Browsing by Subject "Turbulence"

Sort by: Order: Results:

Now showing items 1-6 of 6
  • Chen, Lifeng; Dong, Zhao; Jiang, Jifa; Niu, Lei; Zhai, Jianliang (2019)
    Motivated by the remarkable works of Busse and his collaborators in the 1980s on turbulent convection in a rotating layer, we explore the long-run behavior of stochastic Lotka-Volterra (LV) systems both in pull-back trajectories and in stationary measures. A decomposition formula is established to describe the relationship between the solutions of stochastic and deterministic LV systems and the stochastic logistic equation. By virtue of this formula, it can be verified that every pull-back omega limit set is an omega limit set of the deterministic LV system multiplied by the random equilibrium of the stochastic logistic equation. The formula is also used to derive the existence of a stationary measure, its support and ergodicity. We prove the tightness of stationary measures and that their weak limits are invariant with respect to the corresponding deterministic system and supported on the Birkhoff center. The developed theory is successfully utilized to completely classify three dimensional competitive stochastic LV systems into 37 classes. The expected occupation measures weakly converge to a strongly mixing measure and all stationary measures are obtained for each class except class 27 c). Among them there are two classes possessing a continuum of random closed orbits and strongly mixing measures supported on the cone surfaces, which weakly converge to the Haar measures of periodic orbits as the noise intensity vanishes. The class 27 c) is an exception, almost every pull-back trajectory cyclically oscillates around the boundary of the stochastic carrying simplex characterized by three unstable stationary solutions. The limit of the expected occupation measures is neither unique nor ergodic. These are consistent with symptoms of turbulence. (C) 2019 Elsevier Masson SAS. All rights reserved.
  • Afonso, M.M.; Mazzino, A.; Muratore-Ginanneschi, P. (2013)
    We perform an analytical study of the inertial-particle dynamics in the limit of small but finite inertia, in incompressible flows, exploring two specific issues. First, by means of a multiscale expansion, we analyse the particle effective diffusivity, and in particular its dependence on Brownian diffusivity, gravity and particle-to-fluid density ratio. We identify some cases of anomalous diffusion. Secondly, we investigate the concentration of particles continuously emitted from a point source with a given exit velocity distribution. The anisotropy of the latter turns out to be a necessary factor for the presence of a correction (with respect to the corresponding tracer case) at order square root of the Stokes number. In both cases, we obtain forced advection-diffusion equations for auxiliary quantities in the physical space, thus simplifying the problem from the full phase space to a system which can easily be solved numerically. Copyright © ETC 2013 - 14th European Turbulence Conference.All rights reserved.
  • Warnecke, Jörn; Käpylä, Petri J.; Mantere, Maarit J.; Brandenburg, Axel (2012)
  • Musacchio, Stefano; Boffetta, Guido; Muratore-Ginanneschi, Paolo (2020)
    We investigate the mechanisms of transfer of kinetic and potential energy in stably stratified turbulent flows. By means of high-resolution direct numerical simulations and theoretical analysis of the Karman-Howarth-Monin relations we show that increasing the stratification the transfer of energy in the vertical direction is suppressed and a joint forward cascade of kinetic and potential energy develops. Copyright © ETC 2013 - 14th European Turbulence Conference.All rights reserved.
  • Horppila, Jukka; Härkönen, Laura; Hellén, Noora; Estlander, Satu; Pekcan-Hekim, Zeynep; Ojala, Anne (2019)
    The effects of water turbulence on rotifer communities were experimentally studied under different predation pressures. When the larvae of the phantom midge (Chaoborus flavicans) were present in turbulent water, the abundance of most rotifer taxa was enhanced. Especially the genera Chromogaster, Keratella, Polyarthra, and Trichocerca, increased in abundance. In calm water, chaoborids did not affect the rotifer community. In turbulent water predation by chaoborids was targeted more towards cladocerans (Bosmina sp.) and predation pressure on rotifers was relaxed. Additionally, reduced competition with cladocerans probably contributed to the increase of rotifer abundance. Turbulence alone had no significant effect on rotifer abundance because their individual size was small compared with the diameter of the turbulent eddies. The study suggested that the effects of turbulence on rotifers is not direct but takes place through changed predator-prey relations, i.e., the effect depends on the abundance of invertebrate predators. In aquatic ecosystems with a high density of chaoborids, increasing turbulence can considerably increase the abundance of rotifers.
  • Bussov, Maarja; Nattila, Joonas (2021)
    Computer vision and machine learning tools offer an exciting new way for automatically analyzing and categorizing information from complex computer simulations. Here we design an ensemble machine learning framework that can independently and robustly categorize and dissect simulation data output contents of turbulent flow patterns into distinct structure catalogs. The segmentation is performed using an unsupervised clustering algorithm, which segments physical structures by grouping together similar pixels in simulation images. The accuracy and robustness of the resulting segment region boundaries are enhanced by combining information from multiple simultaneously-evaluated clustering operations. The stacking of object segmentation evaluations is performed using image mask combination operations. This statistically-combined ensemble (SCE) of different cluster masks allows us to construct cluster reliability metrics for each pixel and for the associated segments without any prior user input. By comparing the similarity of different cluster occurrences in the ensemble, we can also assess the optimal number of clusters needed to describe the data. Furthermore, by relying on ensemble-averaged spatial segment region boundaries, the SCE method enables reconstruction of more accurate and robust region of interest (ROI) boundaries for the different image data clusters. We apply the SCE algorithm to 2-dimensional simulation data snapshots of magnetically-dominated fully-kinetic turbulent plasma flows where accurate ROI boundaries are needed for geometrical measurements of intermittent flow structures known as current sheets.