Generalised quasilinear approximations of turbulent channel flow: Part 1. Streamwise nonlinear energy transfer

Abstract

A generalised quasilinear (GQL) approximation (Marston \emph{et al.}, \emph{Phys. Rev. Lett.}, vol. 116, 104502, 2016) is applied to turbulent channel flow at $R{e}_{\tau }\simeq 1700$ ($R{e}_{\tau }$ is the friction Reynolds number), with emphasis on the energy transfer in the streamwise wavenumber space. The flow is decomposed into low and high streamwise wavenumber groups, the former of which is solved by considering the full nonlinear equations whereas the latter is obtained from the linearised equations around the former. The performance of the GQL approximation is subsequently compared with that of a QL model (Thomas \emph{et al.}, \emph{Phys. Fluids.}, vol. 26, no. 10, 105112, 2014), in which the low-wavenumber group only contains zero streamwise wavenumber. It is found that the QL model exhibits a considerably reduced multi-scale behaviour at the given moderately high Reynolds number. This is improved significantly by the GQL approximation which incorporates only a few more streamwise Fourier modes into the low-wavenumber group, and it reasonably well recovers the distance-from-the-wall scaling in the turbulence statistics and spectra. Finally, it is proposed that the energy transfer from the low to the high-wavenumber group in the GQL approximation, referred to as the `scattering' mechanism, depends on the neutrally stable leading Lyapunov spectrum of the linearised equations for the high wavenumber group. In particular, it is shown that if the threshold wavenumber distinguishing the two groups is sufficiently high, the scattering mechanism can completely be absent due to the linear nature of the equations for the high-wavenumber group.

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Cited by in corpus (4)

• Recent Developments in Theories of Inhomogeneous and Anisotropic Turbulence
• Generalised quasilinear approximations of turbulent channel flow: Part 2. Spanwise scale interactions
• Improved assessment of the statistical stability of turbulent flows using extended Orr-Sommerfeld stability analysis
• Direct Statistical Simulation Using Generalised Cumulant Expansions