Nonequilibrium Statistical Physics Research Articles

Specific heat ratio effects of compressible Rayleigh—Taylor instability studied by discrete Boltzmann method

Rayleigh-Taylor (RT) instability widely exists in nature and engineering fields. How to better understand the physical mechanism of RT instability is of great theoretical significance and practical value. At present, abundant results of RT instability have been obtained by traditional macroscopic methods. However, research on the thermodynamic non-equilibrium (TNE) effects in the process of system evolution is relatively scarce. In this paper, the discrete Boltzmann method based on non-equilibrium statistical physics is utilized to study the effects of the specific heat ratio on compressible RT instability. The evolution process of the compressible RT system with different specific heat ratios can be analyzed by the temperature gradient and the proportion of the non-equilibrium region. Firstly, as a result of the competition between the macroscopic magnitude gradient and the non-equilibrium region, the average TNE intensity first increases and then reduces, and it increases with the specific heat ratio decreasing; the specific heat ratio has the same effect on the global strength of the viscous stress tensor. Secondly, the moment when the total temperature gradient in y direction deviates from the fixed value can be regarded as a physical criterion for judging the formation of the vortex structure. Thirdly, under the competition between the temperature gradients and the contact area of the two fluids, the average intensity of the non-equilibrium quantity related to the heat flux shows diversity, and the influence of the specific heat ratio is also quite remarkable.

Fluctuations of water quality time series in rivers follow superstatistics

SummarySuperstatistics is a general method from nonequilibrium statistical physics which has been applied to a variety of complex systems, ranging from hydrodynamic turbulence to traffic delays and air pollution dynamics. Here, we investigate water quality time series (such as dissolved oxygen concentrations and electrical conductivity) as measured in rivers and provide evidence that they exhibit superstatistical behavior. Our main example is time series as recorded in the River Chess in South East England. Specifically, we use seasonal detrending and empirical mode decomposition to separate trends from fluctuations for the measured data. With either detrending method, we observe heavy-tailed fluctuation distributions, which are well described by log-normal superstatistics for dissolved oxygen. Contrarily, we find a double peaked non-standard superstatistics for the electrical conductivity data, which we model using two combined -distributions.

Optimization of an active heat engine.

Optimization of heat engines at the microscale has applications in biological and artificial nanotechnology and stimulates theoretical research in nonequilibrium statistical physics. Here we consider noninteracting overdamped particles confined by an external harmonic potential, in contact with either a thermal reservoir or a stochastic self-propulsion force (active Ornstein-Uhlenbeck model). A cyclical machine is produced by periodic variation of the parameters of the potential and of the noise. An exact mapping between the passive and the active model allows us to define the effective temperature T_{eff}(t), which is meaningful for the thermodynamic performance of the engine. We show that T_{eff}(t) is different from all other known active temperatures, typically used in static situations. The mapping allows us to optimize the active engine, regardless of the values of the persistence time or self-propulsion velocity. In particular, through linear irreversible thermodynamics (small amplitude of the cycle), we give an explicit formula for the optimal cycle period and phase delay (between the two modulated parameters, stiffness and temperature) achieving maximum power with Curzon-Ahlborn efficiency. In the quasistatic limit, the formula for T_{eff}(t) simplifies and coincides with a recently proposed temperature for stochastic thermodynamics, bearing a compact expression for the maximum efficiency. A point, which has been overlooked in recent literature, is made about the difficulty in defining efficiency without a consistent definition of effective temperature.

Random Fields in Physics, Biology and Data Science

A random field is the representation of the joint probability distribution for a set of random variables. Markov fields, in particular, have a long standing tradition as the theoretical foundation of many applications in statistical physics and probability. For strictly positive probability densities, a Markov random field is also a Gibbs field, i.e., a random field supplemented with a measure that implies the existence of a regular conditional distribution. Markov random fields have been used in statistical physics, dating back as far as the Ehrenfests. However, their measure theoretical foundations were developed much later by Dobruschin, Lanford and Ruelle, as well as by Hammersley and Clifford. Aside from its enormous theoretical relevance, due to its generality and simplicity, Markov random fields have been used in a broad range of applications in equilibrium and non-equilibrium statistical physics, in non-linear dynamics and ergodic theory. Also in computational molecular biology, ecology, structural biology, computer vision, control theory, complex networks and data science, to name but a few. Often these applications have been inspired by the original statistical physics approaches. Here, we will briefly present a modern introduction to the theory of random fields, later we will explore and discuss some of the recent applications of random fields in physics, biology and data science. Our aim is to highlight the relevance of this powerful theoretical aspect of statistical physics and its relation to the broad success of its many interdisciplinary applications.

Symmetries and effect of time dimension in non-equilibrium quantum matter

Non-equilibrium quantum many-body systems have attracted considerable attention in the past decades. The scope of the research of this kind of novel system involves interdisciplinary research of condensed matter, atomic and molecular physics, quantum optics, quantum information and quantum computation, as well as the non-equilibrium statistical physics. The non-equilibrium phenomena emerging from the aforementioned quantum systems can exhibit rich and universal behaviors, which have far from being well understood due to the novelties and complexities of these systems, and hence the quantum many-body physics becomes the research highlight. At the same time, with the rapid development of quantum techniques, the understanding of these complex systems is of important practical significance due to their potential applications in quantum computation and quantum manipulation. In this paper, we show our recent progress of non-equilibrium quantum many-body systems. We focus on the novel phenomena closely related to the temporary symmetry breaking, including the exotic quantum matter, quasi-particles as well as the dynamical universality classes in non-equilibrium quantum many-body systems.

Nonequilibrium thermodynamics of turbulence and stochastic fluid systems

Fluid systems are found in the Universe at various scales. Turbulence as a complex form of fluid motion far from thermodynamic equilibrium remains one of the most challenging problems in physics. In this work, we study the nonequilibrium thermodynamics of stochastic fluid systems in general and turbulence in particular. Our approach is based on a reinterpretation of the stochastic fluid system as an interacting many-body system in contact with multiple heat baths. A set of nonequilibrium thermodynamic equations for general stochastic fluid systems, applicable to turbulence in the far-from-equilibrium regime, is constructed using the potential landscape and flux field theory. In addition to the energy and entropy balance equations that represent the first and second laws of thermodynamics, a new thermodynamic equation is found to be crucial for relating the first law with the second law and connecting violation of detailed balance to entropy flow and entropy production at the steady state. It is demonstrated that steady-state entropy production and energy flow are manifestations of the nonequilibrium irreversible nature of fluid systems characterized by the nonequilibrium trinity construct that originates from temperature nonuniformity. We propose an intuitive thermodynamic picture of the turbulence energy cascade process as heat conduction in the scale domain, where energy flow across scales is conducted by nonlinear convection and driven by the temperature difference between the large and small scales. Nonequilibrium irreversibility of turbulence energy cascade is quantified by the steady-state entropy production rate. This work is rooted in both fluid dynamics and nonequilibrium statistical physics, fostering a deeper level of communication between these fields. Further extensions of this work have the potential to grow into a more complete nonequilibrium statistical theory, with a much wider range of applications encompassing general physical, chemical and biological nonequilibrium systems.

Invariance Principle for the Random Lorentz Gas\u2014Beyond the Boltzmann-Grad Limit

We prove the invariance principle for a random Lorentz-gas particle in 3 dimensions under the Boltzmann-Grad limit and simultaneous diffusive scaling. That is, for the trajectory of a point-like particle moving among infinite-mass, hard-core, spherical scatterers of radius r, placed according to a Poisson point process of density varrho , in the limit varrho rightarrow infty , rrightarrow 0, varrho r^{2}rightarrow 1 up to time scales of order T=o(r^{-2}left| {log r}right| ^{-2}). To our knowledge this represents the first significant progress towards solving rigorously this problem in classical nonequilibrium statistical physics, since the groundbreaking work of Gallavotti (Phys Rev 185:308–322, 1969, Nota Interna Univ di Roma 358, 1970, Statistical mechanics. A short treatise. Theoretical and mathematical physics series, Springer, Berlin, 1999), Spohn (Commun Math Phys 60:277–290, 1978, Rev Mod Phys 52:569–611, 1980) and Boldrighini–Bunimovich–Sinai (J Stat Phys 32:477–501, 1983). The novelty is that the diffusive scaling of particle trajectory and the kinetic (Boltzmann-Grad) limit are taken simultaneously. The main ingredients are a coupling of the mechanical trajectory with the Markovian random flight process, and probabilistic and geometric controls on the efficiency of this coupling. Similar results have been earlier obtained for the weak coupling limit of classical and quantum random Lorentz gas, by Komorowski–Ryzhik (Commun Math Phys 263:277–323, 2006), respectively, Erdős–Salmhofer–Yau (Acta Math 200:211–277, 2008, Commun Math Phys 271:1–53, 2007). However, the following are substantial differences between our work and these ones: (1) The physical setting is different: low density rather than weak coupling. (2) The method of approach is different: probabilistic coupling rather than analytic/perturbative. (3) Due to (2), the time scale of validity of our diffusive approximation—expressed in terms of the kinetic time scale—is much longer and fully explicit.

Monitoring Lévy-process crossovers

The crossover among two or more types of diffusive processes represents a vibrant theme in nonequilibrium statistical physics. In this work we propose two models to generate crossovers among different Lévy processes: in the first model we change gradually the order of the derivative in the Laplacian term of the diffusion equation, whereas in the second one we consider a combination of fractional-derivative diffusive terms characterized by coefficients that change in time. The proposals are illustrated by considering semi-analytical (i.e., analytical together with numerical) procedures to follow the time-dependent solutions. We find changes between two different regimes and it is shown that, far from the crossover regime, both models yield qualitatively similar results, although these changes may occur in different forms for the two models. The models introduced herein are expected to be useful for describing crossovers among distinct diffusive regimes that occur frequently in complex systems.

Determination of the steady-state kinetics in a spin-1 Ising model using path probability method

As a continuation of our previously published work, we propose a theoretical framework for the determination of steady-state kinetics in a spin-1 Ising model by the path probability method. The framework is based on the principles of non-equilibrium statistical physics and is quite different from the phenomenological approach. We construct a set of linear kinetic equations for the order parameters using the non-linear dynamic (or rate) equations in the presence of external magnetic field. From the steady-state solutions of the linear equations, an expression for the complex (or dynamic) magnetic susceptibility [Formula: see text] is derived. The temperature dependence of the magnetic dispersion relation [Formula: see text] and magnetic absorption factor [Formula: see text] has been studied in the ferromagnetic (FM) and paramagnetic (PM) phases as well as near the critical regime.

Quantifying configurational information for a stochastic particle in a flow-field

Flow-fields are ubiquitous systems that are able to transport vital signaling molecules necessary for system function. While information regarding the location and transport of such particles is often crucial, it is not well-understood how to quantify the information in such stochastic systems. Using the framework of nonequilibrium statistical physics, we develop theoretical tools to address this question. We observe that rotation in a flow-field does not explicitly appear in the generalized potential that governs the rate of system entropy production. Specifically, in the neighborhood of a flow-field, rotation contributes to the information content only in the presence of strain—and then with a comparatively weaker contribution than strain and at higher orders in time. Indeed, strain and especially the flow divergence, contribute most strongly to transport properties such as particle residence time and the rate of information change. These results shed light on how information can be analyzed and controlled in complex artificial and living flow-based systems.

The physics of climate variability and climate change

The climate system is a forced, dissipative, nonlinear, complex and heterogeneous system that is out of thermodynamic equilibrium. The system exhibits natural variability on many scales of motion, in time as well as space, and it is subject to various external forcings, natural as well as anthropogenic. This paper reviews the observational evidence on climate phenomena and the governing equations of planetary-scale flow, as well as presenting the key concept of a hierarchy of models as used in the climate sciences. Recent advances in the application of dynamical systems theory, on the one hand, and of nonequilibrium statistical physics, on the other, are brought together for the first time and shown to complement each other in helping understand and predict the system's behavior. These complementary points of view permit a self-consistent handling of subgrid-scale phenomena as stochastic processes, as well as a unified handling of natural climate variability and forced climate change, along with a treatment of the crucial issues of climate sensitivity, response, and predictability.

Government Intervention Modeling in Microeconomic Company Market Evolution

Modern technology and innovations are becoming more crucial than ever for the survival of companies in the market. Therefore, it is significant both from theoretical and practical points of view to understand how governments can influence technology growth and innovation diffusion (TGID) processes. We propose a simple but essential extension of Ausloos-Clippe-P\c{e}kalski and related Cichy numerical models of the TGID in the market. Both models are inspired by the nonlinear non-equilibrium statistical physics. Our extension involves a parameter describing the probability of government intervention in the TGID process in the company market. We show, using Monte Carlo simulations, the effects interventionism can have on the companies' market, depending on the segment of firms that are supported. The high intervention probability can result, paradoxically, in the destabilization of the market development. It lowers the market's technology level in the long-time limit compared to markets with a lower intervention parameter. We found that the intervention in the technologically weak and strong segments of the company market does not substantially influence the market dynamics, compared to the intervention helping the middle-level companies. However, this is still a simple model which can be extended further and made more realistic by including other factors. Namely, the cost and risk of innovation or limited government resources and capabilities to support companies.

Fluctuations in Bacterial Suspensions Driven by Chemotaxis

Stochastic fluctuations in living systems create a spectacular variety of unique states far from equilibrium, which however remains largely unexplored regarding its underlying physics, especially when considering their instinctive responses to environmental stimuli. Here, by utilizing a molecular-dynamics model of bacterial chemotaxis, we present an investigation of tracer statistics in suspensions of chemotactic bacteria. Unlike Brownian motion in conventional media, the tracer particle performs a short-time ballistic but long-time Fickian diffusion with non-Gaussian dynamics. A phenomenological extension of Langevin equation accounts for the observed anomalous behaviors. Moreover, a violation of the Stokes-Einstein relation is identified regarding the size-dependence of particle diffusivity in bacterial suspensions. Our findings uncover the physical nature of stimulus-driven fluctuation statistics in bacterial fluids, and suggest a theoretical framework to deepen the understanding of the nonequilibrium statistical physics of active matter under external stimuli.

Cluster dynamics in the open-boundary heterogeneous ASEPs coupled with interacting energies

Cluster dynamics possess the promising studies in statistical physics and complexity science. While proposing physical models, applying analytical methods and exploring macroscopic properties and microscopic interactions are critical to investigate physical mechanisms of cluster dynamics. Among these physical models, totally asymmetric simple exclusion process is an important non-equilibrium statistical physics model, as it is essential enough to lattice models. Besides, it is also abbreviated as TASEP. Additionally, asymmetric simple exclusion processes, ASEPs, are derivatives, whose dynamics are based on TASEP with other nonlinear physical processes. Actually, heterogeneity of ASEPs is available to depict the real physical and engineering phenomena in a more reasonable manner. Different with previous researches, connected ASEPs are proposed here, which are constituted by two isolated ASEP coupled with a conjunction. As for each individual ASEP, its dynamics are dominated by interacting energies. Different with previous work, two completely different energies are introduced in system, which are used to describe the competition between subsystems. By analyzing complete transition processes among different particle configurations, mean-field dynamical equations are established. Phase diagrams under different energies are obtained, which yield to findings of coexistence phase of low densities, coexistence phase of high densities and coexistence phase of low and high densities. These local densities are qualitatively different. Hereafter, evolution regularities of phase diagrams obtained from local densities and currents are obtained to deeply understand physical mechanisms of complexity science. Expanded results and discussions about potential applications and fewer basic results are also presented. Expanded model with different Langmuir kinetics and internal interacting energies is established. Governing partially differential equations of the whole system are derived. Four steady stacking states of kinesin-II and OSM-3 on bovine brain tubulins affected by two different kinds of buffers are found. This work will help and promote the understanding of non-equilibrium statistical evolutions and nonlinear dynamic behaviors of such multibody system.

Pair-distribution function of active Brownian spheres in two spatial dimensions: Simulation results and analytic representation.

We investigate the full pair-distribution function of a homogeneous suspension of spherical active Brownian particles interacting by a Weeks-Chandler-Andersen potential in two spatial dimensions. The full pair-distribution function depends on three coordinates describing the relative positions and orientations of two particles, the Péclet number specifying the activity of the particles, and their mean packing density. This five-dimensional function is obtained from Brownian dynamics simulations. We discuss its structure taking into account all of its degrees of freedom. In addition, we present an approximate analytic expression for the product of the full pair-distribution function and the interparticle force. We find that the analytic expression, which is typically needed when deriving analytic models for the collective dynamics of active Brownian particles, is in good agreement with the simulation results. The results of this work can thus be expected to be helpful for the further theoretical investigation of active Brownian particles as well as nonequilibrium statistical physics in general.

Calculation of the Thermodynamic Equilibrium of a Multicomponent Two-Phase System Based on Minimization of the Gibbs Potential

The authors have proposed a method to model the thermodynamic equilibrium of the two-phase multicomponent system “solution–vapor” by solving a system of ordinary differential equations following from the principles of nonequilibrium thermodynamics and statistical physics. Calculation of the thermodynamics of liquid and vapor phases was based on the equation of state. During the solution of the system of ordinary differential equations, the authors determined the molar volumes of the solution and the vapor, the molar composition of the phases, and the pressures of the onset of boiling of the solution and condensation of the vapor. The proposed method is fundamentally different from the popular empirical Rachford–Rice method. Results of calculating with this method have been presented.

Norm violation versus punishment risk in a social model of corruption.

We analyze the onset of social-norm-violating behaviors when social punishment is present. To this aim, a compartmental model is introduced to illustrate the flows among the three possible states: honest, corrupt, and ostracism. With this simple model we attempt to capture some essential ingredients such as the contagion of corrupt behaviors to honest agents, the delation of corrupt individuals by honest ones, and the warning to wrongdoers (fear like that triggers the conversion of corrupt people into honesty). In nonequilibrium statistical physics terms, the former dynamics can be viewed as a non-Hamiltonian kinetic spin-1 Ising model. After developing in full detail its mean-field theory and comparing its predictions with simulations made on regular networks, we derive the conditions for the emergence of corrupt behaviors and, more importantly, illustrate the key role of the warning-to-wrongdoers mechanism in the latter.

Quantum non-demolition measurement of a many-body Hamiltonian

In an ideal quantum measurement, the wave function of a quantum system collapses to an eigenstate of the measured observable, and the corresponding eigenvalue determines the measurement outcome. If the observable commutes with the system Hamiltonian, repeated measurements yield the same result and thus minimally disturb the system. Seminal quantum optics experiments have achieved such quantum non-demolition (QND) measurements of systems with few degrees of freedom. In contrast, here we describe how the QND measurement of a complex many-body observable, the Hamiltonian of an interacting many-body system, can be implemented in a trapped-ion analog quantum simulator. Through a single-shot measurement, the many-body system is prepared in a narrow band of (highly excited) energy eigenstates, and potentially even a single eigenstate. Our QND scheme, which can be carried over to other platforms of quantum simulation, provides a framework to investigate experimentally fundamental aspects of equilibrium and non-equilibrium statistical physics including the eigenstate thermalization hypothesis and quantum fluctuation relations.

Thermodynamic efficiency in dissipative chemistry

Chemical processes in closed systems inevitably relax to equilibrium. Living systems avoid this fate and give rise to a much richer diversity of phenomena by operating under nonequilibrium conditions. Recent experiments in dissipative self-assembly also demonstrated that by opening reaction vessels and steering certain concentrations, an ocean of opportunities for artificial synthesis and energy storage emerges. To navigate it, thermodynamic notions of energy, work and dissipation must be established for these open chemical systems. Here, we do so by building upon recent theoretical advances in nonequilibrium statistical physics. As a central outcome, we show how to quantify the efficiency of such chemical operations and lay the foundation for performance analysis of any dissipative chemical process.

Validity of local thermal equilibrium in anomalous heat diffusion

Local thermal equilibrium (LTE) is a general presumption of many theoretical analyses in nonequilibrium statistical physics. It describes a situation that although the system is not in global thermal equilibrium, each small portion of the system may still be described approximately by the laws of thermal equilibrium. The validity of LTE has however seldom been investigated carefully. Here, by studying the ensemble velocity distribution and its spatial correlation, we present strong evidences for the lack of LTE in anomalous heat diffusion processes in one dimensional harmonic lattices and the Fermi–Pasta–Ulam-β lattices. In particular, clear nonzero excess kurtosis and long range correlations have been observed, with values scaling linearly with the initial temperature difference. Therefore near thermal equilibrium is not sufficient to achieve LTE. Some existing studies that are based on the existence of LTE should be revisited. Using the same methods, we also show that LTE is still valid in the ϕ4 lattice, in which heat diffuses following Fourier’s law.