| 1. |
- Bec, J., et al.
(författare)
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Statistical Models for the Dynamics of Heavy Particles in Turbulence
- 2024
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Ingår i: ANNUAL REVIEW OF FLUID MECHANICS. - 0066-4189 .- 1545-4479. ; 56, s. 189-213
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Forskningsöversikt (refereegranskat)abstract
- When very small particles are suspended in a fluid in motion, they tend to follow the flow. How such tracer particles are mixed, transported, and dispersed by turbulent flow has been successfully described by statistical models. Heavy particles, with mass densities larger than that of the carrying fluid, can detach from the flow. This results in preferential sampling, small-scale fractal clustering, and large relative velocities. To describe these effects of particle inertia, one must consider both particle positions and velocities in phase space. In recent years, statistical phase-space models have significantly contributed to our understanding of inertial-particle dynamics in turbulence. These models help to identify the key mechanisms and nondimensional parameters governing the particle dynamics and have made qualitative and, in some cases, quantitative predictions. This article reviews statistical phase-space models for the dynamics of small, yet heavy, spherical particles in turbulence. We evaluate their effectiveness by comparing their predictions with results from numerical simulations and laboratory experiments, and we summarize their successes and failures.
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| 2. |
- Bhatnagar, Akshay, et al.
(författare)
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Relative velocities in bidisperse turbulent aerosols : Simulations and theory
- 2018
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Ingår i: Physical review. E. - : American Physical Society. - 2470-0045 .- 2470-0053. ; 98:6
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Tidskriftsartikel (refereegranskat)abstract
- We perform direct numerical simulations of a bidisperse suspension of heavy spherical particles in forced, homogeneous, and isotropic three-dimensional turbulence. We compute the joint distribution of relative particle distances and longitudinal relative velocities between particles of different inertia. For a pair of particles with small difference in their inertias we compare our results with recent theoretical predictions [Meibohm et al., Phys. Rev. E 96, 061102 (2017)] for the shape of this distribution. We also compute the moments of relative velocities as a function of particle separation and compare with the theoretical predictions. We observe good agreement. For a pair of particles that are very different from each other-one is heavy and the other one has negligible inertia-we give a theory to calculate their root-mean-square relative velocity. This theory also agrees well with the results of our simulations.
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| 3. |
- Bhatnagar, Akshay, et al.
(författare)
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Statistics of the relative velocity of particles in turbulent flows: Monodisperse particles
- 2018
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Ingår i: Physical Review E. - : AMER PHYSICAL SOC. - 2470-0045 .- 2470-0053. ; 97:2
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Tidskriftsartikel (refereegranskat)abstract
- We use direct numerical simulations to calculate the joint probability density function of the relative distance R and relative radial velocity component V-R for a pair of heavy inertial particles suspended in homogeneous and isotropic turbulent flows. At small scales the distribution is scale invariant, with a scaling exponent that is related to the particle-particle correlation dimension in phase space, D-2. It was argued [K. Gustavsson and B. Mehlig, Phys. Rev. E 84, 045304 (2011); J. Turbul. 15, 34 (2014)] that the scale invariant part of the distribution has two asymptotic regimes: (1) vertical bar V-R vertical bar << R, where the distribution depends solely on R, and (2) vertical bar V-R vertical bar >> R, where the distribution is a function of vertical bar V-R vertical bar alone. The probability distributions in these two regimes are matched along a straight line: vertical bar V-R vertical bar = z*R. Our simulations confirm that this is indeed correct. We further obtain D-2 and z* as a function of the Stokes number, St. The former depends nonmonotonically on St with aminimum at about St approximate to 0.7 and the latter has only a weak dependence on St.
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| 4. |
- Bhowmick, T., et al.
(författare)
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Inertia Induces Strong Orientation Fluctuations of Nonspherical Atmospheric Particles
- 2024
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Ingår i: Physical Review Letters. - 0031-9007 .- 1079-7114. ; 132:3
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Tidskriftsartikel (refereegranskat)abstract
- The orientation of nonspherical particles in the atmosphere, such as volcanic ash and ice crystals, influences their residence times and the radiative properties of the atmosphere. Here, we demonstrate experimentally that the orientation of heavy submillimeter spheroids settling in still air exhibits decaying oscillations, whereas it relaxes monotonically in liquids. Theoretical analysis shows that these oscillations are due to particle inertia, caused by the large particle-fluid mass-density ratio. This effect must be accounted for to model solid particles in the atmosphere.
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| 5. |
- Biferale, L., et al.
(författare)
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Helicoidal particles in turbulent flows with multi-scale helical injection
- 2019
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Ingår i: Journal of Fluid Mechanics. - : Cambridge University Press (CUP). - 0022-1120 .- 1469-7645. ; 869, s. 646-673
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Tidskriftsartikel (refereegranskat)abstract
- We present numerical and theoretical results concerning the properties of turbulent flows with strong multi-scale helical injection. We perform direct numerical simulations of the Navier-Stokes equations under a random helical stirring with power-law spectrum and with different intensities of energy and helicity injections. We show that there exists three different regimes where the forward energy and helicity inertial transfers are: (i) both leading with respect to the external injections, (ii) energy transfer is leading and helicity transfer is sub-leading and (iii) both are sub-leading and helicity is maximal at all scales. As a result, the cases (ii)-(iii) give flows with Kolmogorov-like inertial energy cascade and tuneable helicity transfers/contents. We further explore regime (iii) by studying its effect on the kinetics of point-like isotropic helicoids, particles whose dynamics is isotropic but breaks parity invariance. We investigate small-scale fractal clustering and preferential sampling of intense helical flow structures. Depending on their structural parameters, the isotropic helicoids either preferentially sample co-chiral or anti-chiral flow structures. We explain these findings in limiting cases in terms of what is known for spherical particles of different densities and degrees of inertia. Furthermore, we present theoretical and numerical results for a stochastic model where dynamical properties can be calculated using analytical perturbation theory. Our study shows that a suitable tuning of the stirring mechanism can strongly modify the small-scale turbulent helical properties and demonstrates that isotropic helicoids are the simplest particles able to preferentially sense helical properties in turbulence.
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| 6. |
- Borgnino, M., et al.
(författare)
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Alignment of elongated swimmers in a laminar and turbulent Kolmogorov flow
- 2022
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Ingår i: Physical Review Fluids. - 2469-990X. ; 7:7
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Tidskriftsartikel (refereegranskat)abstract
- Many aquatic microorganisms are able to swim. In natural environments they typically do so in the presence of flows. In recent years it has been shown that the interplay of swimming and flows can give rise to interesting and biologically relevant phenomena, such as accumulation of microorganisms in specific flow regions and local alignment with the flow properties. Here, we consider a mechanical model for elongated microswimmers in a Kolmogorov flow, a prototypic shear flow, both in steady and in turbulent conditions. By means of direct numerical simulations, supported by analytical calculation in a simplified stochastic setting, we find that the alignment of the swimming direction with the local velocity is a general phenomenon. We also explore how the accumulation of microorganisms, typically observed in steady flows, is modified by the presence of unsteady fluctuations.
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| 7. |
- Borgnino, M., et al.
(författare)
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Alignment of Nonspherical Active Particles in Chaotic Flows
- 2019
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Ingår i: Physical Review Letters. - 0031-9007. ; 123:13
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Tidskriftsartikel (refereegranskat)abstract
- We study the orientation statistics of spheroidal, axisymmetric microswimmers, with shapes ranging from disks to rods, swimming in chaotic, moderately turbulent flows. Numerical simulations show that rodlike active particles preferentially align with the flow velocity. To explain the underlying mechanism, we solve a statistical model via the perturbation theory. We show that such an alignment is caused by correlations of fluid velocity and its gradients along particle paths combined with fore-aft symmetry breaking due to both swimming and particle nonsphericity. Remarkably, the discovered alignment is found to be a robust kinematical effect, independent of the underlying flow evolution. We discuss its possible relevance for aquatic ecology.
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| 8. |
- Buzzicotti, M., et al.
(författare)
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Optimal control of point-to-point navigation in turbulent time dependent flows using reinforcement learning
- 2021
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Ingår i: CEUR Workshop Proceedings. - 1613-0073.
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Konferensbidrag (refereegranskat)abstract
- We present theoretical and numerical results concerning the problem to find the path that minimizes the time to navigate between two given points in a complex fluid and under realistic navigation constraints. We contrast deterministic Optimal Navigation (ON) control with stochastic policies obtained by Reinforcement Learning (RL) algorithms. We show that Actor-Critic RL algorithms are able to find quasi-optimal solutions in the presence of either time-independent or chaotically evolving flow configurations. For our application, ON solutions develop unstable behaviour within the typical duration of the navigation process, and are therefore not useful in practice. The explored setup consists of using a constant propulsion speed to navigate a turbulent flow. Based on a discretized phase-space the propulsion direction is adjusted with the aim to minimize the time spent to reach the target. Our approach can be generalized to other set-ups, for example unmanned navigation with minimal energy consumption under imperfect environmental forecast or with different models for the moving vessel.
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| 9. |
- Byron, M., et al.
(författare)
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Shape-dependence of particle rotation in isotropic turbulence
- 2015
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Ingår i: Physics of Fluids. - : AIP Publishing. - 1070-6631 .- 1089-7666. ; 27:3
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Tidskriftsartikel (refereegranskat)abstract
- We consider the rotation of neutrally buoyant axisymmetric particles suspended in isotropic turbulence. Using laboratory experiments as well as numerical and analytical calculations, we explore how particle rotation depends upon particle shape. We find that shape strongly affects orientational trajectories, but that it has negligible effect on the variance of the particle angular velocity. Previous work has shown that shape significantly affects the variance of the tumbling rate of axisymmetric particles. It follows that shape affects the spinning rate in away that is, on average, complementary to the shape-dependence of the tumbling rate. We confirm this relationship using direct numerical simulations, showing how tumbling rate and spinning rate variances show complementary trends for rod-shaped and disk-shaped particles. We also consider a random but non-turbulent flow. This allows us to explore which of the features observed for rotation in turbulent flow are due to the effects of particle alignment in vortex tubes. (C) 2015 AIP Publishing LLC.
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| 10. |
- Cichos, F., et al.
(författare)
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Machine learning for active matter
- 2020
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Ingår i: Nature Machine Intelligence. - : Springer Science and Business Media LLC. - 2522-5839. ; 2:2, s. 94-103
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Tidskriftsartikel (refereegranskat)abstract
- The availability of large datasets has boosted the application of machine learning in many fields and is now starting to shape active-matter research as well. Machine learning techniques have already been successfully applied to active-matter data-for example, deep neural networks to analyse images and track objects, and recurrent nets and random forests to analyse time series. Yet machine learning can also help to disentangle the complexity of biological active matter, helping, for example, to establish a relation between genetic code and emergent bacterial behaviour, to find navigation strategies in complex environments, and to map physical cues to animal behaviours. In this Review, we highlight the current state of the art in the application of machine learning to active matter and discuss opportunities and challenges that are emerging. We also emphasize how active matter and machine learning can work together for mutual benefit. This Review surveys machine learning techniques that are currently developed for a range of research topics in biological and artificial active matter and also discusses challenges and exciting opportunities. This research direction promises to help disentangle the complexity of active matter and gain fundamental insights for instance in collective behaviour of systems at many length scales from colonies of bacteria to animal flocks.
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