Investigation of fast GPU-based algorithms for jet-surface interaction noise calculations

Afsar, Mohammed; Kokkinakis, Ioannis; Stirrat, Sarah

doi:10.2514/6.2020-2564

Cited by 1 publication

(2 citation statements)

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“…From an analytical and numerical standpoint, a lot of work has been done on this problem [10][11][12][13][14][15]. The discontinuity in the solid surface boundary condition can be treated formally using the Wiener-Hopf technique for a flat plate that is doubly infinite in the spanwise direction and lies parallel to the level curves of the streamwise mean flow.…”

Section: Introductionmentioning

confidence: 99%

“…[18]) on jet flow turbulence. All models are formulated with arbitrary 'tuning' parameters to quantify the degree of spatial and temporal de-correlation as well as the permanence of a finite anti-correlation region in spatial and temporal separation [14]. Previous modeling approaches tuned these scales by hand to obtain good agreement with the acoustic data.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Optimization Methods for Aeroacoustic Prediction of Trailing-Edge Interaction Noise in Axisymmetric Jets

Stirrat¹,

Afsar²,

Minisci³

2021

Mathematics

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Our concern in this paper is in the fine-tuning of the arbitrary parameters within the upstream turbulence structure for the acoustic spectrum of a rapid-distortion theory (RDT)-based model of trailing-edge noise. RDT models are based on an appropriate asymptotic limit of the Linearized Euler Equations and apply when the interaction time of the turbulence with the surface edge discontinuity is small compared to the eddy turnover time. When an arbitrary transversely sheared jet mean flow convects a finite region of nonhomogeneous turbulence, the acoustic spectrum of the pressure field scattered by the trailing-edge depends on (among other things) the upstream turbulence via the Fourier transform of the correlation function, R22 (where subscript 2 refers to a co-ordinate surface normal to the plate). We show that the length and time scale parameters that govern the spatial and temporal de-correlation of R22 can be found using formal optimization methods to avoid any uncertainty in their selection by hand-tuning. We assess various optimization methods that are broadly categorized into an ‘evolutionary’ and ‘non-evolutionary’ paradigm. That is, we optimize the acoustic spectrum using the Multi-Start algorithm, Particle Swarm Optimization and the Multi-Population Adaptive Inflationary Differential Evolution Algorithm. The optimization is based upon different objective functions for the acoustic spectrum and/or turbulence structure. We show that this approach, while resulting in the total modest increase in computation time (on average 2 h), gives excellent prediction over most frequencies (within 2–4 dB) where the trailing-edge noise associated amplification in sound exists.

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