Velocity estimation in the mixing layer of a subsonic jet using artificial neural networks

Andrew S. Tenney, Mark N. Glauser, Zachary P. Berger

Research output: Contribution to conferencePaper

Abstract

Time-resolved Particle Image Velocimetry (PIV, 10 kHz) was used to measure the flow-field of a Mach 0.6 axisymmetric jet. The field was decomposed into its requisite spatial POD eigenfunctions, and the time dependent coefficients were recovered. An Artificial Neural Network (ANN) and a Linear Stochastic Estimation (LSE) model were then trained to estimate the first five time-dependent POD coefficients from five point velocity measurements made by”virtual crosswires” in the mixing layer. We show that the prediction accuracy is strongly dependent on the POD mode number for both models. On average, the ANN-based model is able to predict the velocity fluctuations more accurately than the LSE-based model. Finally, we examine the estimated reduced-order velocity fields and their correlation to analytically reconstructed reduced-order velocity fields. Possible extensions of this method are also discussed.

Original languageEnglish (US)
StatePublished - Jan 1 2019
Event11th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2019 - Southampton, United Kingdom
Duration: Jul 30 2019Aug 2 2019

Conference

Conference11th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2019
CountryUnited Kingdom
CitySouthampton
Period7/30/198/2/19

ASJC Scopus subject areas

  • Atmospheric Science
  • Aerospace Engineering

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    Tenney, A. S., Glauser, M. N., & Berger, Z. P. (2019). Velocity estimation in the mixing layer of a subsonic jet using artificial neural networks. Paper presented at 11th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2019, Southampton, United Kingdom.