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A lowspeed wind tunnel is developed for fluid dynamics research at Reykjavik University. The tunnel is designed for conducting research on the flow past micro air vehicles, as well as fundamental research on turbulence. High flow quality is elemental for both research projects. The tunnel is of open suction type and is composed of a square inlet with a honeycomb and turbulence screens, settling chamber, contraction, experimental section housing, diffuser, and axial fan. Here, we describe the details of the design optimization procedure of the contraction, which is a key to getting a high quality flow in the experimental section. A high fidelity computational fluid dynamic (CFD) flow solver is used to capture the nonlinear flow physics. Due to the high computational cost of the CFD simulations, surrogate based optimization (SBO) is used to accelerate the design process. The SBO approach replaces direct optimization of the high fidelity (accurate but computationally expensive) model by iterative optimization of a properly corrected low fidelity model obtained from low fidelity CFD simulations. The optimum contraction design is verified using high fidelity CFD simulation, as well as by experimental measurements.

The role of turbulence in droplet growth in clouds is controversial, in part because of the difficulty of studying underlying processes in the cloud environment, and in part because of the difficulty of achieving real cloud conditions in controlled laboratory or computational studies. This paper is a synthesis of research on turbulence effects on cloud droplets that includes field and laboratory studies. Results from cloud measurements show that the turbulence exhibits similar internal intermittency to that observed in the laboratory, and in direct numerical simulations. We explore the consequences of this by relating measurements of droplet accelerations in the laboratory, to conditions observed in the clouds. We show that there is a strong likelihood of droplet accelerations in clouds exceeding the acceleration due to gravity. We discuss these observations in terms of the dynamics of droplets, including velocity statistics and clustering, and their influence on droplet growth.

Homogeneous, approximately isotropic turbulence at two Taylor-scale Reynolds numbers,Rλ=50, 190, with a mean transverse temperature gradient is passed through an axisymmetric contraction. The effects of the straining on the velocity field, and on the passive scalar field, are investigated within the contraction as are the effects of releasing the strain in the post-contraction region. Components of the fluctuating velocity and scalar gradient covariance are measured in order to understand their relation to the large-scale anisotropy of the flow. The scale-dependent spectral evolution of the scalar is also determined. A tensor model is constructed to predict the evolution of the fluctuating scalar gradient covariance. The model constants are determined in the post-contraction relaxation region, where the flow geometry does not vary. The model is shown to perform well throughout the flow, even in the contraction in which the geometry varies. Rapid distortion theory is applied to the scalar field in the contraction, and its solutions are compared to the experimental results.

The dynamics of inertial particles in turbulence is modelled and investigated by means of direct numerical simulation of an axisymmetrically expanding homogeneous turbulent strained flow. This flow can mimic the dynamics of particles close to stagnation points. The influence of mean straining flow is explored by varying the dimensionless strain rate parameter $Sk_{0}/{\it\epsilon}_{0}$ from 0.2 to 20, where $S$ is the mean strain rate, $k_{0}$ and ${\it\epsilon}_{0}$ are the turbulent kinetic energy and energy dissipation rate at the onset of straining. We report results relative to the acceleration variances and probability density functions for both passive and inertial particles. A high mean strain is found to have a significant effect on the acceleration variance both directly by an increase in the frequency of the turbulence and indirectly through the coupling of the fluctuating velocity and the mean flow field. The influence of the strain on the normalized particle acceleration probability distribution functions is more subtle. For the case of a passive particle we can approximate the acceleration variance with the aid of rapid-distortion theory and obtain good agreement with simulation data. For the case of inertial particles we can write a formal expression for the accelerations. The magnitude changes in the inertial particle acceleration variance and the effect on the probability density function are then discussed in a wider context for comparable flows, where the effects of the mean flow geometry and of the anisotropy at small scales are present.

We perform direct numerical simulations of an unstably stratified turbulent channel flow to address the effects of buoyancy on the boundary layer dynamics and mean field quantities. We systematically span a range of parameters in the space of friction Reynolds number ($\mathit{Re}_{{\it\tau}}$) and Rayleigh number ($\mathit{Ra}$). Our focus is on deviations from the logarithmic law of the wall due to buoyant motion. The effects of convection in the relevant ranges are discussed, providing measurements of mean profiles of velocity, temperature and Reynolds stresses as well as of the friction coefficient. A phenomenological model is proposed and shown to capture the observed deviations of the velocity profile in the log-law region from the non-convective case.

The scalar structure function scaling exponent ζn is experimentally determined for n⩽10 in decaying, grid-generated wind-tunnel turbulence with a constant mean temperature gradient. The Reynolds number is varied over the range 150⩽Rλ⩽700 by using static and active grids. The results show that up to n=10 the scaling exponent does not saturate although saturation is not precluded at higher orders. There appears to be no dependence of ζn on Reynolds number and the values of ζn are the same for the transverse (along the gradient) and the longitudinal (streamwise) structure functions. A compilation of previous work shows large variation in ζn, with a few results indicating saturation and most not. Reasons for the scatter are attributed to convergence problems at high orders, effects of flow or computational domain size causing clipping of large rare fluctuations, and differences in initial and boundary conditions.

From hot-wire anemometer measurements in active-grid wind-tunnel turbulence we have determined the Reynolds number dependence of the velocity derivative moments, the mean-squared pressure gradient, $\chi$ , and the normalized acceleration variance, $a_0$ , over the Reynolds number range $100\,{\leq}\,R_\lambda\,{\leq}\,900$ . The values of $\chi$ and $a_0$ were obtained from the fourth-order velocity structure functions. The derivative moments show power-law dependence on Reynolds number and the exponent is the same with or without shear. In particular, we find the derivative kurtosis, $K_{\partial u/\partial x }\,{\sim}\,R_\lambda^{0.39}$ , and there is no evidence of the transition that has been observed in this quantity in some recent work. We find that at high Reynolds numbers, $\chi$ and $a_0$ tend to values similar to those obtained by direct particle tracking measurements and by direct numerical simulation. However, at lower Reynolds number our estimates of $\chi$ and $a_0$ appear to be affected by the evaluation technique which imposes strict requirements on local homogeneity and isotropy.

We present a numerical study of Rayleigh-B\'enard convection disturbed by a longitudinal wind. Our results show that under the action of the wind, the vertical heat flux through the cell initially decreases, due to the mechanism of plumes-sweeping, and then increases again when turbulent forced convection dominates over the buoyancy. As a result, the Nusselt number is a non-monotonic function of the shear Reynolds number. We provide a simple model that captures with good accuracy all the dynamical regimes observed. We expect that our findings can lead the way to a more fundamental understanding of the of the complex interplay between mean-wind and plumes ejection in the Rayleigh-B\'enard phenomenology., Comment: 5 pages, 4 figures

We present preliminary measurements of the Lagrangian acceleration probability density function (pdf) of fluid particles in decaying grid-generated turbulence and show that they are in very good agreement with direct numerical simulations determined at the same Reynolds number. We contrast these pdf’s with inertial particle acceleration pdf’s done in the same apparatus.

We describe Lagrangian measurements of water droplets in grid generated wind tunnel turbulence at a Taylor Reynolds number of ${R}_{\ensuremath{\lambda}}=250$ and an average Stokes number ($⟨\mathrm{St}⟩$) of approximately 0.1. The inertial particles are tracked by a high speed camera moving along the side of the tunnel at the mean flow speed. The standardized acceleration probability density functions of the particles have spread exponential tails that are narrower than those of a fluid particles ($\mathrm{St}\ensuremath{\approx}0$) and there is a decrease in the acceleration variance with increasing Stokes number. A simple vortex model shows that the inertial particles selectively sample the fluid field and are less likely to experience regions of the fluid undergoing the largest accelerations. Recent direct numerical simulations compare favorably with these first measurements of Lagrangian statistics of inertial particles in highly turbulent flows.

We report experimental evidence of spatial clustering of dense particles in homogenous, isotropic turbulence at high Reynolds numbers. The dissipation-scale clustering becomes stronger as the Stokes number increases and is found to exhibit similarity with respect to the droplet Stokes number over a range of experimental conditions (particle diameter and turbulent energy dissipation rate). These findings are in qualitative agreement with recent theoretical and computational studies of inertial particle clustering in turbulence. Because of the large Reynolds numbers a broad scaling range of particle clustering due to turbulent mixing is present, and the inertial clustering can clearly be distinguished from that due to mixing of fluid particles.

We present results from direct numerical simulations of strained turbulent flows. Our focus is twofold. One is to improve our understanding of the interactions of large and small scale dynamics in strained turbulent flow; another focus is to understand the influence of the straining on the motions of passive and inertial particles of varied Stokes numbers. We seek to emphasize the effects of the strain geometry and strain rate on the particles' behaviors. Eulerian flow field results and the Lagrangian particle velocity and acceleration statistics will be discussed. The Rogallo algorithm is applied for simulating the flow field in a non-cubical domain.