A Kinetic View of Statistical Physics

Krapivsky, P. L.; Redner, S.; Ben‐Naim, E.

doi:10.1017/cbo9780511780516

Cited by 974 publications

(1,935 citation statements)

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“…The latter is determined by the homogeneity degree 8 and is practically insensitive to the detailed form of these coefficients (35,36). We use this property and replace the original rate coefficients [9] and [11] by the generalized product kernel…”

Section: [4]mentioning

confidence: 99%

Size distribution of particles in Saturn’s rings from aggregation and fragmentation

Brilliantov

Krapivsky

Bodrova

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

135

129

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Saturn's rings consist of a huge number of water ice particles, with a tiny addition of rocky material. They form a flat disk, as the result of an interplay of angular momentum conservation and the steady loss of energy in dissipative interparticle collisions. For particles in the size range from a few centimeters to a few meters, a power-law distribution of radii, ∼ r −q with q ≈ 3, has been inferred; for larger sizes, the distribution has a steep cutoff. It has been suggested that this size distribution may arise from a balance between aggregation and fragmentation of ring particles, yet neither the power-law dependence nor the upper size cutoff have been established on theoretical grounds. Here we propose a model for the particle size distribution that quantitatively explains the observations. In accordance with data, our model predicts the exponent q to be constrained to the interval 2.75 ≤ q ≤ 3.5. Also an exponential cutoff for larger particle sizes establishes naturally with the cutoff radius being set by the relative frequency of aggregating and disruptive collisions. This cutoff is much smaller than the typical scale of microstructures seen in Saturn's rings. (1-3) and the observation of rapid processes in the ring system (4) indicate that the shape of the particle size distribution is likely not primordial or a direct result of the ring-creating event. Rather, ring particles are believed to be involved in active accretion-destruction dynamics (5-13) and their sizes vary over a few orders of magnitude as a power law (14-17), with a sharp cutoff for large sizes (18-21). Moreover, tidal forces fail to explain the abrupt decay of the size distribution for house-sized particles (22). One wishes to understand the following: (i) Can the interplay between aggregation and fragmentation lead to the observed size distribution? And (ii) is this distribution peculiar for Saturn's rings, or is it universal for planetary rings in general? To answer these questions quantitatively, one needs to elaborate a detailed model of the kinetic processes in which the ring particles are involved. Here we develop a theory that quantitatively explains the observed properties of the particle size distribution and show that these properties are generic for a steady state, when a balance between aggregation and fragmentation holds. Our model is based on the hypothesis that collisions are binary and that they may be classified as aggregative, restitutive, or disruptive (including collisions with erosion); which type of collision is realized depends on the relative speed of colliding particles and their masses. We apply the kinetic theory of granular gases (23, 24) for the multicomponent system of ring particles to quantify the collision rate and the type of collision. Results and DiscussionModel. Ring particles may be treated as aggregates built up of primary grains (9) of a certain size r 1 and mass m 1 . [Observations indicate that particles below a certain radius are absent in dense rings (16).] Denote by m k = km 1 the mass of rin...

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Section: [4]mentioning

confidence: 99%

Size distribution of particles in Saturn’s rings from aggregation and fragmentation

Brilliantov

Krapivsky

Bodrova

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

135

129

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“…Taking the limit δt → 0 in 597 the formal solution, P (τ ) = [I + L] τ P (0) (I being the identity), leads then to generally, the kinetic point of view seems to be a promising and fruitful approach to 625 non-equilibrium systems (see e.g., [97]). …”

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confidence: 99%

Non-equilibrium statistical mechanics: from a paradigmatic model to biological transport

Chou¹,

2011

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Abstract. Unlike equilibrium statistical mechanics, with its well-established foundations, a similar widely-accepted framework for non-equilibrium statistical mechanics (NESM) remains elusive. Here, we review some of the many recent activities on NESM, focusing on some of the fundamental issues and general aspects. Using the language of stochastic Markov processes, we emphasize general properties of the evolution of configurational probabilities, as described by master equations. Of particular interest are systems in which the dynamics violate detailed balance, since such systems serve to model a wide variety of phenomena in nature. We next review two distinct approaches for investigating such problems. One approach focuses on models sufficiently simple to allow us to find exact, analytic, non-trivial results. We provide detailed mathematical analyses of a onedimensional continuous-time lattice gas, the totally asymmetric exclusion process (TASEP). It is regarded as a paradigmatic model for NESM, much like the role the Ising model played for equilibrium statistical mechanics. It is also the starting point for the second approach, which attempts to include more realistic ingredients in order to be more applicable to systems in nature. Restricting ourselves to the area of biophysics and cellular biology, we review a number of models that are relevant for transport phenomena. Successes and limitations of these simple models are also highlighted.

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“…We assume that each individual interacts with people living in her own location (family, friends, neighbors) with a probability α, while with probability 1 − α she does so with individuals of her work place. Once an individual interacts with others, its opinion is updated following a noisy voter model [6,7,8,9,10]: an interaction partner is chosen and the original agent copies her opinion imperfectly (with a certain probability of making mistakes). A more detailed description of the SIRM model can be found in [26].…”

Section: Model Definition and Analytical Descriptionmentioning

confidence: 99%

Persistence in Voting Behavior: Stronghold Dynamics in Elections

Pérez

Fernández-Gracia

Ramasco

et al. 2015

Social Computing, Behavioral-Cultural Modeling, and Prediction

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Abstract. Influence among individuals is at the core of collective social phenomena such as the dissemination of ideas, beliefs or behaviors, social learning and the diffusion of innovations. Different mechanisms have been proposed to implement inter-agent influence in social models from the voter model, to majority rules, to the Granoveter model. Here we advance in this direction by confronting the recently introduced Social Influence and Recurrent Mobility (SIRM) model, that reproduces generic features of vote-shares at different geographical levels, with data in the US presidential elections. Our approach incorporates spatial and population diversity as inputs for the opinion dynamics while individuals' mobility provides a proxy for social context, and peer imitation accounts for social influence. The model captures the observed stationary background fluctuations in the vote-shares across counties. We study the so-called political strongholds, i.e., locations where the votes-shares for a party are systematically higher than average. A quantitative definition of a stronghold by means of persistence in time of fluctuations in the voting spatial distribution is introduced, and results from the US Presidential Elections during the period 1980-2012 are analyzed within this framework. We compare electoral results with simulations obtained with the SIRM model finding a good agreement both in terms of the number and the location of strongholds. The strongholds duration is also systematically characterized in the SIRM model. The results compare well with the electoral results data revealing an exponential decay in the persistence of the strongholds with time.

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A Kinetic View of Statistical Physics

Cited by 974 publications

References 1 publication

Size distribution of particles in Saturn’s rings from aggregation and fragmentation

Size distribution of particles in Saturn’s rings from aggregation and fragmentation

Non-equilibrium statistical mechanics: from a paradigmatic model to biological transport

Persistence in Voting Behavior: Stronghold Dynamics in Elections

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