Internal regulation in compressible turbulent shear layers

Matsuno, Kristen; Lele, Sanjiva K.

doi:10.1017/jfm.2020.925

Cited by 13 publications

(4 citation statements)

References 34 publications

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“…The size of the computational domain is twice as large than those in previous numerical simulations (Pantano & Sarkar 2002; Arun et al. 2019; Vadrot, Giauque & Corre 2020; Matsuno & Lele 2021). Further analysis in § 4 shows that the integral length scales and are sufficiently small compared with the length of the computational domain, ensuring that the self-similar growth of large-scale structures is not confined.…”

Section: Governing Equations and Numerical Methodsmentioning

confidence: 89%

“…The Kolmogorov length scale is defined by It is listed in table 1 that the resolution parameter is in the range at the centreline, where is the grid length in each direction, indicating that the resolution of the present simulations is fine enough to resolve down to the order of the Kolmorgorov length scale of the flow (Pantano & Sarkar 2002; Arun et al. 2019; Matsuno & Lele 2021). It is noted that the Kolmogorov length scale achieves its minimum value at the centreline, and it increases slightly during the self-similar region of the mixing layer, seeing the detailed analysis in § 4.…”

Section: Governing Equations and Numerical Methodsmentioning

confidence: 99%

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Compressibility effects on statistics and coherent structures of compressible turbulent mixing layers

Wang

Chen

2022

J. Fluid Mech.

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The effects of compressibility on the statistics and coherent structures of a temporally developing mixing layer are studied using numerical simulations at convective Mach numbers ranging from $M_c=0.2$ to $1.8$ and at Taylor Reynolds numbers up to 290. As the convective Mach number increases, the streamwise dissipation becomes more effective to suppress the turbulent kinetic energy. At $M_c=1.8$ , the streamwise dissipation increases much faster than the other two components in the transition region, even larger than pressure–strain redistribution, correlating with the streamwise elongated vortical structures at a higher level of compressibility. We confirm the existence of the large-scale high- and low-speed structures in the mixing layers, which accompany the spanwise Kelvin–Helmholtz rollers at low convective Mach number and dominate the mixing layer at higher convective Mach number. Conditional statistics demonstrate that the large-scale low-speed structures are lifted upwards by a pair of counter-rotating quasi-streamwise rollers flanking the structures. The small-scale vortical structures have an apparent preference for clustering into the top of the low-speed regions, which is directly associated with high-shearing motions on top of the low-speed structures. The high-speed structures statistically exhibit central symmetry with the low-speed structures. The statistics and dynamics of large-scale high- and low-speed structures in the compressible mixing layers resemble those in the outer region of the turbulent boundary layers, which reveals the universality of the large-scale structures in free shear and wall-bounded turbulence. A conceptual model is introduced for the large-scale high- and low-speed structures in turbulent mixing layers.

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Section: Governing Equations and Numerical Methodsmentioning

confidence: 89%

Section: Governing Equations and Numerical Methodsmentioning

confidence: 99%

Compressibility effects on statistics and coherent structures of compressible turbulent mixing layers

Wang

Chen

2022

J. Fluid Mech.

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“…Generally, a quantitative measure of compressibility effects is the Mach number U/c s , where U is the velocity scale of the flow. In fact, a variety of Mach numbers, including convective Mach number (M c ), gradient Mach number (M g ) or turbulent Mach number (M t ), are introduced to quantify compressible turbulence (see, e.g., [1,[52][53][54]. The convective Mach number is determined through the flow general parameters and for the temporal shear layer between two free streams of fluid with the velocities U 1 = −U 2 = ∆U/2 and constant density and sound speed, is defined as…”

Section: Dns Of Compressible Turbulent Temporal Shear Layermentioning

confidence: 99%

“…Aerodynamic sound generation is a major subject of fluid dynamics research and represents an enormous scientific and technological challenge. A simplified model to get physical insight into the mechanisms of aerodynamic sound generation in jets and other turbulent flows, is the compressible shear/mixing layer -the flow consisting of two parallel fluid streams with unequal velocities and, generally, densities as wellas it mimics shear layer regions of some natural and engineering non-uniform flows (see the recent paper by [1], and references therein). Over time, it has became clear that the flow turbulence is a mix of chaotic and coherent motions, focusing interest on coherent structures in exploration of aerodynamic sound sources.…”

Section: Introductionmentioning

confidence: 99%

The nature of aerodynamic sound of turbulent temporal shear layers

Khujadze,

Gogichaishvili,

Chagelishvili

et al. 2024

Preprint

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This paper aims to establish the pivotal role of the linear non-modal dynamics of perturbations in the aerodynamic sound emission of turbulent temporal shear/mixing layers at the self-similar stage of evolution. This is achieved by comparing the results of direct numerical simulations (DNS) of the mixing layer to the linear non-modal analysis of the flow central/body part model – 3D homentropic, unbounded flow, U0(Ay, 0, 0), with shear rate, A. The non-modal analysis captures the only linear mechanism of acoustic wave generation – the linear vortex-wave mode coupling induced by the non-modal dynamics of perturbations in shear flows. The efficiency of this linear generation is uniquely determined by the mode-dependent Mach number,M= Aλx/cs, related to the convective Mach number viaM= Mcλx/πΔy. Here, λx, cs and Δy denote the streamwise wavelength, speed of sound and shear-layer width, respectively. DNS of compressible turbulent temporal mixing layers were performed for different convective Mach numbers (Mc = 0.3, 0.7, 1.4) and simulation boxes (Lx, Ly, Lz) for reliably identifying the essence of the origin of sound in the turbulent layer. The simulations clearly show the decisive role of the linear non-modal mechanism of sound generation in the flow. This novelty highlights the significance of M in the sound generation, thus ensuring maximum efficiency for harmonics with largest streamwise wavelength, λx = Lx. Accordingly, the aerodynamic sound efficiency is determined by McLx rather than Mc. Overall, our analysis points to the inadequacy of employing the modal approach to describe compressible dynamic processes in the turbulent flow.

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