Calculation of Electron Energy Distribution Functions From Electron Swarm Parameters Using Artificial Neural Network in SF6 and Argon


Tezcan S. S. , AKCAYOL M. A. , ÖZERDEM Ö. C. , Dincer M. S.

IEEE TRANSACTIONS ON PLASMA SCIENCE, vol.38, no.9, pp.2332-2339, 2010 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 38 Issue: 9
  • Publication Date: 2010
  • Doi Number: 10.1109/tps.2010.2049588
  • Journal Name: IEEE TRANSACTIONS ON PLASMA SCIENCE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.2332-2339
  • Keywords: Argon, boltzmann equation, neural networks, SF6, EFFECTIVE IONIZATION COEFFICIENTS, COLLISION CROSS-SECTIONS, MAGNETIC-FIELDS, GAS-MIXTURES, TRANSPORT, DISCHARGES, SIMULATION, PLASMAS
  • Gazi University Affiliated: Yes

Abstract

This paper proposes an artificial neural network (ANN) to obtain the electron energy distribution functions (EEDFs) in SF6 and argon from the following: 1) mean energies; 2) the drift velocities; and 3) other related swarm data. In order to obtain the required swarm data, the electron swarm behavior in SF6 and argon is analyzed over the range of the density-reduced electric field strength E/N from 50 to 800 Td from a Boltzmann equation analysis based on the finite difference method under a steady-state Townsend condition. A comparison between the EEDFs calculated by the Boltzmann equation and by ANN for various values of E/N suggests that the proposed ANN yields good agreement of EEDFs with those of the Boltzmann equation solution results.