Chemical equilibrium composition of air-copper-PA66 mixtures and their effects on dielectric breakdown for low-voltage circuit breaker post-current-zero

Abstract

Summary form only given. Metallic vapor emanating from the electrodes and plastic vapor from wall ablation on or before current-zero in a low-voltage circuit breaker (LVCB) significantly affect the dielectric recovery characteristics of atmospheric pressure air in the contact gap after current-zero. When the net ionization coefficient becomes positive, dielectric breakdown is said to occur and the reduced electric field (E/N) of this occurrence is termed the critical reduced electric field ((E/N)crit) [1]. In this paper, we analyze the dielectric breakdown behavior for the case of copper being the electrode material and polyamide 6/6 (PA-66) being the plastic wall material. Firstly, the chemical composition is calculated by the minimization of Gibbs free energy and the results are compared for two different methodologies (denoted as M1 [2] and M2 [3], for convenience). Unlike M2, M1 includes the condensed species of copper and carbon (graphite) and it will be shown that below 3500 K, the two methods provide widely different composition results. Secondly, Boltzmann's EEDF equation is solved to obtain the generalized non-Maxwellian electron-energy distribution function (EEDF) [4], with the electron-impact collision cross-sections gathered from literature as input. Using the afore-mentioned inputs, (E/N)crit is calculated and plotted against temperature ranging between 300-6000 K, for different mass-fraction values of air, copper and PA-66. Additionally, it has been observed that the presence of vibrationally- and electronically-excited species enhances the dielectric breakdown by lowering (E/N)crit. This approach is part of an initial attempt towards addressing realistic chemical non-equilibrium conditions involving finite-rate kinetics in an LVCB after current-zero and the numerical results will subsequently be utilized for comparisons with available experimental data.