Prediction of supersonic jet noise using non-parallel flow asymptotics and LES data within Goldstein’s acoustic analogy

Mohammad Koshuriyan, Adrian Sescu, Vasilis Sassanis, Aaron Towne, Guillaume Brès, Sanjiva Lele

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this study we show how accurate jet noise predictions can be achieved within Gold-stein’s generalized acoustic analogy formulation for heated and un-heated supersonic jets using a previously developed asymptotic theory for the adjoint vector Green’s function. In this approach, mean flow non-parallelism enters the leading order dominant balance producing enhanced amplification at low frequencies, which we believe corresponds to the peak sound at small polar observation angles. We determine all relevant mean flow and turbulence quantities using Large Eddy Simulations of two axi-symmetric round jets at fixed jet Mach number and different nozzle temperature ratios. Certain empirical co-efficients that enter the turbulence length scales formula are tuned for good agreement with the far-field noise data. Our results indicate that excellent jet noise predictions are obtained using the asymptotic approach, remarkably, up to a Strouhal number of 0.5.
Original languageEnglish
Title of host publicationStudying Turbulence using Numerical Simulation Databases - XVI
Place of PublicationStanford, California
PublisherStanford University, Center for Turbulence Research
Publication statusPublished - 31 Dec 2016

Keywords

  • jet noise predictions
  • Gold-stein’s generalized acoustic analogy formulation
  • adjoint vector Green’s function
  • asymptotic approach
  • turbulence
  • mean flow

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