Experiments in Fluids

, Volume 26, Issue 6, pp 549–552

Transitional intermittency detection by neural network

Authors

  • M. Chattopadhyay
    • Department of Aerospace Engineering Indian Institute of Science, Bangalore 560012, India
  • J. Dey
    • Department of Aerospace Engineering Indian Institute of Science, Bangalore 560012, India
  • V. Mani
    • Department of Aerospace Engineering Indian Institute of Science, Bangalore 560012, India

DOI: 10.1007/s003480050322

Cite this article as:
Chattopadhyay, M., Dey, J. & Mani, V. Experiments in Fluids (1999) 26: 549. doi:10.1007/s003480050322

Abstract

 A neural network has been used to predict the flow intermittency from velocity signals in the transition zone in a boundary layer. Unlike many of the available intermittency detection methods requiring a proper threshold choice in order to distinguish between the turbulent and non-turbulent parts of a signal, a trained neural network does not involve any threshold decision. The intermittency prediction based on the neural network has been found to be very satisfactory.

Copyright information

© Springer-Verlag Berlin Heidelberg 1999