Instability waves and low-frequency noise radiation in the subsonic chevron jet

Abstract

Spatial instability waves associated with lowfrequency noise radiation at shallow polar angles in the chevron jet are investigated and are compared to the round counterpart. The Reynolds-averaged Navier-Stokes equations are solved to obtain the mean flow fields, which serve as the baseflow for linear stability analysis. The chevron jet has more complicated instability waves than the round jet, where three types of instability modes are identified in the vicinity of the nozzle, corresponding to radial shear, azimuthal shear, and their integrated effect of the baseflow, respectively. The most unstable frequency of all chevron modes and round modes in both jets decrease as the axial location moves downstream. Besides, the azimuthal shear effect related modes are more unstable than radial shear effect related modes at low frequencies. Compared to a round jet, a chevron jet reduces the growth rate of the most unstable modes at downstream locations. Moreover, linearized Euler equations are employed to obtain the beam pattern of pressure generated by spatially evolving instability waves at a dominant low frequency St = 0.3, and the acoustic efficiencies of these linear wavepackets are evaluated for both jets. It is found that the acoustic efficiency of linear wavepacket is able to be reduced greatly in the chevron jet, compared to the round jet.

A theoretical study of serrated leading edges in aerofoil and vortical gust interaction noise

“…Overall, the current model extensively simplifies our previous analytical modes for serrations [10,21] and should be able to provide a clear physical insight of the inherent noise control mechanisms. In particular, the innovative concept that models the effect of serrations as transfer functions is uniquely different from other analysis in fluid mechanics and shall be able to assist the future analysis, design and optimisation of new low-noise aviation systems [2,31,32].…”

Deep neural networks for waves assisted by the Wiener–Hopf method

“…In the training of the U-net, we deliberately choose a dataset of 10 000 items that only covers 4 modes, that is, (m, n) = (6, 1), (6,2), (7,1) and (7,2), with uniform background flows of M 0 = M 1 = 0.1. The corresponding training loss is similar to figure 10.…”

“…the Wiener-Hopf technique are proposed in this paper for duct acoustics and spatial derivatives, respectively. The first network model can find practical applications in (radial) wave mode detections, which is a popular topic in the aerospace research community for low-noise aircraft engine design [1][2][3][4], because of the connection between duct acoustics and aeroengine fan noise problems [5][6][7]. The second network can find applications in solving various partial differential equations that has attracted increasing research attentions recently [8][9][10].…”