Clathrate hydrates are crystalline inclusion compounds wherein a water framework encages small guest atoms/molecules within its cavities. Among the others, methane clathrates are the largest fossil fuel resource still available. They can also be used to safely transport gases and can also form spontaneously under suitable conditions plugging pipelines. Understanding the crystallization mechanism is very important, and given the impossibility of experimentally identifying the atomistic path, simulations played an important role in this field. Given the large computational cost of these simulations, in addition to all-atom force fields, scientists considered coarse-grained water models. Here, we have investigated the effect of coarse-graining, as implemented in the water model mW, on the crystallization characteristics of methane clathrate in comparison with the all-atom TIP4P force field. Our analyses revealed that although the characteristics directly depending on the energetics of the water models are well reproduced, dynamical properties are off by the orders of magnitude. Being crystallization a non-equilibrium process, the altered kinetics of the process results in different characteristics of crystalline nuclei. Both TIP4P and mW water models produce methane clathrate nuclei with some amount of the less stable (in the given thermodynamic conditions) structure II phase and an excess of pentagonal dodecahedral cages over the tetrakaidecahedral ones regarding the ideal ratio in structure I. However, the dependence of this excess on the methane concentration in solution is higher with the former water model, whereas with the latter, the methane concentration in solution dependence is reduced and within the statistical error.

Physiological solvent flows surround biological structures triggering therein collective motions. Notable examples are virus/host-cell interactions and solvent-mediated allosteric regulation. The present work describes a multiscale approach joining the Lattice Boltzmann fluid dynamics (for solvent flows) with the all-atom atomistic molecular dynamics (for proteins) to model functional interactions between flows and molecules. We present, as an applicative scenario, the study of the SARS-CoV-2 virus spike glycoprotein protein interacting with the surrounding solvent, modeled as a mesoscopic fluid. The equilibrium properties of the wild-type spike and of the Alpha variant in implicit solvent are described by suitable observables. The mesoscopic solvent description is critically compared to the all-atom solvent model, to quantify the advantages and limitations of the mesoscopic fluid description.

In the last decades kinetic theory has developed into a very elegant and effective framework to handle a broad spectrum of problems involving complex states of flowing matter, far beyond the original realm of rarefied gas dynamics. In this paper, we present recent applications of the lattice Boltzmann method to the computational design of soft mesoscale materials, including soft flowing crystals, dense multicore emulsions, as well as Petascale simulations of deep-sea glassy sponges. This manuscript is a tribute to the groundbreaking work of Carlo Cercignani and his undiminished impact on modern non-equilibrium statistical physics.

Preface to DSFD 2021, the 30th edition of the discrete simulation of fluid dynamics at the University of Tuscia, Viterbo, Italy, on September 13-17, 2021.

Active droplets are artificial microswimmers built from a liquid dispersion by microfluidic tools and showing self-propelled motion. These systems hold particular interest for mimicking biological phenomena, such as some aspects of cell locomotion and collective behaviors of bacterial colonies, as well as for the design of droplet-based biologically inspired materials, such as engineered tissues. Growing evidence suggests that geometrical confinement crucially affects their morphology and motility, but the driving physical mechanisms are still poorly understood. Here, we study the effect of activity on a droplet containing a contractile polar fluid confined within microfluidic channels of various sizes. We find a surprising wealth of shapes and dynamic regimes, whose mechanics is regulated by a subtle interplay between contractile stress, droplet elasticity, and microchannel width. They range from worm-like and cell-like shaped droplets displaying an oscillating behavior within wider channels to bullet-shaped droplets exhibiting rectilinear motion in narrower slits. Our findings support the view that geometrical confinement can provide a viable strategy to control and predict the propulsion direction of active droplets. It would be of interest to look for analogs of these motility modes in biological cells or in synthetic active matter.

Tracking droplets in microfluidics is a challenging task. The difficulty arises in choosing a tool to analyze general microfluidic videos to infer physical quantities. The state-of-the-art object detector algorithm You Only Look Once (YOLO) and the object tracking algorithm Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) are customizable for droplet identification and tracking. The customization includes training YOLO and DeepSORT networks to identify and track the objects of interest. We trained several YOLOv5 and YOLOv7 models and the DeepSORT network for droplet identification and tracking from microfluidic experimental videos. We compare the performance of the droplet tracking applications with YOLOv5 and YOLOv7 in terms of training time and time to analyze a given video across various hardware configurations. Despite the latest YOLOv7 being 10% faster, the real-time tracking is only achieved by lighter YOLO models on RTX 3070 Ti GPU machine due to additional significant droplet tracking costs arising from the DeepSORT algorithm. This work is a benchmark study for the YOLOv5 and YOLOv7 networks with DeepSORT in terms of the training time and inference time for a custom dataset of microfluidic droplets.

A suspension of nanoparticles with very low volume fraction is found to assemble into a macroscopic cellular phase that is composed of particle-rich walls and particle-free voids under the collective influence of AC and DC voltages. Systematic study of this phase transition shows that it was the result of electrophoretic assembly into a two-dimensional configuration followed by spinodal decomposition into particle-rich walls and particle-poor cells mediated principally by electrohydrodynamic flow. This mechanistic understanding reveals two characteristics needed for a cellular phase to form, namely (1) a system that is considered two dimensional and (2) short-range attractive, long-range repulsive interparticle interactions. In addition to determining the mechanism underpinning the formation of the cellular phase, this work presents a method to reversibly assemble microscale continuous structures out of nanoscale particles in a manner that may enable the creation of materials that impact diverse fields including energy storage and filtration.

We report new dynamical modes in confined soft granular flows, such as stochastic jetting and dripping, with no counterpart in continuum viscous fluids. The new modes emerge as a result of the propagation of the chaotic behavior of individual grains - here, monodisperse emulsion droplets - to the level of the entire system as the emulsion is focused into a narrow orifice by an external viscous flow. We observe avalanching dynamics and the formation of remarkably stable jets - single-file granular chains - which occasionally break, resulting in a non-Gaussian distribution of cluster sizes. We find that the sequences of droplet rearrangements that lead to the formation of such chains resemble unfolding of cancer cell clusters in narrow capillaries, overall demonstrating that microfluidic emulsion systems could serve to model various aspects of soft granular flows, including also tissue dynamics at the mesoscale.

We have investigated systematically and statistically methanol-concentration effects on methane-hydrate nucleation using both experiment and restrained molecular-dynamics simulation, employing simple observables to achieve an initially homogeneous methane-supersaturated solution particularly favorable for nucleation realization in reasonable simulation times. We observe the pronounced "bifurcated" character of the nucleation rate upon methanol concentration in both experiments and simulation, with promotion at low concentrations and switching to industrially familiar inhibition at higher concentrations. Higher methanol concentrations suppress hydrate growth by in-lattice methanol incorporation, resulting in the formation of "defects", increasing the energy of the nucleus. At low concentrations, on the contrary, the detrimental effect of defects is more than compensated for by the beneficial contribution of CHin easing methane incorporation in the cages or replacing it altogether.

In this perspective we take stock of the current state of the art of computational models for droplets microfluidics and we suggest some strategies which may open the way to the full-scale simulation of microfluidic phenomena with interfaces, from near-contact interactions to the device operational lengths.

We present LBcuda, a GPU accelerated version of LBsoft, our open-source MPI-based software for the simulation of multi-component colloidal flows. We describe the design principles, the optimization and the resulting performance as compared to the CPU version, using both an average cost GPU and high-end NVidia GPU cards (V100 and the latest A100). The results show a substantial acceleration for the fluid solver reaching up to 200 GLUPS (Giga Lattice Updates Per Second) on a cluster made of 512 A100 NVIDIA cards simulating a grid of eight billion lattice points. These results open attractive prospects for the computational design of new materials based on colloidal particles. Program summary: Program Title: LBcuda CPC Library link to program files: https://doi.org/10.17632/v6fvmzpcrn.1 Developer's repository link: https://github.com/copmat/LBcuda Licensing provisions: 3-Clause BSD License Programming language: CUDA Fortran Nature of problem: Hydro-dynamics of colloidal multi-component systems and Pickering emulsions. Solution method: Lattice-Boltzmann method solving the Navier-Stokes equations for the fluid dynamics within an Eulerian description. Particle solver describing colloidal particles within a Lagrangian representation coupled to the fluid solver. The numerical solution of the coupling algorithm includes the back reaction effects for each force terms according to a fluid-particle multi-scale paradigm.

Deep neural networks are rapidly emerging as data analysis tools, often outperforming the conventional techniques used in complex microfluidic systems. One fundamental analysis frequently desired in microfluidic experiments is counting and tracking the droplets. Specifically, droplet tracking in dense emulsions is challenging due to inherently small droplets moving in tightly packed configurations. Sometimes, the individual droplets in these dense clusters are hard to resolve, even for a human observer. Here, two deep learning-based cutting-edge algorithms for object detection [you only look once (YOLO)] and object tracking (DeepSORT) are combined into a single image analysis tool, DropTrack, to track droplets in the microfluidic experiments. DropTrack analyzes input microfluidic experimental videos, extracts droplets' trajectories, and infers other observables of interest, such as droplet numbers. Training an object detector network for droplet recognition with manually annotated images is a labor-intensive task and a persistent bottleneck. In this work, this problem is partly resolved by training many object detector networks (YOLOv5) with several hybrid datasets containing real and synthetic images. We present an analysis of a double emulsion experiment as a case study to measure DropTrack's performance. For our test case, the YOLO network trained by combining 40% real images and 60% synthetic images yields the best accuracy in droplet detection and droplet counting in real experimental videos. Also, this strategy reduces labor-intensive image annotation work by 60%. DropTrack's performance is measured in terms of mean average precision of droplet detection, mean squared error in counting the droplets, and image analysis speed for inferring droplets' trajectories. The fastest configuration of DropTrack can detect and track the droplets at approximately 30 frames per second, well within the standards for a real-time image analysis.

The radical polymerization process of acrylate compounds is, nowadays, numerically investigated using classical force fields and reactive molecular dynamics, with the aim to probe the gel-point transition as a function of the initial radical concentration. In the present paper, the gel-point transition of the 1,6-hexanediol dimethacrylate (HDDMA) is investigated by a coarser force field which grants a reduction in the computational costs, thereby allowing the simulation of larger system sizes and smaller radical concentrations. Hence, the polymerization is investigated using reactive classical molecular dynamics combined with a dynamical approach of the nonequilibrium molecular dynamics (D-NEMD). The network structures in the polymerization process are probed by cluster analysis tools, and the results are critically compared with the similar all-atom system, showing a good agreement.

Photocurable polymers are used ubiquitously in 3D printing, coatings, adhesives, and composite fillers. In the present work, the free radical polymerization of photocurable compounds is studied using reactive classical molecular dynamics combined with a dynamical approach of the nonequilibrium molecular dynamics (D-NEMD). Different concentrations of radicals and reaction velocities are considered. The mechanical properties of the polymer resulting from 1,6-hexanediol dimethacrylate systems are characterized in terms of viscosity, diffusion constant, and activation energy, whereas the topological ones through the number of cycles (polymer loops) and cyclomatic complexity. Effects like volume shrinkage and delaying of the gel point for increasing monomer concentration are also predicted, as well as the stress-strain curve and Young's modulus. Combining ab initio, reactive molecular dynamics, and the D-NEMD method might lead to a novel and powerful tool to describe photopolymerization processes and to original routes to optimize additive manufacturing methods relying on photosensitive macromolecular systems.

This work analyzes trajectories obtained by YOLO and DeepSORT algorithms of dense emulsion systems simulated via lattice Boltzmann methods. The results indicate that the individual droplet's moving direction is influenced more by the droplets immediately behind it than the droplets in front of it. The analysis also provide hints on constraints of a dynamical model of droplets for the dense emulsion in narrow channels.

Modeling and direct numerical simulation of particle-laden flows have a tremendous variety of applications in science and engineering across a vast spectrum of scales from pollution dispersion in the atmosphere, to fluidization in the combustion process, to aerosol deposition in spray medication, along with many others. Due to their strongly nonlinear and multiscale nature, the above complex phenomena still raise a very steep challenge to themost computationalmethods. In this review,we provide comprehensive coverage of multibody hydrodynamic (MBH) problems focusing on particulate suspensions in complex fluidic systems that have been simulated using hybrid Eulerian-Lagrangian particulate flow models. Among these hybridmodels, the Immersed Boundary-Lattice Boltzmann Method (IB-LBM) provides mathematically simple and computationally-efficient algorithms for solid-fluid hydrodynamic interactions in MBH simulations. This paper elaborates on the mathematical framework, applicability, and limitations of various 'simple to complex' representations of closecontact interparticle interactions and collision methods, including short-range interparticle and particle-wall steric interactions, spring and lubrication forces, normal and oblique collisions, and mesoscale molecular models for deformable particle collisions based on hard-sphere and soft-sphere models in MBH models to simulate settling or flow of nonuniform particles of different geometric shapes and sizes in diverse fluidic systems.

Multiple emulsions are a class of soft fluid in which small drops are immersed within a larger one and stabilized over long periods of time by a surfactant. We recently showed that, if a monodisperse multiple emulsion is subject to a pressure-driven flow, a wide variety of nonequilibrium steady states emerges at late times, whose dynamics relies on a complex interplay between hydrodynamic interactions and multibody collisions among internal drops. In this work, we use lattice Boltzmann simulations to study the dynamics of polydisperse double emulsions driven by a Poiseuille flow within a microfluidic channel. Our results show that their behavior is critically affected by multiple factors, such as initial position, polydispersity index, and area fraction occupied within the emulsion. While at low area fraction inner drops may exhibit either a periodic rotational motion (at low polydispersity) or arrange into nonmotile configurations (at high polydispersity) located far from each other, at larger values of area fraction they remain in tight contact and move unidirectionally. This decisively conditions their close-range dynamics, quantitatively assessed through a time-efficiency-like factor. Simulations also unveil the key role played by the capsule, whose shape changes can favor the formation of a selected number of nonequilibrium states in which both motile and nonmotile configurations are found.

A recently proposed mesoscale approach for the simulation of multicomponent flows with near-contact interactions is employed to investigate the early stage formation and clustering statistics of soft flowing crystals in microfluidic channels. Specifically, we first demonstrate the ability of the aforementioned mesoscale model to accurately reproduce main mechanisms leading to the formation of two basic droplet patterns (triangular and hexagonal), in close agreement with experimental evidence. Next, we quantitatively evaluate the device-scale clustering efficiency of the crystal formation process by introducing a new orientational order parameter, based on the Delaunay triangulation and Voronoi diagrams analysis of the droplet patterns. The mesoscale computational approach employed in this work proves to be an efficient tool to shed new light on the complex dynamics of dense emulsions, from short-scale thin-film hydrodynamics, all the way up to global structure formation and statistics of the resulting droplets ensembles.

A recently proposed mesoscale approach for the simulation of multicomponent flows with near-contact interactions is employed to investigate the early stage formation and clustering statistics of soft flowing crystals in microfluidic channels. Specifically, we first demonstrate the ability of the aforementioned mesoscale model to accurately reproduce main mechanisms leading to the formation of two basic droplet patterns (triangular and hexagonal), in close agreement with experimental evidence. Next, we quantitatively evaluate the device-scale clustering efficiency of the crystal formation process by introducing a new orientational order parameter, based on the Delaunay triangulation and Voronoi diagrams analysis of the droplet patterns. The mesoscale computational approach employed in this work proves to be an efficient tool to shed new light on the complex dynamics of dense emulsions, from short-scale thin-film hydrodynamics, all the way up to global structure formation and statistics of the resulting droplets ensembles.

Thermal and mechanical energy storage is pivotal for the effective exploitation of renewable energy sources, thus fostering the transition to a sustainable economy. Hydrogen-based systems are among the most promising solutions for electrical energy storage. However, several technical and economic barriers (e.g., high costs, low energy and power density, advanced material requirements) still hinder the diffusion of such solutions. Similarly, the realization of latent heat storages through phase change materials is particularly attractive because it provides high energy density in addition to allowing for the storage of the heat of fusion at a (nearly) constant temperature. In this paper, we posit the challenge to couple a metal hydride H canister with a latent heat storage, in order to improve the overall power density and realize a passive control of the system temperature. A highly flexible numerical solver based on a hybrid Lattice Boltzmann Phase-Field (LB-PF) algorithm is developed to assist the design of the hybrid PCM-MH tank by studying the melting and solidification processes of paraffin-like materials. The present approach is used to model the storage of the heat released by the hydride during the H loading process in a phase change material (PCM). The results in terms of Nusselt numbers are used to design an enhanced metal-hydride storage for H-based energy systems, relevant for a reliable and cost-effective "Hydrogen Economy". The application of the developed numerical model to the case study demonstrates the feasibility of the posited design. Specifically, the phase change material application significantly increases the heat flux at the metal hydride surface, thus improving the overall system power density.