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, cilt.38, sa.9, ss.2332-2339, 2010 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 38 Sayı: 9
  • Basım Tarihi: 2010
  • Doi Numarası: 10.1109/tps.2010.2049588
  • Dergi Adı: IEEE TRANSACTIONS ON PLASMA SCIENCE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2332-2339
  • Anahtar Kelimeler: Argon, boltzmann equation, neural networks, SF6, EFFECTIVE IONIZATION COEFFICIENTS, COLLISION CROSS-SECTIONS, MAGNETIC-FIELDS, GAS-MIXTURES, TRANSPORT, DISCHARGES, SIMULATION, PLASMAS
  • Gazi Üniversitesi Adresli: Evet

Özet

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.