Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2101.03047

An improved equation of state for air plasma simulations

Frederik Träuble·Stephen Millmore·Nikolaos Nikiforakis
View original source ↗Read source document ↗Export metadata ↓

Abstract

This work is concerned with the development of a novel, accurate equation of state for describing partially ionised air plasma in local thermodynamic equilibrium. One key application for this new equation of state is the simulation of lightning strike on aircraft. Due to the complexities of species ionisation and interaction, although phenomenological curve fitting of thermodynamic properties is possible, these curves are intractable for practical numerical simulation. The large difference in size of the parameters (many orders of magnitude) and complexity of the equations means they are not straightforward to invert for conversion between thermodynamic variables. The approach of this paper is to take an accurate 19-species phenomenological model, and use this to generate a tabulated data set. Coupled with a suitable interpolation procedure this offers an accurate and computationally efficient technique for simulating partially ionised air plasma. The equation of state is implemented within a multiphysics methodology which can solve for two-way coupling between a plasma arc and an elastoplastic material substrate. The implementation is validated against experimental results, both for a single material plasma, and an arc coupled to a substrate. It is demonstrated that accurate, oscillation-free thermodynamic profiles can be obtained, with good results even close to material surfaces.

Explore related subjects

physics.comp-phphysics.app-phphysics.flu-dynphysics.plasm-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Frederik Träuble, Stephen Millmore, Nikolaos Nikiforakis. 2021-01-11. An improved equation of state for air plasma simulations. https://doi.org/10.1063/5.0044008

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Bi-Hamiltonian in Semiflexible Polymers built upon Overdamping Process

Quantifying the interaction between a system of interest and its ambient conditions, the memory effect links the states of two distinct Hamiltonians: one for the target system and one for the environment. In this paper, we propose the diffusion process derived from the Smoluchowski equation that can derive the evolution process described by the memory effect integration in a non Markovian regime. The Smoluchowski picture, within the framework of stochastic thermodynamics, justifies a diffusion process incorporated into the equations of motion, and the result of the derivation enables a coarse-grained molecular dynamics simulation with the modified equation of motion to reproduce attenuation from collisions between single walled carbon nanotubes (SWCNTs) under far from equilibrium conditions. The results of the numerical experiments on the collision confirm that heat diffusion compensates for the correlated momentum arising from the memory effect between the two Hamiltonians in both equilibrium and far from equilibrium states.

physics.comp-ph↗
arXiv2026

Translation of transient acoustic fields

A method is presented for the translation of acoustic field data from a source to a target region. Field data are represented as spherical harmonic expansions on spheres surrounding the source and target regions respectively and expansions are translated using a ``point and shoot'' method using the Kirchhoff--Helmholtz integral to carry out an axial translation from one sphere to the other. The principal motivation for the method is its use in a time-domain Fast Multipole Method, and test cases reflective of this application are presented. The method converges to six digits for appropriate values of parameters and for the values of $N$ considered here computational effort scales approximately as $N^{2}$ where $N$ is the order of spherical harmonic expansion for the field data. The method is causal and thus avoids artifacts generated in methods which are not based on intrinsically causal formulations.

physics.comp-ph↗
arXiv2026

Learning continuous reaction paths for transition-state prediction

Transition states are defined by reaction pathways, yet most machine-learning methods predict them as isolated geometries. We introduce MARC-TS, a two-stage framework that learns a continuous, endpoint-conditioned path, queries it at any resolution and uses local path context to refine a transition-state candidate. We construct T1x-IRC-8K, a dataset of 8,209 reactions and 1,088,725 path-resolved geometries. On held-out reactions, the path model reduced complete-path error by 48.4% relative to endpoint interpolation, and the localizer achieved a mean aligned structural error of 0.127 Å. Quantum-chemical optimization and vibrational analysis yielded 405 frequency-confirmed first-order saddle-point candidates from 410 predictions. In a 100-reaction nudged elastic band comparison, learned-path initialization reached a joint geometry-and-force target for 66% of reactions, compared with 12% for geometric interpolation after 100 optimizer steps. By treating the path as a reusable representation rather than an auxiliary output, MARC-TS connects transition-state prediction, mechanistic interpretation and quantum-chemical refinement.

physics.comp-ph↗