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Statistical models for spatial patterns of heavy particles in turbulence

Forskningsöversiktsartikel
Författare Kristian Gustafsson
Bernhard Mehlig
Publicerad i Advances in Physics
Volym 65
Nummer/häfte 1
Sidor 1-57
ISSN 0001-8732
Publiceringsår 2016
Publicerad vid Institutionen för fysik (GU)
Sidor 1-57
Språk en
Länkar dx.doi.org/10.1080/00018732.2016.11...
Ämnesord Turbulent aerosols
Ämneskategorier Fysik

Sammanfattning

The dynamics of heavy particles suspended in turbulent flows is of fundamental importance for a wide range of questions in astrophysics, atmospheric physics, oceanography, and technology. Laboratory experiments and numerical simulations have demonstrated that heavy particles respond in intricate ways to turbulent fluctuations of the carrying fluid: non-interacting particles may cluster together and form spatial patterns even though the fluid is incompressible, and the relative speeds of nearby particles can fluctuate strongly. Both phenomena depend sensitively on the parameters of the system. This parameter dependence is difficult to model from first principles since turbulence plays an essential role. Laboratory experiments are also very difficult, precisely since they must refer to a turbulent environment. But in recent years it has become clear that important aspects of the dynamics of heavy particles in turbulence can be understood in terms of statistical models where the turbulent fluctuations are approximated by Gaussian random functions with appropriate correlation functions. In this review we summarise how such statistical-model calculations have led to a detailed understanding of the factors that determine heavy-particle dynamics in turbulence. We concentrate on spatial clustering of heavy particles in turbulence. This is an important question because spatial clustering affects the collision rate between the particles and thus the long-term fate of the system.

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Denna text är utskriven från följande webbsida:
http://www.gu.se/forskning/publikation/?publicationId=233453
Utskriftsdatum: 2019-12-14