Transitions between multiple dynamical states in a confined dense active-particle system

Mahapatra, Pallab Sinha; Kulkarni, Ajinkya; Mathew, Sam; Panchagnula, Mahesh V.; Vedantam, Srikanth

doi:10.1103/physreve.95.062610

Cited by 13 publications

(14 citation statements)

References 36 publications

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“…The ABP model has also been implemented with additional complexities e.g. confinement [13,[17][18][19], anisotropy in particle shape [20], particle interactions [21] and size polydispersity [17,22,23] leading to the discovery of new emergent phenomena, such as particle sorting and segregation.…”

Section: Introductionmentioning

confidence: 99%

Active binary mixtures of fast and slow hard spheres

Kolb

Klotsa

2020

Soft Matter

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We computationally studied the phase behavior and dynamics of binary mixtures of active particles, where each 'species' had distinct activities leading to distinct velocities, fast and slow. We obtained phase diagrams demonstrating motility-induced phase separation (MIPS) upon varying the activity and concentration of each species, and extended current kinetic theory of active/passive mixtures to active/active mixtures. We discovered two regimes of behavior quantified through the participation of each species in the dense phase compared to their monodisperse counterparts. In regime I (active/passive and active/weakly-active), we found that the dense phase was segregated by particle type into domains of fast and slow particles. Moreover, fast particles were suppressed from entering the dense phase while slow particles were enhanced entering the dense phase, compared to monodisperse systems of all-fast or all-slow particles. These effects decayed asymptotically as the activity of the slow species increased, approaching the activity of the fast species until they were negligible (regime II). In regime II, the dense phase was homogeneously mixed and each species participated in the dense phase as if it were it a monodisperse system; each species behaved as if it weren't mixed at all. Finally, we collapsed our data defining two quantities that measure the total activity of the system. We thus found expressions that predict, a priori, the percentage of each particle type that participates in the dense phase through MIPS.

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Section: Introductionmentioning

confidence: 99%

Active binary mixtures of fast and slow hard spheres

Kolb

Klotsa

2020

Soft Matter

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“…Similar to the Mahapatra et al . 56 and Carillo et al . 58 ,where, C v is a coordination coefficient and is the average velocity of the agents.…”

Section: Methodsmentioning

confidence: 93%

“…4 to restrict the unbounded acceleration of the agents. For a dense system, the dependency of α on the final solution is very less 56 . Similarly for the predator, the self-propelled force is modelled as,where M is the mass of the predator, is a unit vector in the direction of the velocity of the predator.…”

Section: Methodsmentioning

confidence: 99%

Confined System Analysis of a Predator-Prey Minimalistic Model

Mohapatra

Mahapatra

2019

Sci Rep

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In nature exists a properly defined food chain- an order of hunting and getting hunted. One such hunter-hunted pair is considered in this context and coordinated escape manoeuvres in response to predation is studied in case of a rarely examined confined system. Both the predator agent and prey agents are considered to be self-propelled particles moving in a viscous fluid. The state of motility when alive and passivity on death has been accounted for. A novel individual-based combination of Vicsek model and Boids flocking model is used for defining the self-propelling action and inter-agent interactions. The regimes observed at differing levels of co-ordination segregated by quantification of global order parameter are found to be in agreement with the extant literature. This study strives to understand the penalty on the collective motion due to the restraints employed by the rigid walls of the confinement and the predator’s hunting tactics. The success of any escape manoeuvre is dependent on the rate of information transfer and the strength of the agitation at the source of the manoeuvre. The rate of information transfer is studied as a function of co-ordination and the size of the influence zone and the source strength is studied as a function of escape acceleration activated on the agitated prey. The role of these factors in affecting survival rate of prey is given due coverage.

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“…This becomes a motivation for the present work to develop an active Langevin force model for crowd analysis. In [45] and [46], the Viscek model have been used for slurry dynamics to understand the transitions in confined dense active-particle systems. This has motivated us to develop the present method by combining active Langevin force and other components of the force to obtain a model that can segment linear and non-linear motions in crowd.…”

Section: B Motivation and Contributionsmentioning

confidence: 99%

Understanding Crowd Flow Movements Using Active-Langevin Model

Behera¹,

Dogra²,

Bandyopadhyay³

et al. 2020

Preprint

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Crowd flow describes the elementary group behavior of crowds. Understanding the dynamics behind these movements can help to identify various abnormalities in crowds. However, developing a crowd model describing these flows is a challenging task. In this paper, a physics-based model is proposed to describe the movements in dense crowds. The crowd model is based on active Langevin equation where the motion points are assumed to be similar to active colloidal particles in fluids. The model is further augmented with computer-vision techniques to segment both linear and non-linear motion flows in a dense crowd. The evaluation of the active Langevin equation-based crowd segmentation has been done on publicly available crowd videos and on our own videos. The proposed method is able to segment the flow with lesser optical flow error and better accuracy in comparison to existing state-of-the-art methods.

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Transitions between multiple dynamical states in a confined dense active-particle system

Cited by 13 publications

References 36 publications

Active binary mixtures of fast and slow hard spheres

Active binary mixtures of fast and slow hard spheres

Confined System Analysis of a Predator-Prey Minimalistic Model

Understanding Crowd Flow Movements Using Active-Langevin Model

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