arXiv subjects

Christopher McDevitt

Publications and source records attributed to Christopher McDevitt.

5 recordsLinked to original sources

arXiv2026

Hierarchical Framework of Runaway Electrons using Deep Learning

We present an adjoint deep learning framework describing the evolution of fluid moments and the energy distribution of the runaway electron (RE) population. We demonstrate that a careful formulation of the adjoint problem allows for the temporal evolution of these quantities for arbitrary initial electron distributions, and in combination with a physics-informed neural network (PINN), we show that the resulting surrogates can resolve a broad range of plasma parameters. This combination of the adjoint formulation and rapid inference of neural networks enables orders of magnitude faster predictions of RE kinetics than traditional methods. Here, we detail the mathematical formulation and the design of three PINNs which recover the temporal evolution of the RE current, average energy and energy distribution. Predictions are validated against a traditional RE solver, with good agreement across a broad range of scenarios.

physics.plasm-ph↗

arXiv2026

A Deep Learning Approach to Describing the Plasma Sheath

Despite their ubiquity, the rich physics present in a plasma sheath has inhibited the development of a generally applicable description of this critical region. The present study utilizes a physics-informed neural network (PINN) to evaluate a hierarchy of models of the plasma sheath. Unlike traditional deep learning methods, PINNs use the governing PDEs to constrain the predictions of a neural network, and thus do not require any experimental or simulation data to train. In this work, we utilize a PINN to identify the parametric solution to fluid models of different physics fidelity of the plasma sheath. While the offline training time of the PINN is often longer than a traditional solver, once trained, the PINN is able to efficiently predict the sheath profiles across a broad range of parameter regimes, thus yielding an effective surrogate of the plasma sheath.

physics.plasm-ph↗

arXiv2024

A reduced kinetic method for investigating non-local ion heat transport in ideal multi-species plasmas

A reduced kinetic method (RKM) with a first-principle collision operator is introduced in a 1D2V planar geometry and implemented in a computationally inexpensive code to investigate non-local ion heat transport in multi-species plasmas. The RKM successfully reproduces local results for multi-species ion systems and the important features expected to arise due to non-local effects on the heat flux are captured. In addition to this, novel features associated with multi-species, as opposed to single species, case are found. Effects of non-locality on the heat flux are investigated in mass and charge symmetric and asymmetric ion mixtures with temperature, pressure, and concentration gradients. In particular, the enthalpy flux associated with diffusion is found to be insensitive to sharp pressure and concentration gradients, increasing its significance in comparison to the conductive heat flux driven by temperature gradients in non-local scenarios. The RKM code can be used for investigating other kinetic and non-local effects in a broader plasma physics context. Due to its relatively low computational cost it can also serve as a practical non-local ion heat flux closure in hydrodynamic simulations or as a training tool for machine learning surrogates.

physics.plasm-ph↗

arXiv2024

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 $\nu_{*, crit}$ 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, $\nu_{*, crit}$ 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, $\nu_{*, crit} \gtrsim 1$, no reduction of the avalanche is observed due to toroidal geometry. This sharp contrast in the impact of low-versus 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.

physics.plasm-ph↗

arXiv2023

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 (RE) 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. The present work develops a physics-informed neural network (PINN) tailored to the solution of the hot tail seed during an idealized 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 time scale, initial plasma temperature, and local current density, in the absence of experimental or simulation data. The hot tail PINN is verified by comparison with a direct Monte Carlo solution, with excellent agreement found across a broad range of thermal quench conditions.

physics.plasm-ph↗