Inertial effects in dusty Rayleigh–Taylor turbulence

We investigate the dynamics of a dilute suspension of small, heavy particles superposed on a reservoir of still, pure fluid. The study is performed by means of numerical simulations of the Saffman model for a dilute particle suspension (Saffman, J. Fluid Mech., vol. 13, issue 1, 1962, pp. 120–128). In the presence of gravity forces, the interface between the two phases is unstable and evolves in a turbulent mixing layer which broadens in time. In the case of negligible particle inertia, the particle-laden phase behaves as a denser fluid, and the dynamics of the system recovers to that of the incompressible Rayleigh–Taylor set-up. Conversely, particles with large inertia affect the evolution of turbulent flow, delaying the development of turbulent mixing and breaking the up–down symmetry within the mixing layer. The inertial dynamics also leads to particle clustering, characterised by regions with higher particle density than the initial uniform density, and by the increase of the local Atwood number.

Statistically Targeted Forcing (STF) Method for Synthetic Turbulence Generation of Initial Conditions in Three-Dimensional Turbulent Mixing Layer Flow

Computational fluid dynamics (CFD) results are presented for synthetic turbulence generation of initial conditions for the canonical test case of a temporally-developing turbulent mixing layer (TTML) flow. This numerical study investigates the performance of a newly proposed Statistically Targeted Forcing (STF) method, and its capability to act as a restoring force to match the target mean velocity and turbulent stress in a temporally-developing flow where highly unsteady destabilizing mechanisms and influence are evident. Several previous investigations exist documenting vortex dynamics of the turbulent mixing layer, but limited investigations exist on synthetic turbulence generation forcing methods to prescribe initial conditions. The objective of this study is to evaluate the performance of the newly proposed STF method to capture the vortex dynamics and effectively match target mean velocity and resolved turbulent stress predictions using large-eddy simulation. Results are interrogated and compared to statistical velocity and turbulent stress distributions obtained from DNS simulations available in the literature. Results show that the STF method can successfully reproduce desired statistical distributions in a turbulent mixing layer flow.

Estimation of the height of the turbulent mixing layer from data of Doppler lidar measurements using conical scanning by a probe beam

A method is proposed for determining the height of the turbulent mixing layer on the basis of the vertical profiles of the dissipation rate of turbulent energy, which is estimated from lidar measurements of the radial wind velocity using conical scanning by a probe beam around the vertical axis. The accuracy of the proposed method is discussed in detail. It is shown that for the estimation of the mixing layer height (MLH) with the acceptable relative error not exceeding 20 %, the signal-to-noise ratio should be no less than −16 dB, when the relative error of lidar estimation of the dissipation rate does not exceed 30 %. The method was tested in a 6 d experiment in which the wind velocity turbulence was estimated in smog conditions due to forest fires in Siberia in summer 2019. The results of the experiment reveal that the relative error of determination of the MLH time series obtained by this method does not exceed 10 % in the period of turbulence development. The estimates of the turbulent mixing layer height by the proposed method are in a qualitative agreement with the MLH estimated from the distributions of the Richardson number in height and time obtained during the comparison experiment in spring 2020.

Estimation of the height of turbulent mixing layer from data of Doppler lidar measurements using conical scanning by a probe beam

A method is proposed for determining the height of the turbulent mixing layer on the basis of the vertical profiles of the dissipation rate of turbulent energy, which is estimated from lidar measurements of the radial wind velocity using conical scanning by a probe beam around the vertical axis. The accuracy of the proposed method is discussed in detail. It is shown that for the estimation of the mixing layer height (MLH) with the acceptable relative error not exceeding 20 %, the signal-to-noise ratio should be no less than −16 dB, when the relative error of lidar estimation of the dissipation rate does not exceed 30 %. The method was tested in an experiment in which the wind velocity turbulence was estimated in smog conditions due to forest fires in Siberia in 2019. The results of the experiment reveal that the relative error of determination of the MLH time series obtained by this method does not exceed 10 % in the period of turbulence development. The estimates of the turbulent mixing layer height by the proposed method are in good agreement with the MLH estimated from the distributions of the variance of radial velocity and the Richardson number in height and time.