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Haack, Jeffrey R.

Publications and source records attributed to Haack, Jeffrey R..

Experimental and computational study of phase space dynamics in strongly coupled plasmas with steep density gradients

Understanding how plasmas thermalize when density gradients are steep remains a fundamental challenge in plasma physics, with direct implications for fusion experiments and astrophysical phenomena. Standard hydrodynamic models break down in these regimes, and kinetic theories make predictions that have never been directly tested. Here, we present the first detailed phase-space measurements of a strongly coupled plasma as it evolves from sharp density gradients to thermal equilibrium. Using laser-induced fluorescence imaging of an ultracold calcium plasma, we track the complete ion distribution function f(x,v,t). We discover that commonly used kinetic models (Bhatnagar–Gross–Krook and Lenard–Bernstein) overpredict thermalization rates, even while correctly capturing the initial counterstreaming plasma formation. Our measurements reveal that the initial ion acceleration response scales linearly with electron temperature, and that the simulations underpredict the initial ion response. In our geometry we demonstrate the formation of well-controlled counterpropagating plasma beams. This experimental platform enables precision tests of kinetic theories and opens new possibilities for studying plasma stopping power and flow-induced instabilities in strongly coupled systems.

Bergeson, Scott (ORCID:0000000231249226)↗

On a Spectral Method for β -particle Bound Excitation Collisions in Kilonovae

The interaction of β-particles with the weakly ionized plasma background is an important mechanism for powering the kilonova (KN) transient signal from neutron star mergers. For this purpose, we present an implementation of the approximate fast-particle collision kernel, described by Inokuti following the seminal formulation of Bethe, in a spectral solver of the Vlasov–Maxwell–Boltzmann equation. In particular, we expand the fast-particle plane-wave atomic excitation kernel into coefficients of the Hermite basis, and derive the relevant discrete spectral system. In this fast-particle limit, the approach permits the direct use of atomic data, including optical oscillator strengths, normally applied to photon–matter interaction. The resulting spectral matrix is implemented in the MASS-APP spectral solver framework, in a way that avoids full matrix storage per spatial zone. We numerically verify aspects of the matrix construction, and present a proof-of-principle 3D simulation of a 2D axisymmetric KN ejecta snapshot. Our preliminary numerical results indicate that a reasonable choice of Hermite basis parameters for β-particles in the KN is a bulk velocity parameter u = 0, a thermal velocity parameter α = 0.5c, and a 9 × 9 × 9 mode velocity basis set (Hermite orders of 0–8 in each dimension). For interior-ejecta sample zones, we estimate that the ratio of thermalization from large-angle (≳2fdg5) bound excitation scattering to total thermalization is ~0.002–0.003.

79 ASTRONOMY AND ASTROPHYSICS↗

Predictive scale-bridging simulations through active learning

Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-bridging over brute-force increases in the number of mesh elements. For instance, quantitative predictions of transport in nanoporous media, critical to hydrocarbon extraction from tight shale formations, are impossible without accounting for molecular-level interactions. Similarly, inertial confinement fusion simulations rely on numerical diffusion to simulate molecular effects such as non-local transport and mixing without truly accounting for molecular interactions. With these two disparate applications in mind, we develop a novel capability which uses an active learning approach to optimize the use of local fine-scale simulations for informing coarse-scale hydrodynamics. Our approach addresses three challenges: forecasting continuum coarse-scale trajectory to speculatively execute new fine-scale molecular dynamics calculations, dynamically updating coarse-scale from fine-scale calculations, and quantifying uncertainty in neural network models.

97 MATHEMATICS AND COMPUTING↗