Engineering topics

McDevitt, Christopher J.

Publications and source records attributed to McDevitt, Christopher J..

4 records

DOE OSTI2024

The impact of collisionality on the runaway electron avalanche during a tokamak disruption

The exponential growth (avalanching) of runaway electrons (REs) during a tokamak disruption continues to be a large uncertainty in RE modeling. The present work investigates the impact of tokamak geometry on the efficiency of the avalanche mechanism across a broad range of disruption scenarios. It is found that the parameter ν *,crit ⁠, describing the collisionality at the critical energy to run away, delineates how toroidal geometry impacts RE formation. In particular, utilizing a reduced but self-consistent description of plasma power balance, it is shown that for a high-density deuterium-dominated plasma, ν *,crit is robustly less than one, resulting in a substantial decrease in the efficiency of the RE avalanche compared to predictions from slab geometry. In contrast, for plasmas containing a substantial quantity of neon or argon, ν *,crit ≳1, no reduction of the avalanche is observed due to toroidal geometry. This sharp contrast in the impact of low- vs high-Z material results primarily from the relatively strong radiative cooling from high-Z impurities, enabling the plasma to be radiatively pinned at low temperatures and thus large electric fields, even for modest quantities of high-Z material.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

A Physics-Informed Deep Learning Description of Knudsen Layer Reactivity Reduction

A physics-informed neural network (PINN) is used to evaluate the fast ion distribution in the hot spot of an inertial confinement fusion target. The use of tailored input and output layers to the neural network is shown to enable a PINN to learn the parametric solution to the Vlasov–Fokker–Planck equation in the absence of any synthetic or experimental data. As an explicit demonstration of the approach, the specific problem of Knudsen layer fusion yield reduction is treated. Here, the predictions from the Vlasov–Fokker–Planck PINN are used to provide a non-perturbative solution of the fast ion tail in the vicinity of the hot spot, thus allowing the spatial profile of the fusion reactivity to be evaluated for a range of collisionalities and hot spot conditions. Excellent agreement is found between the predictions of the Vlasov–Fokker–Planck PINN and the results from traditional numerical solvers with respect to both the energy and spatial distribution of fast ions and the fusion reactivity profile, demonstrating that the Vlasov–Fokker–Planck PINN provides an accurate and efficient means of determining the impact of Knudsen layer yield reduction across a broad range of plasma conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Runaway electron current reconstitution after a nonaxisymmetric magnetohydrodynamic flush

Benign termination of mega-ampere (MA) level runaway current has been convincingly demonstrated in recent JET and DIII-D experiments, establishing it as a leading candidate for runaway mitigation on ITER. This comes in the form of a runaway flush by parallel streaming loss along stochastic magnetic field lines formed by global magnetohydrodynamic instabilities, which are found to correlate with a low-Z injection that purges the high-Z impurities from a post-thermal-quench plasma. Here, in this study, we show the competing physics that govern the postflush reconstitution of the runaway current in an ITER-like reactor where significantly higher current is expected. The trapped “runaways” are found to dominate the seeding for runaway reconstitution, and the incomplete purge of high-Z impurities helps drain the seed but produces a more efficient avalanche, two of which compete to produce a 2–3 MA step in current drop before runaway reconstitution of the plasma current.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

A physics-informed deep learning model of the hot tail runaway electron seed

A challenging aspect of the description of a tokamak disruption is evaluating the hot tail runaway electron seed that emerges during the thermal quench. This problem is made challenging due to the requirement of describing a strongly non-thermal electron distribution, together with the need to incorporate a diverse range of multiphysics processes, including magnetohydrodynamic instabilities, impurity transport, and radiative losses. Here this work develops a physics-informed neural network (PINN) tailored to the solution of the hot tail seed during an axisymmetric thermal quench. Here, a PINN is developed to identify solutions to the adjoint relativistic Fokker–Planck equation in the presence of a rapid quench of the plasma's thermal energy. It is shown that the PINN is able to accurately predict the hot tail seed across a range of parameters, including the thermal quench timescale, initial plasma temperature, and local current density, in the absence of experimental or simulation data. The hot tail PINN is verified by comparison with direct Monte Carlo simulations, with excellent agreement found across a broad range of thermal quench conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗