Translational and rotational dynamics of a large buoyant sphere in turbulence

We report experimental measurements of the translational and rotational dynamics of a large buoyant sphere in isotropic turbulence. We introduce an efficient method to simultaneously determine the position and (absolute) orientation of a spherical body from visual observation. The method employs a minimization algorithm to obtain the orientation from the 2D projection of a specific pattern drawn onto the surface of the sphere. This has the advantages that it does not require a database of reference images, is easily scalable using parallel processing, and enables accurate absolute orientation reference. Analysis of the sphere’s translational dynamics reveals clear differences between the streamwise and transverse directions. The translational autocorrelations and PDFs provide evidence for periodicity in the particle’s dynamics even under turbulent conditions. The angular autocorrelations show weak periodicity. The angular accelerations exhibit wide tails, however without a directional dependence

Penetrative internally heated convection in two and three dimensions

Convection of an internally heated fluid, confined between top and bottom plates of equal temperature, is studied by direct numerical simulation in two and three dimensions. The unstably stratified upper region drives convection that penetrates into the stably stratified lower region. The fraction of produced heat escaping across the bottom plate, which is one half without convection, initially decreases as convection strengthens. Entering the turbulent regime, this decrease reverses in two dimensions but continues monotonically in three dimensions. The mean fluid temperature, which grows proportionally to the heating rate (H) without convection, grows proportionally to H4=5 when convection is strong in both two and three dimensions. The ratio of the heating rate to the fluid temperature is likened to the Nusselt number of Rayleigh–Bénard convection. Simulations are reported for Prandtl numbers between 0.1 and 10 and for Rayleigh numbers (defined in terms of the heating rate) up to 5 1010

Comparison between two- and three-dimensional Rayleigh- Bénard convection

Two-dimensional and three-dimensional Rayleigh–Bénard convection is compared using results from direct numerical simulations and previous experiments. The phase diagrams for both cases are reviewed. The differences and similarities between two- and three-dimensional convection are studied using Nu(Ra) for Pr=4.38 and Pr=0.7 and Nu(Pr) for Ra up to 108. In the Nu(Ra) scaling at higher Pr, two- and three-dimensional convection is very similar, differing only by a constant factor up to Ra=1010. In contrast, the difference is large at lower Pr, due to the strong roll state dependence of Nu in two dimensions. The behaviour of Nu(Pr) is similar in two and three dimensions at large Pr. However, it differs significantly around Pr=1. The Reynolds number values are consistently higher in two dimensions and additionally converge at large Pr. Finally, the thermal boundary layer profiles are compared in two and three dimension

The unifying theory of scaling in thermal convection: the updated prefactors

The unifying theory of scaling in thermal convection (Grossmann & Lohse, J. Fluid. Mech., vol. 407, 2000, pp. 27–56; henceforth the GL theory) suggests that there are no pure power laws for the Nusselt and Reynolds numbers as function of the Rayleigh and Prandtl numbers in the experimentally accessible parameter regime. In Grossmann & Lohse (Phys. Rev. Lett., vol. 86, 2001, pp. 3316–3319) the dimensionless parameters of the theory were fitted to 155 experimental data points by Ahlers & Xu (Phys. Rev. Lett., vol. 86, 2001, pp. 3320–3323) in the regime 3×107≤Ra≤3×109 and 4≤Pr≤34 and Grossmann & Lohse (Phys. Rev. E, vol. 66, 2002, p. 016305) used the experimental data point from Qiu & Tong (Phys. Rev. E, vol. 64, 2001, p. 036304) and the fact that Nu(Ra,Pr) is independent of the parameter a, which relates the dimensionless kinetic boundary thickness with the square root of the wind Reynolds number, to fix the Reynolds number dependence. Meanwhile the theory is, on the one hand, well-confirmed through various new experiments and numerical simulations; on the other hand, these new data points provide the basis for an updated fit in a much larger parameter space. Here we pick four well-established (and sufficiently distant) Nu(Ra,Pr) data points and show that the resulting Nu(Ra,Pr) function is in agreement with almost all established experimental and numerical data up to the ultimate regime of thermal convection, whose onset also follows from the theory. One extra Re(Ra,Pr) data point is used to fix Re(Ra,Pr). As Re can depend on the definition and the aspect ratio, the transformation properties of the GL equations are discussed in order to show how the GL coefficients can easily be adapted to new Reynolds number data while keeping Nu(Ra,Pr) unchange

Plume emission statistics in turbulent Rayleigh- Bénard convection

Direct numerical simulations (DNS) of turbulent thermal convection in a PrD0:7 fluid up to RaD1012 are used to study the statistics of thermal plumes. At various vertical locations in a cylindrical set-up with aspect ratio D width=height D 1=3, plumes are identified and their properties extracted. It is found that plumes are much less likely to be emitted from plate regions with large wind shear. Close to the plates, the plumes have a unimodal log–normal distribution, whereas at more central locations the distribution becomes weakly bimodal, which can be traced back to clustering of the plumes and influence of the large-scale circulation. The number of hot plumes decreases with height. The width of the plumes scales with Ra approximately as Nu indicating that it is determined by the thermal boundary layer thickness

A multiple-resolution strategy for Direct Numerical Simulation of scalar turbulence

In this paper a numerical procedure to simulate low diffusivity scalar turbulence is presented. The method consists of using a grid for the advected scalar with a higher spatial resolutionthan that of the momentum. The latter usually requires a less refined mesh and integrating both fields on a single grid tailored to the most demanding variableproduces an unnecessary computational overhead. A multiple resolution approach is used also in the time integration in order to maintain the stability of the scalars on the finer grid. The method is the more advantageous the less diffusive the scalar is with respect to momentum, therefore it is particularly well suited for large Prandtl or Schmidt number flows. However, even in the case of equal diffusivities the present procedure gives CPU time and memory occupation savings, due to the increased gradients and more intermittent behaviour of the scalars when compared to momentum

Seroprevalence of Schmallenberg virus antibodies among dairy cattle, the Netherlands, winter 2011-2012.

Contains fulltext : 109487.pdf (publisher's version ) (Open Access)Infections with Schmallenberg virus (SBV) are associated with congenital malformations in ruminants. Because reporting of suspected cases only could underestimate the true rate of infection, we conducted a seroprevalence study in the Netherlands to detect past exposure to SBV among dairy cattle. A total of 1,123 serum samples collected from cattle during November 2011-January 2012 were tested for antibodies against SBV by using a virus neutralization test; seroprevalence was 72.5%. Seroprevalence was significantly higher in the central-eastern part of the Netherlands than in the northern and southern regions (p<0.001). In addition, high (70%-100%) within-herd seroprevalence was observed in 2 SBV-infected dairy herds and 2 SBV-infected sheep herds. No significant differences were found in age-specific prevalence of antibodies against SBV, which is an indication that SBV is newly arrived in the country.01 juli 201

Schmallenberg virus outbreak in the Netherlands: Routine diagnostics and test results

In 2006 and 2007 pig farming in the region of Lombardy, in the north of Italy, was struck by an epidemic of Swine Vesicular Disease virus (SVDV). In fact this epidemic could be viewed as consisting of two sub-epidemics, as the reported outbreaks occurred in two separate time periods. These periods differed in terms of the provinces or municipalities that were affected and also in terms of the timing of implementation of movement restrictions. Here we use a simple mathematical model to analyse the epidemic data, quantifying between-farm transmission probability as a function of between-farm distance. The results show that the distance dependence of between-farm transmission differs between the two periods. In the first period transmission over relatively long distances occurred with higher probability than in the second period, reflecting the effect of movement restrictions in the second period. In the second period however, more intensive transmission occurred over relatively short distances. Our model analysis explains this in terms of the relatively high density of pig farms in the area most affected in this period, which exceeds a critical farm density for between-farm transmission. This latter result supports the rationale for the additional control measure taken in 2007 of pre-emptively culling farms in that area

Schmallenberg virus outbreak in the Netherlands: Routine diagnostics and test results

In 2006 and 2007 pig farming in the region of Lombardy, in the north of Italy, was struck by an epidemic of Swine Vesicular Disease virus (SVDV). In fact this epidemic could be viewed as consisting of two sub-epidemics, as the reported outbreaks occurred in two separate time periods. These periods differed in terms of the provinces or municipalities that were affected and also in terms of the timing of implementation of movement restrictions. Here we use a simple mathematical model to analyse the epidemic data, quantifying between-farm transmission probability as a function of between-farm distance. The results show that the distance dependence of between-farm transmission differs between the two periods. In the first period transmission over relatively long distances occurred with higher probability than in the second period, reflecting the effect of movement restrictions in the second period. In the second period however, more intensive transmission occurred over relatively short distances. Our model analysis explains this in terms of the relatively high density of pig farms in the area most affected in this period, which exceeds a critical farm density for between-farm transmission. This latter result supports the rationale for the additional control measure taken in 2007 of pre-emptively culling farms in that area