We use fast synchrotron x-ray microtomography to investigate the pore-scale dynamics of water injection in an oil-wet carbonate reservoir rock at subsurface conditions. We measure, in situ, the geometric contact angles to confirm the oil-wet nature of the rock and define the displacement contact angles using an energy-balance-based approach. We observe that the displacement of oil by water is a drainagelike process, where water advances as a connected front displacing oil in the center of the pores, confining the oil to wetting layers. The displacement is an invasion percolation process, where throats, the restrictions between pores, fill in order of size, with the largest available throats filled first. In our heterogeneous carbonate rock, the displacement is predominantly size controlled; wettability has a smaller effect, due to the wide range of pore and throat sizes, as well as largely oil-wet surfaces. Wettability only has an impact early in the displacement, where the less oil-wet pores fill by water first. We observe drainage associated pore-filling dynamics including Haines jumps and snap-off events. Haines jumps occur on single- and/or multiple-pore levels accompanied by the rearrangement of water in the pore space to allow the rapid filling. Snap-off events are observed both locally and distally and the capillary pressure of the trapped water ganglia is shown to reach a new capillary equilibrium state. We measure the curvature of the oil-water interface. We find that the total curvature, the sum of the curvatures in orthogonal directions, is negative, giving a negative capillary pressure, consistent with oil-wet conditions, where displacement occurs as the water pressure exceeds that of the oil. However, the product of the principal curvatures, the Gaussian curvature, is generally negative, meaning that water bulges into oil in one direction, while oil bulges into water in the other. A negative Gaussian curvature provides a topological quantification of the good connectivity of the phases throughout the displacement.

The success of direct laser-driven inertial confinement fusion (ICF) relies critically on the efficient coupling of laser light to plasma. At ignition scale, the absolute stimulated Raman scattering (SRS) instability can severely inhibit this coupling by redirecting and strongly depleting laser light. This article describes a new dynamic saturation regime of the absolute SRS instability near one-quarter of the critical density. The saturation occurs when spatiotemporal ion-acoustic fluctuations in the plasma density detune the instability resonance. The dynamic saturation mitigates the strong depletion of laser light and enhances its transmission through the instability region, explaining the coupling of laser light to ICF targets at higher plasma densities.

For a collisionless plasma in contact with a dielectric surface, where with unit probability electrons and ions are, respectively, absorbed and neutralized, thereby injecting electrons and holes into the conduction and valence bands, we study the kinetics of plasma loss by nonradiative electron-hole recombination inside the dielectric. We obtain a self-consistently embedded electric double layer, merging with the quasineutral, field-free regions inside the plasma and the solid. After a description of the numerical scheme for solving the two sets of Boltzmann equations, one for the electrons and ions of the plasma and one for the electrons and holes of the solid, to which this transport problem gives rise to, we present numerical results for a $p$-doped dielectric. Besides potential, density, and flux profiles, plasma-induced changes in the electron and hole distribution functions are discussed, from which a microscopic view on plasma loss inside the dielectric emerges.

A significant step has been made towards understanding the physics of the transient surface current triggered by ejected electrons during the interaction of a short intense laser pulse with a high-conductivity target. Unlike the commonly discussed hypothesis of neutralization current generation as a result of the fast loss of hot electrons to the vacuum, the proposed mechanism is associated with excitation of the fast current by electric polarization due to transition radiation triggered by ejected electrons. We present a corresponding theoretical model and compare it with two simulation models using the finite-difference time-domain and particle-in-cell methods. Distinctive features of the proposed theory are clearly manifested in both of these models.

Transport coefficients are of crucial importance in theoretical as well as experimental studies. Despite substantial research on classical hard sphere or disk gases in low- and high-density regimes, a thorough investigation of transport coefficients for massive relativistic systems is missing in the literature. In this work a fully relativistic molecular dynamics simulation is employed to numerically obtain the transport coefficients of a hard sphere relativistic gas based on Helfand-Einstein expressions. The numerical data are then used to check the accuracy of Chapmann-Enskog (CE) predictions in a wide range of temperature. The results indicate that while simulation data in low-temperature regime agrees very well with theoretical predictions, it begins to show deviations as temperature rises, except for the thermal conductivity which fits very well to CE theory in the whole range of temperature. Since our simulations are done in low density regimes, where CE approximation is expected to be valid, the observed deviations can be attributed to the inaccuracy of linear CE theory in extremely relativistic cases.

Self-sustained, elevated neuronal activity persisting on timescales of 10 s or longer is thought to be vital for aspects of working memory, including brain representations of real space. Continuous-attractor neural networks, one of the most well-known modeling frameworks for persistent activity, have been able to model crucial aspects of such spatial memory. These models tend to require highly structured or regular synaptic architectures. In contrast, we study numerical simulations of a geometrically embedded model with a local, but otherwise random, connectivity profile; imposing a global regulation of our system's mean firing rate produces localized, finely spaced discrete attractors that effectively span a two-dimensional manifold. We demonstrate how the set of attracting states can reliably encode a representation of the spatial locations at which the system receives external input, thereby accomplishing spatial memory via attractor dynamics without synaptic fine-tuning or regular structure. We then measure the network's storage capacity numerically and find that the statistics of retrievable positions are also equivalent to a full tiling of the plane, something hitherto achievable only with (approximately) translationally invariant synapses, and which may be of interest in modeling such biological phenomena as visuospatial working memory in two dimensions.

To study the effects of chunk mixing, the implosion experiments using capsules filled with deuterated foam (CDF capsule) were carried out on the Shenguang laser facility. Three types of the CDF capsules, namely the capsules without Au dopant, with micrometer Au dopant, and with atomic Au dopant, were used in the experiments. The neutron yields, the size, and the emission intensity of the hotspots were measured. The CDF capsules without Au dopant produced the highest neutron yield and the largest hotspot size at the time of peak emission. The capsules with micrometer or atomic Au dopant showed similar reduced neutron yield and hotspot size. The time-resolved hotspot emissions showed different behaviors between different capsules. One-dimensional simulations were carried out to understand the implosion dynamics of the CDF capsule without Au dopant, and to provide the thermodynamic conditions that the Au dopant would experience during the implosion. The effects of Au dopant were then discussed qualitatively.

We use the Fortuin-Kasteleyn representation-based improved estimator of the correlation configuration as an alternative to the ordinary correlation configuration in the machine-learning study of the phase classification of spin models. The phases of classical spin models are classified using the improved estimators, and the method is also applied to the quantum Monte Carlo simulation using the loop algorithm. We analyze the Berezinskii-Kosterlitz-Thouless (BKT) transition of the spin-1/2 quantum $XY$ model on the square lattice. We classify the BKT phase and the paramagnetic phase of the quantum $XY$ model using the machine-learning approach. We show that the classification of the quantum $XY$ model can be performed by using the training data of the classical $XY$ model.

Search processes with stochastic resetting provide a general theoretical framework for understanding a wide range of naturally occurring phenomena. Most current models focus on the first-passage-time problem of finding a single target in a given search domain. Here we use a renewal method to derive general expressions for the splitting probabilities and conditional mean first passage times (MFPTs) in the case of multiple targets. Our analysis also incorporates the effects of delays arising from finite return times and refractory periods. Carrying out a small-$r$ expansion, where $r$ is the mean resetting rate, we obtain general conditions for when resetting increases the splitting probability or reduces the conditional MFPT to a particular target. This also depends on whether ${\pi }_{\mathrm{tot}}=1$ or ${\pi }_{\mathrm{tot}}<1$, where ${\pi }_{\mathrm{tot}}$ is the probability that the particle is eventually absorbed by one of the targets in the absence of resetting. We illustrate the theory by considering two distinct examples. The first consists of an actin-rich cell filament (cytoneme) searching along a one-dimensional array of target cells, a problem for which the splitting probabilities and MFPTs can be calculated explicitly. In particular, we highlight how the resetting rate plays an important role in shaping the distribution of splitting probabilities along the array. The second example involves a search process in a three-dimensional bounded domain containing a set of $N$ small interior targets. We use matched asymptotics and Green's functions to determine the behavior of the splitting probabilities and MFPTs in the small-$r$ regime. In particular, we show that the splitting probabilities and MFPTs depend on the “shape capacitance” of the targets.

We propose an alternative method of estimating the mean diameter and dispersion of clusters of particles, formed in a cooling gas, right after the nucleation stage. Using a moment model developed by Friedlander [S. K. Friedlander, Ann. N. Y. Acad. Sci. 404, 354 (1983)], we derive an analytic relationship for both cluster mean diameter and diameter dispersion as a function of two of the characteristic times of the system: the cooling time and the primary constituents collision time. These formulas can be used to predict diameter and dispersion variation with process parameters, such as the initial primary constituents' concentration or cooling rate. It is also possible to use them as an input to the coagulation stage, without the need to compute complex cluster generation during the nucleation burst. We compared our results with a nodal code (NGDE) and got excellent agreement.

We propose Möbius maps as a tool to model synchronization phenomena in coupled phase oscillators. Not only does the map provide fast computation of phase synchronization, it also reflects the underlying group structure of the sinusoidally coupled continuous phase dynamics. We study map versions of various known continuous-time collective dynamics, such as the synchronization transition in the Kuramoto-Sakaguchi model of nonidentical oscillators, chimeras in two coupled populations of identical phase oscillators, and Kuramoto-Battogtokh chimeras on a ring, and demonstrate similarities and differences between the iterated map models and their known continuous-time counterparts.

We study the pedestrian motion along a corridor in a nonpanic regime, as usually happens in evacuation scenarios in, e.g., schools, hospitals, or airports, by means of Monte Carlo simulations. We present a model, a combination of the well-known social force model (SFM) and Vicsek model (VM), that takes into account both model interactions, based on the relative position (SFM) and based on the velocity of the particles with some randomness (modulated by an external control parameter, the noise $\eta$, VM), respectively. To clarify the influence of the model ingredients we have compared simulations using (a) the pure Vicsek model (VM) with two boundary conditions (periodic and bouncing back) and with or without desired direction of motion, (b) the social force model (SFM), and (c) the model $(\mathrm{SFM}+\mathrm{VM})$. The study of steady-state particle configurations in the VM with confined geometry shows the expected bands perpendicular to the motion direction, while in the SFM and $\mathrm{SFM}+\mathrm{VM}$ particles order in stripes of a given width $w$ along the direction of motion. The results in the $\mathrm{SFM}+\mathrm{VM}$ case show that $w\left(t\right)\simeq t^{\alpha }$ has a diffusivelike behavior at low noise $\eta$ (dynamic exponent $\alpha \approx 1/2$), while it is subdiffusive at high values of external noise $(\alpha <1/2)$. We observe the well-known order-disorder transition in the VM with both boundary conditions, but the application of a desired direction condition inhibits the existence of disorder as expected. Similar behavior is observed in the SFM case. For the $\mathrm{SFM}+\mathrm{VM}$ case we find a susceptibility maximum which slowly increases with system size as a function of noise strength. This might be indicative of a order-disorder transition in the range of densities $\left(\rho \epsilon \right[\frac{1}{12},\frac{1}{9}\left]\right)$ and speeds $\left(v_0\epsilon [0.5,2]\right)$ studied.

A network consisting of excitatory and inhibitory (EI) neurons is a canonical model for understanding local cortical network activity. In this study, we extended the local circuit model and investigated how its dynamical landscape can be enriched when it interacts with another excitatory (E) population with long transmission delays. Through analysis of a rate model and numerical simulations of a corresponding network of spiking neurons, we studied the transition from stationary to oscillatory states by analyzing the Hopf bifurcation structure in terms of two network parameters: (1) transmission delay between the EI subnetwork and the E population and (2) inhibitory couplings that induced oscillatory activity in the EI subnetwork. We found that the critical coupling strength can strongly modulate as a function of transmission delay, and consequently the stationary state can be interwoven intricately with the oscillatory state. Such a dynamical landscape gave rise to an isolated stationary state surrounded by multiple oscillatory states that generated different frequency modes, and cross-frequency coupling developed naturally at the bifurcation points. We identified the network motifs with short- and long-range inhibitory connections that underlie the emergence of oscillatory states with multiple frequencies. Thus, we provided a mechanistic explanation of how the transmission delay to and from the additional E population altered the dynamical landscape. In summary, our results demonstrated the potential role of long-range connections in shaping the network activity of local cortical circuits.

We study the interfacial evolution of immiscible two-phase flow within a capillary tube in the partial wetting regime using direct numerical simulation. We investigate the flow patterns resulting from the displacement of a more viscous fluid by a less viscous one under a wide range of wettability conditions. We find that beyond a wettability dependent critical capillary number, a uniform displacement by a less viscous fluid can transition into a growing finger that eventually breaks up into discrete blobs by a series of pinch-off events for both wetting and nonwetting contact angles. This study validates previous experimental observations of pinch-off for wetting contact angles and extends those to nonwetting contact angles. We find that the blob length increases with the capillary number. We observe that the time between consecutive pinch-off events decreases with the capillary number and is greater for more wetting conditions in the displaced phase. We further show that the blob separation distance as a function of the difference between the inlet velocity and the contact line speed collapses into two monotonically decreasing curves for wetting and nonwetting contact angles. For the phase separation in the form of pinch-off, this work provides a quantitative study of the emerging length and timescales and their dependence on the wettability conditions, capillary effects, and viscous forces.

Conservative phase-field (CPF) equations based on the Allen-Cahn model for interface tracking in multiphase flows have become more popular in recent years, especially in the lattice-Boltzmann (LB) community. This is largely due to their simplicity and improved efficiency and accuracy over their Cahn-Hilliard–based counterparts. Additionally, the improved locality of the resulting LB equation (LBE) for the CPF models makes them more ideal candidates for LB simulation of multiphase flows on nonuniform grids, particularly within an adaptive-mesh refinement framework and massively parallel implementation. In this regard, some modifications—intended as improvements—have been made to the original CPF-LBE proposed by Geier et al. [Phys. Rev. E 91, 063309 (2015)] which require further examination. The goal of the present study is to conduct a comparative investigation into the differences between the original CPF model proposed by Geier et al. [Phys. Rev. E 91, 063309 (2015)] and the so-called improvements proposed by Ren et al. [Phys. Rev. E 94, 023311 (2016)] and Wang et al. [Phys. Rev. E 94, 033304 (2016)]. Using the Chapman-Enskog analysis, we provide a detailed derivation of the governing equations in each model and then examine the efficacy of the above-mentioned models for some benchmark problems. Several test cases have been designed to study different configurations ranging from basic yet informative flows to more complex flow fields, and the results are compared with finite-difference simulations. Furthermore, as a development of the previously proposed CPF-LBE model, axisymmetric formulations for the proposed model by Geier et al. [Phys. Rev. E 91, 063309 (2015)] are derived and presented. Finally, two benchmark problems are designed to compare the proposed axisymmetric model with the analytical solution and previous work. We find that the accuracy of the model for interface tracking is roughly similar for different models at high viscosity ratios, high density ratios, and relatively high Reynolds numbers, while the original CFP-LBE without the additional time-dependent terms outperforms the so-called improved models in terms of efficiency, particularly on distributed parallel machines.

A theoretical model is developed to characterize spatiotemporal mode locking (ML) in quadratic nonlinear media. The model is based on the two-dimensional nonlinear Schrödinger equation with coupling to a mean term (NLSM) and constructed as an extension of the master mode-locking model. It is numerically demonstrated that there exists steady-state soliton solutions of the ML-NLSM model that are astigmatic in nature. A full stability analysis and bifurcation study is performed for the ML-NLSM model, and it is manifest that spatiotemporal ML of the astigmatic steady-state solutions is possible in quadratic nonlinear media.

Much recent research has shown that network structure and human mobility have great influences on epidemic spreading. In this paper, we propose a discrete-time Markov chain method to model susceptible-infected-susceptible epidemic dynamics in heterogeneous networks. There are two types of locations, residences and common places, for which different infection mechanisms are adopted. We also give theoretical results about the impacts of important factors, such as mobility probability and isolation, on epidemic threshold. Numerical simulations are conducted, and experimental results support our analysis. In addition, we find that the dominations of different types of residences might reverse when mobility probability varies for some networks. In summary, the findings are helpful for policy making to prevent the spreading of epidemics.

The dielectric properties of confined water is of fundamental interest and is still controversial. For water confined in channels with height smaller than $h=8\AA$, we found a commensurability effect and an extraordinary decrease in the out-of-plane dielectric constant down to the limit of the dielectric constant of optical water. Spatial resolved polarization density data obtained from molecular dynamics simulations are found to be antisymmetric across the channel and are used as input in a mean-field model for the dielectric constant as a function of the height of the channel for $h>15\AA$. Our results are in excellent agreement with a recent experiment [L. Fumagalli et al., Science 360, 1339 (2018)].

We examine coherent vortices appearing as a result of the inverse cascade of two-dimensional turbulence in a finite box in the case of pumping with arbitrary correlation time in the quasilinear regime. We demonstrate that the existence of the vortices depends on the ratio between the values of the bottom friction coefficient $\alpha$ and the viscous damping of the flow fluctuations at the pumping scale $\nu k_f^2$ ($\nu$ is the kinematic viscosity coefficient and $k_f$ is the characteristic wave vector at the pumping scale). The coherent vortices appear if $\nu k_f^2\gg \alpha$ and cannot exist if $\nu k_f^2\ll \alpha$. Therefore there is a border value $\alpha \sim \nu k_f^2$ separating the regions. In numerical simulations, $\nu k_f^2/\alpha$ can be arbitrary, whereas in a laboratory experiment $\nu k_f^2/\alpha \lesssim 1$ and the coherent vortices can be observed solely near the border value of $\nu k_f^2/\alpha$.