Experiments in Fluids

, Volume 14, Issue 6, pp 416–422 | Cite as

Computational and experimental study of a railplug igniter

  • J. L. Ellzey
  • M. J. Hall
  • X. Zhao
  • H. Tajima
Originals

Abstract

The plasma plume generated by a new type of high energy igniter known as the railplug is examined. The railplug is a miniaturized railgun that has the potential for improving ignition characteristics of combustible mixtures in engines. The objective of the study is to gain an understanding of the characteristics of the plasma created by a transparent railplug and to validate a multidimensional computer simulation of the plasma and shock fronts. The nature of the plume emitted by the railplug was examined for three levels of electrical energy while firing into air at a pressure of 1 atm. The computer model is to be used to predict trends in railplug performance for various railplug designs, energies, and ambient conditions. The velocity of the plasma movement inside a transparent railplug was measured, as well as the velocity of the plume ejected from the cavity. A shock is produced at the initiation point of the arc and propagates down the cavity, eventually exiting the plug. The velocity of the shock was both measured experimentally and simulated by the model. The computer simulation produces a mushroom-shaped plasma plume at the railplug exit similar to that observed in the shadowgraph photos. The simulation also reproduced the toroidal circulation observed at the plug exit in the shadowgraphs, the radial expansion, and the penetration depth of the plume. The trend of linearly increasing plasma kinetic energy with stored electrical energy predicted by the simulation was verified by shadowgraph photos. The agreement between the experiments and the simulations suggests that the multidimensional model holds promise as a predictive design tool.

Keywords

Computer Simulation Penetration Depth Electrical Energy Shock Front Design Tool 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag 1993

Authors and Affiliations

  • J. L. Ellzey
    • 1
  • M. J. Hall
    • 1
  • X. Zhao
    • 1
  • H. Tajima
    • 2
  1. 1.Department of Mechanical EngineeringThe University of Texas at AustinAustinUSA
  2. 2.Department of Mechanical EngineeringMiyazaki UniversityJapan

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