheme is successfully deployed on DIII-D and KSTAR tokamaks, consistently achieving reactor-relevant core confinement and the highest fusion performance without triggering damaging instabilities or bursts while demonstrating ITER-relevant automated 3D optimization for the first time. This is enabled both by advances in the physics understanding of self-organized transport in the plasma edge and by advances in machine-learning technology, which is used to optimize the 3D field spectrum for automated management of a volatile and complex system. These findings establish real-time adaptive 3D field optimization as a crucial tool for ITER and future reactors to maximize fusion performance while simultaneously minimizing damage to machine components.

physics.plasm-ph↗

arXiv2020

Heterogeneous recovery from large scale power failures

Large-scale power failures are induced by nearly all natural disasters from hurricanes to wild fires. A fundamental problem is whether and how recovery guided by government policies is able to meet the challenge of a wide range of disruptions. Prior research on this problem is scant due to lack of sharing large-scale granular data at the operational energy grid, stigma of revealing limitations of services, and complex recovery coupled with policies and customers. As such, both quantification and firsthand information are lacking on capabilities and fundamental limitation of energy services in response to extreme events. Furthermore, government policies that guide recovery are often sidelined by prior study. This work studies the fundamental problem through the lens of recovery guided by two commonly adopted policies. We develop data analysis on unsupervised learning from non-stationary data. The data span failure events, from moderate to extreme, at the operational distribution grid during the past nine years in two service regions at the state of New York and Massachusetts. We show that under the prioritization policy favoring large failures, recovery exhibits a surprising scaling property which counteracts failure scaling on the infrastructure vulnerability. However, heterogeneous recovery widens with the severity of failure events: large failures that cannot be prioritized increase customer interruption time by 47 folds. And, prolonged small failures dominate the entire temporal evolution of recovery.

cs.CE↗