Heavy Particle Concentration in Turbulence at Dissipative and Inertial Scales

Abstract

Document VersionPublisher's PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication:• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Spatial distributions of heavy particles suspended in an incompressible isotropic and homogeneous turbulent flow are investigated by means of high resolution direct numerical simulations. In the dissipative range, it is shown that particles form fractal clusters with properties independent of the Reynolds number. Clustering is there optimal when the particle response time is of the order of the Kolmogorov time scale . In the inertial range, the particle distribution is no longer scale invariant. It is, however, shown that deviations from uniformity depend on a rescaled contraction rate, which is different from the local Stokes number given by dimensional analysis. Particle distribution is characterized by voids spanning all scales of the turbulent flow; their signature in the coarse-grained mass probability distribution is an algebraic behavior at small densities.

Turbulence drives microscale patches of motile phytoplankton

“…Previous studies have demonstrated that the trajectories of passive tracer particles integrated via this numerical scheme accurately capture both the velocity 46 and the acceleration 47 statistics of the underlying DNS-derived flow. Moreover, previous studies on clustering of inertial particles 48 have demonstrated the efficacy of this method to resolve sub-Kolmogorov scale fractal aggregations, which we also observed for gyrotactic swimmers.…”

Stochastic suspensions of heavy particles

“…Numerical studies [10,50] show that the qualitative St-dependence of D 2 is similar to that observed in the Kraichnan model. Despite such similarities, it is likely that in turbulence, ejection from vortical regions play, at least for small St, an important role [50]. This can clearly not be accounted for in Kraichnan flows, as δ-correlated flows have no persistent structures.…”

“…However, while in the Kraichnan case the particle distribution depends on the local Stokes number St(r) only, this does not seem to be the case in turbulence, at least for St(r) ≪ 1 as studied in [50] ( which in turbulence is defined by St(r) = τ /τ r , τ r being the characteristic turbulent time scale associated to the scale r). In turbulent flows, for small values of St(r), a different rescaling related to that of the acceleration (and hence pressure) field has been found [50]. However such discrepancies do not question the relevance of the Kraichnan model to turbulent flows as it is expected to be a good approximation only for scales r such that τ r ≪ τ , i.e.…”

Droplet growth in warm turbulent clouds

“…For values of St not too large, the RDF for a monodisperse system typically takes the form of a power law which reflects the multi-scale self-similar nature of droplet clustering (e.g. Figures 7 and 8 of Goto and Vassilicos, 2006;Bec, 2005;Bec et al, 2007). This multi-scale clustering has been accounted for in terms of the sweep-stick mechanism, which explains why droplet clustering mimics the multi-scale clustering of vanishing fluid acceleration points in a turbulent flow (Coleman and Vassilicos, 2009).…”