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Jamming transitions and the rheology of granular avalanches in fluids are investigated using experiments and numerical simulations. Simulations use the lattice-Boltzmann method coupled with the discrete element method, providing detailed stress and deformation data. Both simulations and experiments present a perfect match with each other in carefully conducted deposition experiments, validating the simulation method. We analyze transient rheological laws and jamming transitions using our recently introduced length-scale ratio $G$. $G$ serves as a unified metric for the pressure and shear rate capturing the dynamics of sheared fluid-granular systems. Two key transition points, $G_{Y}$ and $G_{0}$, categorize the material's state into solid-like, creeping, and fluid-like states. Yielding at $G_{Y}$ marks the transition from solid-like to creeping, while $G_{0}$ signifies the shift to the fluid-like state. The $\mu-G$ relationship converges towards the equilibrium $\mu_{eq}(G)$ after $G>G_0$ showing the critical point where the established rheological laws for steady states apply during transient conditions.

Based on the Topographic Wetness Index (TWI), we studied the percolation process of water on the Earth's land surface and discovered a universal discontinuous phase transition across scales, with a critical TWI threshold of 0.671 (0.054). The discontinuity is attributed to the long-range correlation and directionality of the percolation process. Furthermore, the criticality is shown to extend from the critical point to a region corresponding to the Griffiths phase, where natural lake systems are found to develop, indicating the governess of self-organized criticality within the Earth system.

Urban land growth presents a major sustainability challenge, yet its growth patterns and dynamics remain unclear. We quantified urban land evolution by analyzing its statistical distribution in 14 regions and countries over 29 years. The results show a converging temporal trend in urban land expansion from sub-country to global scales, characterized by a coherent shift of urban area distributions from initial power law to exponential distributions, with the consequences of reduced system stability and resilience, and increased exposure of urban populations to extreme heat and air pollution. These changes are attributed to the increased influence from external economies of scale associated with globalization and are predicted to intensify in the future. The findings will advance urban science and direct current land urbanization practices toward sustainable development, especially in developing regions and medium-size cities.

Granular column collapse is a simple but important problem to the granular material community, due to its links to dynamics of natural hazards, such as landslides and pyroclastic flows, and many industrial situations, as well as its potential of analyzing transient and non-local rheology of granular flows. This article proposes a new dimensionless number to describe the run-out behaviour of granular columns on inclined planes based on both previous experimental data and dimensional analysis. With the assistance of the sphero-polyhedral discrete element method (DEM), we simulate inclined granular column collapses with different initial aspect ratios, inter-particle frictions, and initial solid fractions on inclined planes with different inclination angles (2.5$^{\circ}$ - 20.0$^{\circ}$) to verify the proposed dimensional analysis. Detailed analyses are further provided for better understanding of the influence of different initial conditions and boundary conditions, and to help unify the description of the relative run-out distances of systems with different inclination angles. This work determines the similarity and unity between granular column collapses on inclined planes and those on horizontal planes, and helps investigate the transient rheological behaviour of granular flows, which has direct relevance to various natural and engineering systems.

In this letter, following an extensive experimental validation, we perform constant-volume shearing simulations of non-Brownian granular suspensions using the discrete element method coupled with the lattice Boltzmann method. We choose a wide range of solid fractions, shear rates, fluid viscosities, particle sizes, and inter-particle frictional coefficients to obtain a scaling solution for the viscous behavior of suspensions in both dilute and dense regimes. This letter demonstrates that, with a proposed dilute-dense transitional solid fraction, $\phi_d$, there exists a strong correlation between the inverse relative viscosity and the shear stress. This work incorporates both the $\phi$-dependence and the $\dot{\gamma}$-dependence of suspension viscosity in a universal framework, which provides a scaling solution for granular suspensions across dilute and dense regimes and sheds light on the dilute-dense transition mechanisms.

Power law size distribution is found to associate with fractal, self-organized behaviors and patterns of complex systems. Such distribution also emerges from natural lakes, with potentially important links to the dynamics of lake systems. But the driving mechanism that generates and shapes this feature in lake systems remains unclear. Moreover, the power law itself was found inadequate for fully describing the size distribution of lakes, due to deviations at the two ends of size range. Based on observed and simulated lakes in 11 hydro-climatic zones of China, we established a conceptual model for lake systems, which covers the whole size range of lake size distribution and reveals the underlying driving mechanism. The full lake size distribution is composed of three components, with three phases featured by exponential, stretched-exponential and power law distribution. The three phases represent system states with successively increasing degrees of heterogeneity and orderliness, and more importantly, indicate the dominance of exogenic and endogenic forces, respectively. As the dominant driving force changes from endogenic to exogenic, a phase transition occurs with lake size distribution shifted from power law to stretched-exponential and further to exponential distribution. Apart from compressing the power law phase, exogenic force also increases its scaling exponent, driving the corresponding lake size power spectrum into the regime of blue noise. During this process, the autocorrelation function of the lake system diverges with a possibility of going to infinity, indicating the loss of system resilience.

We construct accurate emulators for the projected and redshift space galaxy correlation functions and excess surface density as measured by galaxy-galaxy lensing, based on Halo Occupation Distribution (HOD) modeling. Using the complete Mira-Titan suite of 111 $N$-body simulations, our emulators vary over eight cosmological parameters and include the effects of neutrino mass and dynamical dark energy. We demonstrate that our emulators are sufficiently accurate for the analysis of the BOSS DR12 CMASS galaxy sample over the range 0.5 < r < 50 Mpc/h. Furthermore, we show that our emulators are capable of recovering unbiased cosmological constraints from realistic mock catalogs over the same range. Our mock catalog tests show the efficacy of combining small scale galaxy-galaxy lensing with redshift space clustering and that we can constrain the growth rate and \sigma_8 to 7% and 4.5% respectively for a CMASS-like sample using only the measurements covered by our emulator. With the inclusion of a CMB prior on H_0, this reduces to a 2% measurement on the growth rate., Comment: 31 pages, 13 figures, revised to match published version

The mean stress tensor is essential to investigate the dynamics of granular material. In this paper, we use Hamilton's principle of least action to derive the averaged stress tensor of discrete granular assemblies subjected to hydraulic force fields, as well as rigorous conditions for a proper definition of the Representative Volume Element (RVE). The main goal behind our efforts is to upscale particle physics into a sound stress tensor for systems involving the complex interaction between grains and water. We identify the contributions from the unbalanced forces, hydraulic forces, gravity, external forces, and particle fluctuation to the mean stress tensor. In doing so, it is convenient to separate the influence of different force fields when the granular system is subjected to complex environments, e.g., subaqueous conditions. The obtained formula is then validated by triaxial test simulations of dry and saturated granular systems using the Discrete Element Method (DEM) and the Lattice-Boltzmann Method (LBM). The results show that the deduced formula can accurately calculate the stress tensor of discrete assemblies with various body-force fields. We used validated DEM-LBM simulations of submerged granular column collapses to explore the physics happening at the grain scale with this mathematical formalism and showcase its potential. We provide a new perspective based on the granular assembly scale to pursue the fluid-solid interaction. Due to the importance of stress analysis in the constitutive modelling of granular materials, this work could help to better obtain the stress-strain relationship of saturated or submerged granular systems.

In this communication, we present a new lengthscale-based rheology for dense sheared particle suspensions as they transition from inertial- to viscous-dominated. We derive a lengthscale ratio using straightforward physics-based considerations for a particle subjected to pressure and drag forces. In doing so, we demonstrate that an appropriately chosen length-scale ratio intrinsically provides a consistent relationship between normal stress and system proximity to its ''jammed'' or solid-like state, even as a system transitions between inertial and viscous states, captured by a variable Stokes number.

The behavior of granular column collapses is associated with the dynamics of geohazards, such as debris flows, landslides, and pyroclastic flows, yet its underlying physics is still not well understood. In this paper, we explore granular column collapses using the spheropolyhedral discrete element method (DEM), where the system contains two types of particles with different frictional properties. We impose three different mixing ratios and multiple different particle frictional coefficients, which lead to different run-out distances and deposition heights. Based on our previous work and a simple mixture theory, we propose a new effective initial aspect ratio for the bi-frictional granular mixture, which helps unify the description of the relative run-out distances. We analyze the kinematics of bi-frictional granular column collapses and find that deviations from classical power-law scaling in both the dimensionless terminal time and the dimensionless time when the system reaches the maximum kinetic energy may result from differences in the initial solid fraction and initial structures. To clarify the influence of initial states, we further decrease the initial solid fraction of granular column collapses, and propose a trial function to quantitatively describe its influence. Due to the utilization of a simple mixture theory of contact occurrence probability, this study can be associated with the friction-dependent rheology of granular systems and friction-induced granular segregations, and further generalized into applications with multiple species of particles in various natural and engineering mixtures.

We investigate granular column collapses with different column cross-sections and associate the cross-section shape influence with a finite-size analysis. Previous research, confined to initially circular configurations, reviewed the importance of granular column collapse studies and concluded that the run-out distance scales with the initial aspect ratio. In this work, granular columns with three different types of initial column cross-sections (square, equilateral triangular, and rectangular cross-sections) are simulated using the discrete element method (DEM). We explore how non-circular cross-sections lead to different run-out distances. Based on the previously obtained finite-size analysis, we further link the initial radius in different directions to the relative size of a column and perform the finite-size analysis to explain the cross-section influence. In the end, a universal relationship, which includes frictional properties, relative sizes, and the cross-section influence, is proposed. Our results are shown to have direct relevance to various natural and engineering systems.

Understanding the rheology of granular assemblies is important for natural and engineering systems, but the relationship between inter-particle friction (or microscopic friction) and macroscopic friction is still not well understood. In this study, using the the discrete element method (DEM) with spherical particles and realistic contact laws, we investigate the mechanics of granular systems with a wide range of inter-particle frictional coefficients and aim to establish a friction-dependent rheology for dry granular flows. The corresponding results show that increasing inter-particle friction dramatically increases the effective frictional coefficient, $\mu_{\textrm{eff}}$, while decreasing the solid fraction of the system and increasing the transitional inertial number that marks the division of quasi-static regimes and intermediate flow regimes. We further propose a new dimensionless number, $\mathcal{M}$, as a ratio between the inertial effect and frictional effect, which is similar to the effective aspect ratio in granular column collapses, and unifies the influence of inter-particle friction with the inertial number. We then establish a relationship between $\mathcal{M}$ and the dimensionless granular temperature, $\Theta$, to further universalize the influence of inter-particle frictions. Such study can broaden the application of the $\mu(I)$ rheology in natural and engineering systems and help establish a more general constitutive model for complex granular systems.

In this letter, we investigate the transient rheological behavior of immersed granular flows using both experiments of submerged granular column collapses and corresponding numerical simulations. The simulations are performed with the lattice-Boltzmann method (LBM) coupled with the discrete element method (DEM) and provide a significant amount of data of the stress and deformation conditions at different positions and times during the granular collapse. We derive a new dimensionless number $\mathcal{G}$ that can unify the rheology of transient granular flows in different regimes for all the simulation data points. $\mathcal{G}$ smoothly transforms from an inertial number into a viscous number, unifying both extremes of the rheology law. We also show the need to introduce the kinetic stresses to achieve a universal relation. The findings establish a transient constitutive framework for visco-inertial granular flows and are important for a better understanding of granular-fluid mixtures in both natural and engineering situations.

The increase in the observed volume in cosmological surveys imposes various challenges on simulation preparations. Firstly, the volume of the simulations required increases proportionally to the observations. However, large-volume simulations are quickly becoming computationally intractable. Secondly, on-going and future large-volume survey are targeting smaller objects, e.g. emission line galaxies, compared to the earlier focus, i.e. luminous red galaxies. They require the simulations to have higher mass resolutions. In this work we present a machine learning (ML) approach to calibrate the halo catalogue of a low-resolution (LR) simulation by training with a paired high-resolution (HR) simulation with the same background white noise, thus we can build the training data by matching HR haloes to LR haloes in a one-to-one fashion. After training, the calibrated LR halo catalogue reproduces the mass-clustering relation for mass down to $2.5\times 10^{11}~h^{-1}M_\odot$ within $5~{\rm per~cent}$ at scales $k<1~h\,\rm Mpc^{-1}$. We validate the performance of different statistics including halo mass function, power spectrum, two-point correlation function, and bispectrum in both real and redshift space. Our approach generates high-resolution-like halo catalogues ($>200$ particles per halo) from low-resolution catalogues ($>25$ particles per halo) containing corrected halo masses for each object. This allows to bypass the computational burden of a large-volume real high-resolution simulation without much compromise in the mass resolution of the result. The cost of our ML approach ($\sim 1$ CPU-hour) is negligible compared to the cost of a $N$-body simulation (e.g. millions of CPU-hours), The required computing time is cut a factor of 8., Comment: 15 pages, 13 figures, Accepted for publication in MNRAS

Cosmological $N$-body simulations provide numerical predictions of the structure of the Universe against which to compare data from ongoing and future surveys, but the growing volume of the Universe mapped by surveys requires correspondingly lower statistical uncertainties in simulations, usually achieved by increasing simulation sizes at the expense of computational power. It was recently proposed to reduce simulation variance without incurring additional computational costs by adopting fixed-amplitude initial conditions. This method has been demonstrated not to introduce bias in various statistics, including the two-point statistics of galaxy samples typically used for extracting cosmological parameters from galaxy redshift survey data, but requires us to revisit current methods for estimating covariance matrices of clustering statistics for simulations. In this work, we find that it is not trivial to construct covariance matrices analytically for fixed-amplitude simulations, but we demonstrate that EZmock (Effective Zel'dovich approximation mock catalogue), the most efficient method for constructing mock catalogues with accurate two- and three-point statistics, provides reasonable covariance matrix estimates for such simulations. We further examine how the variance suppression obtained by amplitude-fixing depends on three-point clustering, small-scale clustering, and galaxy bias, and propose intuitive explanations for the effects we observe based on the EZmock bias model., Comment: 9 pages, 7 figures; in v2 we incorporate minor modifications from peer review and copyediting

Lattices form a compact representation of multiple hypotheses generated from an automatic speech recognition system and have been shown to improve performance of downstream tasks like spoken language understanding and speech translation, compared to using one-best hypothesis. In this work, we look into the effectiveness of lattice cues for rescoring n-best lists in second-pass. We encode lattices with a recurrent network and train an attention encoder-decoder model for n-best rescoring. The rescoring model with attention to lattices achieves 4-5% relative word error rate reduction over first-pass and 6-8% with attention to both lattices and acoustic features. We show that rescoring models with attention to lattices outperform models with attention to n-best hypotheses. We also study different ways to incorporate lattice weights in the lattice encoder and demonstrate their importance for n-best rescoring., Comment: Submitted to ICASSP 2022

Many experiments have shown large flow enhancement ratios (up to 10^5) in carbon nanotubes (CNT) with diameters larger than 5nm. However, molecular dynamics simulations have never replicated these results maintaining a three-order-of-magnitude gap with measurements. Our study provides a generic model of nanofluidics for continuum slip flow (diameter>3nm) that fills this significant gap and sheds light on its origin. Compared to 140 literature cases, the model explains the entire range of experimental flow enhancements by changes of nanotube diameters and finite variations of interfacial energies. Despite large variations of flow enhancement ratios spanning 5 orders of magnitude in experimental results, the ratio between these data and corresponding model predictions approaches unity for the majority of experiments. The role of viscous entrance effects is discussed. The model provides insight into puzzling observations such as differences of CNTs and boron nitride nanotubes, the slip on low-contact-angle surfaces and massive functionalization effects. This study could advance our understanding of nano-scale transport mechanisms and aid the design of tailored nanomembranes.

In this letter, we focus on the size effect of granular column collapses, which are potentially connected to the dynamics of complex geophysical flows, even if the link between microscopic structures of granular assemblies and their macroscopic behaviors is still not well understood. Using the sphero-polyhedral discrete element method (DEM), we show that the column radius/grain size ratio has a strong influence on the collapse behavior. A finite-size analysis, which is inspired by a phase transition around an inflection point, is performed to obtain a general scaling equation with critical exponents for run-out distances. We further link the size effect with the strong force network and formalize a correlation length scale that exponentially scales with the effective aspect ratio. Such a scaling solution shows similarities with the percolation problem of two-dimensional random networks and can be extended to other similar natural and engineering systems.

Granular column collapses result in an array of flow phenomena and deposition morphologies, the understanding of which brings insights into studying granular flows in both natural and engineering systems. Guided by experiments, we carried out computational studies with the discrete element method (DEM) to identify fundamental links between the macro-scale behavior and micro-scale properties of granular columns. A physics-based non-dimensional number combining particle and bulk properties of the column, $\alpha_{\textrm{eff}}$, was found to determinate three collapse regimes (quasi-static, inertial, and liquid-like), revealing universal trends of flow regimes and deposition morphologies under different conditions. This non-dimensional number represents physically the competing inertial and frictional effects that govern the behavior of the granular column collapse, including energy conversion and dissipation. The finding is important for understanding quantitatively the flow of granular materials and their deposits.

We present COSMIC BIRTH: COSMological Initial Conditions from Bayesian Inference Reconstructions with THeoretical models: an algorithm to reconstruct the primordial and evolved cosmic density fields from galaxy surveys on the light-cone. The displacement and peculiar velocity fields are obtained from forward modelling at different redshift snapshots given some initial cosmic density field within a Gibbs-sampling scheme. This allows us to map galaxies, observed in a light-cone, to a single high redshift and hereby provide tracers and the corresponding survey completeness in Lagrangian space including phase-space mapping. These Lagrangian tracers in turn permit us to efficiently obtain the primordial density field, making the COSMIC BIRTH code general to any structure formation model. Our tests are restricted for the time being to Augmented Lagrangian Perturbation theory. We show how to robustly compute the non-linear Lagrangian bias from clustering measurements in a numerical way, enabling us to get unbiased dark matter field reconstructions at initial cosmic times. We also show that we can greatly recover the information of the dark matter field from the galaxy distribution based on a detailed simulation. Novel key ingredients to this approach are a higher-order Hamiltonian sampling technique and a non-diagonal Hamiltonian mass-matrix. This technique could be used to study the Eulerian galaxy bias from galaxy surveys and could become an ideal baryon acoustic reconstruction technique. In summary, this method represents a general reconstruction technique, including in a self-consistent way a survey mask, non-linear and non-local bias and redshift space distortions, with an efficiency about 10 times superior to previous comparable methods., Comment: 21 pages, 14 figs (accepted at MNRAS)

We describe our nonlinear emulation (i.e., interpolation) framework that combines the halo occupation distribution (HOD) galaxy bias model with $N$-body simulations of nonlinear structure formation, designed to accurately predict the projected clustering and galaxy-galaxy lensing signals from luminous red galaxies (LRGs) in the redshift range $0.16 < z < 0.36$ on comoving scales $0.6 < r_p < 30$ \hMpc. The interpolation accuracy is $\lesssim 1-2$ per cent across the entire physically plausible range of parameters for all scales considered. We correctly recover the true value of the cosmological parameter $S_8 = ({\sigma_8}/{0.8228}) ({\Omega_{\text{m}}}/{0.3107})^{0.6}$ from mock measurements produced via subhalo abundance matching (SHAM)-based lightcones designed to approximately match the properties of the SDSS LOWZ galaxy sample. Applying our model to Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 14 (DR14) LOWZ galaxy clustering and galaxy-shear cross-correlation measurements made with Sloan Digital Sky Survey (SDSS) Data Release 8 (DR8) imaging, we perform a prototype cosmological analysis marginalizing over $w$CDM cosmological parameters and galaxy HOD parameters. We obtain a 4.4 per cent measurement of $S_8 = 0.847 \pm 0.037$, in $3.5\sigma$ tension with the Planck cosmological results of $1.00 \pm 0.02$. We discuss the possibility of underestimated systematic uncertainties or astrophysical effects that could explain this discrepancy., Comment: Updated to match accepted version

Liquid slip flow with a Knudsen number Kn = 0.001-0.1 plays a dominant role in confined flow channels. Its physical origin can be attributed to the intermolecular fluid-solid (F-S) interaction force. To this end, we propose herein continuous force functions (decaying either exponentially or by a power law) between fluid particles and two confined flat walls in the framework of the mesoscopic lattice Boltzmann model (LBM). The analytical solutions for density profile, velocity profile, slip length, and permeability ratio are derived and related to the mesoscale F-S interaction parameters and the size of the gap of the flow channel. Through nondimensionalization of the analytical solutions, we obtain the dimensionless numbers that indicate the key feature of slip-flow systems for each of the proposed force functions. The analytical solutions are strictly consistent with the LBM solutions. We suggest reasonable ranges for the F-S interaction parameters based on the observed range of density ratio (film fluid to bulk fluid) and the increasing permeability ratios with narrowing gap size. Within the given range of interaction parameters, simple relationships between permeability ratios and dimensionless numbers are provided by fitting. The curves for continuous F-S interaction force with two free parameters are calibrated for a hydrophobic surface by using LBM simulations, which were validated a priori by comparison with the slip velocity profile measured in a benchmark flow experiment. The mesoscopic LBM model based on the proposed F-S interaction force functions provides a robust framework to elucidate the physical process of liquid slip flow.

We present cosmological parameter constraints based on a joint modeling of galaxy-lensing cross correlations and galaxy clustering measurements in the SDSS, marginalizing over small-scale modeling uncertainties using mock galaxy catalogs, without explicit modeling of galaxy bias. We show that our modeling method is robust to the impact of different choices for how galaxies occupy dark matter halos and to the impact of baryonic physics (at the $\sim2\%$ level in cosmological parameters) and test for the impact of covariance on the likelihood analysis and of the survey window function on the theory computations. Applying our results to the measurements using galaxy samples from BOSS and lensing measurements using shear from SDSS galaxies and CMB lensing from Planck, with conservative scale cuts, we obtain $S_8\equiv\left(\frac{\sigma_8}{0.8228}\right)^{0.8}\left(\frac{\Omega_m}{0.307}\right)^{0.6}=0.85\pm0.05$ (stat.) using LOWZ $\times$ SDSS galaxy lensing, and $S_8=0.91\pm0.1$ (stat.) using combination of LOWZ and CMASS $\times$ Planck CMB lensing. We estimate the systematic uncertainty in the galaxy-galaxy lensing measurements to be $\sim6\%$ (dominated by photometric redshift uncertainties) and in the galaxy-CMB lensing measurements to be $\sim3\%$, from small scale modeling uncertainties including baryonic physics., Comment: Published in MNRAS

We present the UNIT $N$-body cosmological simulations project, designed to provide precise predictions for nonlinear statistics of the galaxy distribution. We focus on characterizing statistics relevant to emission line and luminous red galaxies in the current and upcoming generation of galaxy surveys. We use a suite of precise particle mesh simulations (FastPM) as well as with full $N$-body calculations with a mass resolution of $\sim 1.2\times10^9\,h^{-1}$M$_{\odot}$ to investigate the recently suggested technique of Angulo & Pontzen 2016 to suppress the variance of cosmological simulations We study redshift space distortions, cosmic voids, higher order statistics from $z=2$ down to $z=0$. We find that both two- and three-point statistics are unbiased. Over the scales of interest for baryon acoustic oscillations and redshift-space distortions, we find that the variance is greatly reduced in the two-point statistics and in the cross correlation between halos and cosmic voids, but is not reduced significantly for the three-point statistics. We demonstrate that the accuracy of the two-point correlation function for a galaxy survey with effective volume of 20 ($h^{-1}$Gpc)$^3$ is improved by about a factor of 40, indicating that two pairs of simulations with a volume of 1 ($h^{-1}$Gpc)$^3$ lead to the equivalent variance of $\sim$150 such simulations. The $N$-body simulations presented here thus provide an effective survey volume of about seven times the effective survey volume of DESI or Euclid. The data from this project, including dark matter fields, halo catalogues, and their clustering statistics, are publicly available at http://www.unitsims.org., Comment: 12 pages, 9 figures. This version matches the one accepted by MNRAS. The data from this project are publicly available at: http://www.unitsims.org