Order Reduction of the Radiative Heat Transfer Model for the Simulation of Plasma Arcs

TL;DR

This paper introduces a low-complexity, reduced-order model for simulating radiative heat transfer in plasma arcs, enabling faster and more efficient electric arc simulations in switchgear systems.

Contribution

It presents a novel systematic order reduction method that preserves key structural features and improves computational efficiency for plasma arc radiation modeling.

Findings

The reduced-order model accurately matches the full-order absorption spectrum.

The approach enhances computational efficiency in plasma arc simulations.

The model parameters are compatible with existing CFD-based arc simulation tools.

Abstract

An approach to derive low-complexity models describing thermal radiation for the sake of simulating the behavior of electric arcs in switchgear systems is presented. The idea is to approximate the (high dimensional) full-order equations, modeling the propagation of the radiated intensity in space, with a model of much lower dimension, whose parameters are identified by means of nonlinear system identification techniques. The low-order model preserves the main structural aspects of the full-order one, and its parameters can be straightforwardly used in arc simulation tools based on computational fluid dynamics. In particular, the model parameters can be used together with the common approaches to resolve radiation in magnetohydrodynamic simulations, including the discrete-ordinate method, the P-N methods and photohydrodynamics. The proposed order reduction approach is able to systematically compute the partitioning of the electromagnetic spectrum in frequency bands, and the related absorption coefficients, that yield the best matching with respect to the finely resolved absorption spectrum of the considered gaseous medium. It is shown how the problem's structure can be exploited to improve the computational efficiency when solving the resulting nonlinear optimization problem. In addition to the order reduction approach and the related computational aspects, an analysis by means of Laplace transform is presented, providing a justification to the use of very low orders in the reduction procedure as compared with the full-order model. Finally, comparisons between the full-order model and the reduced-order ones are presented.