Numerical Study of Nonequilibrium Seed-Free Argon Plasma Magnetohydrodynamic Generator Using Collisional-Radiative Model

Autor: Yoshihiro Okuno, Takayasu Fujino, Soshi Ito
Rok vydání: 2021
Předmět:
Zdroj: IEEE Transactions on Plasma Science. 49:2954-2966
ISSN: 1939-9375
0093-3813
DOI: 10.1109/tps.2021.3059440
Popis: To examine the influence of radiative transitions on the performance of nonequilibrium seed-free argon plasma magnetohydrodynamic (MHD) generators, we developed MHD numerical simulation technique with a collisional-radiative (C-R) model, where neutral argon atoms with 45 discrete effective electronic excitation levels, singly ionized argon ions, and free electrons were considered under a two-temperature, low-magnetic Reynolds number MHD approximation. Furthermore, a so-called escape factor was implemented in the C-R model to consider the effects of radiation trapping by ground-state neutral atoms. Using the developed technique, we computed the performance characteristics of a nonequilibrium seed-free argon plasma MHD generator. The numerical results showed that the generator performance gets higher as the escape factor decreases, i.e., the optical thickness for radiative transitions to the ground state of neutral atoms increases. The numerical results also suggested that when the escape factor is $1.0\times 10^{-3}$ or less, the generator performance is almost the same as that for the escape factor of 0 corresponding to completely optical-thick plasma case for the radiative transitions to the ground state of neutral atoms. Furthermore, realistic escape factor values for resonance lines in the power generation channel were evaluated using a well-known estimation model. Consequently, the estimated values had the order of $10^{-4}$ . We also showed that under such relatively optically thick plasma cases, a collisional (C) model and a widely used global ionization-recombination rate model with no explicit consideration of electronically excited-state neutral atoms can reproduce the generator performance predicted by the C-R model with relatively good accuracy.
Databáze: OpenAIRE