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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. [ABSTRACT FROM AUTHOR]

A regularized version of the lattice Boltzmann method for efficient simulation of soft materials is introduced. Unlike standard approaches, this method reconstructs the distribution functions from available hydrodynamic variables (density, momentum, and pressure tensor) without storing the full set of discrete populations. This scheme shows significantly lower memory requirements and data access costs. A series of benchmark tests of relevance to soft matter, such as collisions of fluid droplets, is discussed to validate the method. The results can be of particular interest for high-performance simulations of soft matter systems on future exascale computers. [ABSTRACT FROM AUTHOR]

We present a highly optimized thread-safe lattice Boltzmann model in which the non-equilibrium part of the distribution function is locally reconstructed via recursivity of Hermite polynomials. Such a procedure allows the explicit incorporation of non-equilibrium moments of the distribution up to the order supported by the lattice. Thus, the proposed approach increases accuracy and stability at low viscosities without compromising performance and amenability to parallelization with respect to standard lattice Boltzmann models. The high-order thread-safe lattice Boltzmann is tested on two types of turbulent flows, namely, the turbulent channel flow at R e τ = 180 and the axisymmetric turbulent jet at Re = 7000; it delivers results in excellent agreement with reference data [direct numerical simulations (DNS), theory, and experiments] and (a) achieves peak performance [ ∼ 5 × 10 12 floating point operations (FLOP) per second and an arithmetic intensity of ∼ 7 FLOP / byte on a single graphic processing unit] by significantly reducing the memory footprint, (b) retains the algorithmic simplicity of standard lattice Boltzmann computing, and (c) allows to perform stable simulations at vanishingly low viscosities. Our findings open attractive prospects for high-performance simulations of realistic turbulent flows on GPU-based architectures. Such expectations are confirmed by excellent agreement among lattice Boltzmann, experimental, and DNS reference data. [ABSTRACT FROM AUTHOR]

In microfluidic systems, droplets undergo intricate deformations as they traverse flow-focusing junctions, posing a challenging task for accurate measurement, especially during short transit times. This study investigates the physical behavior of droplets within dense emulsions in diverse microchannel geometries, specifically focusing on the impact of varying opening angles within the primary channel and injection rates of fluid components. Employing a sophisticated droplet tracking tool based on deep-learning techniques, we analyze multiple frames from flow-focusing experiments to quantitatively characterize droplet deformation in terms of ratio between maximum width and height and propensity to form liquid with hexagonal spatial arrangement. Our findings reveal the existence of an optimal opening angle where shape deformations are minimal and hexagonal arrangement is maximal. Variations of fluid injection rates are also found to affect size and packing fraction of the emulsion in the exit channel. This paper offers insight into deformations, size, and structure of fluid emulsions relative to microchannel geometry and other flow-related parameters captured through machine learning, with potential implications for the design of microchips utilized in cellular transport and tissue engineering applications. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic model predictive control approach combined with a time-series forecasting technique to tackle the problem of microgrid energy management in the face of uncertainty. The data-driven nonparametric chance constraint method is used to formulate chance constraints for stochastic model predictive control, while removing the dependency on probability density assumptions of uncertain variables and retaining the linear structure of the resulting optimization problem. The proposed approach is suitable for implementation on systems with limited computational power or limited memory storage, thanks to its simple linear structure and its ability to provide accurate results within pre-defined confidence levels, even when using small data batches. The proposed forecasting and stochastic model predictive control approaches are applied on a numerical example featuring a small gridconnected microgrid with PV generation, a battery storage system, and a noncontrollable load, showing the ability to reduce costs by reducing the confidence level, and to satisfy pre-defined confidence levels. [ABSTRACT FROM AUTHOR]

We present an extension of the multiparticle collision dynamics method for flows with complex interfaces, including supramolecular near-contact interactions mimicking the effect of surfactants. The new method is demonstrated for the case of (i) short range repulsion of droplets in close contact, (ii) arrested phase separation, and (iii) different pattern formation during spinodal decomposition of binary mixtures. [ABSTRACT FROM AUTHOR]

Given methane hydrates' importance in marine sediments, as well as the widespread use of seabed acoustic-signaling methods in oil and gas exploration, the elastic characterization of these materials is particularly relevant. A greater understanding of the properties governing phonon, sound, and acoustic propagation would help to better classify methane-hydrate deposits, aiding in their discovery. Recently, we have published a new nonequilibrium molecular-dynamics (NEMD) methodology to recreate longitudinal and transverse perturbations, observing their propagation through a crystalline lattice by various metrics, to study the underlying S- and P-wave velocities (achieving excellent agreement with experiment) [Melgar et al., J. Phys. Chem. 122(5), 3006–3013 (2018); ibid.150, 084101 (2019)]. Here, we apply these NEMD methods to methane-clathrate systems to study acoustic-propagation characteristics, as well as the lattice elastic behavior. In so doing, we determine S- and P-wave velocities in excellent accord with experiment; we also ascertain the allowable magnitude range of acoustic perturbation and establish a threshold for lattice breakup and hydrate decomposition. Interestingly, upon dissociation, we observe the formation of methane nanobubbles, which agrees with previous studies on the microscopic fundamentals of hydrate dissociation by various means. [ABSTRACT FROM AUTHOR]

α-quartz is one of the most important SiO2 polymorphs because it is the basis of very common minerals, especially for seabed materials with geoscientific importance. The elastic characterization of these materials is particularly relevant when the properties governing phonon and sound propagation are involved. These studies are especially interesting for oil exploration purposes. Recently, we published a new method that constitutes to the best of our knowledge the first attempt to recreate longitudinal and transversal perturbations in a simulation box to observe their propagation through the crystal by means of a set of descriptors [D. Melgar et al., J. Phys. Chem. C 122, 3006–3013 (2018)]. The agreement with the experimental S- and P-wave velocities was rather excellent. Thus, an effort has been undertaken to deepen the particularities of this new methodology. Here, bearing in mind this encouraging initial methodology-development progress, we deepen our knowledge of the particularities of this new methodology in presenting a systematic investigation of the implementation of the perturbation source. This includes new ways of creating the perturbation, as well as analyzing the possible effects the perturbation amplitude could have on the resultant velocities. In addition, different force fields were tested to describe the interatomic interactions. The lack of dependence of the seismic velocities on the way the perturbation is created and the perturbation amplitude, and the good agreement with the experimental results are the main reasons that allow the definition of this new methodology as robust and reliable. These qualities are consolidated by the physical behavior of the calculated velocities in the presence of vacancies and under stress. The development of this method opens up a new line of research of calculating seismic velocities for geophysically relevant materials in a systematic way, with full control not only on the sample features (composition, porosity, vacancies, stress, etc.) but also on the particularities of perturbation itself, as well as determining optimal system-response metrics. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We present a deep learning-based object detection and object tracking algorithm to study droplet motion in dense microfluidic emulsions. The deep learning procedure is shown to correctly predict the droplets' shape and track their motion at competitive rates as compared to standard clustering algorithms, even in the presence of significant deformations. The deep learning technique and tool developed in this work could be used for the general study of the dynamics of biological agents in fluid systems, such as moving cells and self-propelled microorganisms in complex biological flows. This article is part of the theme issue 'Progress in mesoscale methods for fluid dynamics simulation'. [ABSTRACT FROM AUTHOR]

We present a new multistage method to study the N-Methyl-D-Aspartate (NMDA) neuroreceptor starting from the reconstruction of its crystallographic structure. Thanks to the combination of Homology Modelling, Molecular Dynamics and Lattice Boltzmann simulations, we analyse the allosteric transition of NDMA upon ligand binding and compute the receptor response to ionic passage across the membrane. [ABSTRACT FROM AUTHOR]

For clathrate-hydrate polymorphic structure-type (sI versus sII), geometric recognition criteria have been developed and validated. These are applied to the study of the rich interplay and development of both sI and sII motifs in a variety of hydrate-nucleation events for methane and H2S hydrate studied by direct and enhanced-sampling molecular dynamics (MD) simulations. In the case of nucleation of methane hydrate from enhanced-sampling simulation, we notice that already at the transition state, ~80% of the enclathrated CH4 molecules are contained in a well-structured (sII) clathrate-like crystallite. For direct MD simulation of nucleation of H2S hydrate, some sI/sII polymorphic diversity was encountered, and it was found that a realistic dissipation of the nucleation energy (in view of nonequilibrium relaxation to either microcanonical (NVE) or isothermal-isobaric (NPT) distributions) is important to determine the relative propensity to form sI versus sII motifs. [ABSTRACT FROM AUTHOR]

We introduce a mesoscale approach for the simulation of multicomponent flows to model the direct-writing printing process, along with the early stage of ink deposition. As an application scenario, alginate solutions at different concentrations are numerically investigated alongside processing parameters, such as apparent viscosity, extrusion rate, and print head velocity. The present approach offers useful insights on the ink rheological effects upon printed products, susceptible to geometric accuracy and shear stress, by manufacturing processes such as the direct-writing printing for complex photonic circuitry, bioscaffold fabrication, and tissue engineering. [ABSTRACT FROM AUTHOR]

The formation of methane-hydrate precursors at large planar water-methane interfaces has been studied using massively parallel molecular dynamics in systems of varying size from around 10 000 to almost 7 × 106 molecules. This process took two distinct steps. First, the concentration of solvated methane clusters increases just inside the aqueous domain via slow diffusion from the methane-water interface, forming "clusters" of solvated methane molecules. Second, the re-ordering process of solvated methane and water molecules takes place in a manner very roughly consistent with the "blob" hypothesis, although with important differences, to form hydrate precursors, necessary for subsequent hydrate nucleation and crystallisation. It was found that larger system sizes serve to promote the formation rate of precursors. [ABSTRACT FROM AUTHOR]

A new class of multiscale scheme is presented for micro‐hydrodynamic problems based on a dual representation of the fluid observables. The hybrid model is first tested against the classical flow between two parallel plates and then applied to a plug flow within a micrometer‐sized striction and a shear flow within a microcavity. Both cases demonstrate the capability of the multiscale approach to reproduce the correct macroscopic hydrodynamics also in the presence of refined grids (one and two levels), while retaining the correct thermal fluctuations, embedded in the multiparticle collision method. This provides the first step toward a novel class of fully mesoscale hybrid approaches able to capture the physics of fluids at the micro‐ and nanoscales whenever a continuum representation of the fluid falls short of providing the complete physical information, due to a lack of resolution and thermal fluctuations. [ABSTRACT FROM AUTHOR]

The dihydrogen complex Ru(H2)2H2(P(C5H9)3)2 has been investigated, via ab initio accelerated molecular dynamics, to elucidate the H ligands dynamics and possible reaction paths for H2/H exchange. We have characterized the free energy landscape associated with the H atoms positional exchange around the Ru centre. From the free energy landscape, we have been able to estimate a barrier of 6 kcal mol−1 for the H2/H exchange process. We have also observed a trihydrogen intermediate as a passing state along some of the possible reaction pathways. [ABSTRACT FROM AUTHOR]

The problem of estimating the parameters of induction motor models is considered, using the data measured by a circuit breaker equipped with industrial sensors. The breaker acquires three-phase stator voltage and current derivative, which are used to formulate an optimization-based identification problem. This setup is novel with respect to the literature, where voltage and current are used. Several algorithmic aspects and improvements are discussed. The presented experimental results indicate that the circuit breaker is able to accurately estimate the machine parameters. The identified motor models can then be used for several applications within a smart grid scenario. [ABSTRACT FROM AUTHOR]

In this paper, we deploy the hybrid Lattice Boltzmann - Particle Dynamics (LBPD) method to investigate the transport properties of blood flow within arterioles and venules. The numerical approach is applied to study the transport of Red Blood Cells (RBC) through plasma, highlighting significant agreement with the experimental data in the seminal work by Fåhræus and Lindqvist. Moreover, the results provide evidence of an interesting hand-shaking between the range of validity of the proposed hybrid approach and the domain of viability of particle methods. A joint inspection of accuracy and computational cost, indicate that LBPD offers an appealing multiscale strategy for the study of blood transport across scales of motion, from macroscopic vessels, down to arterioles and venules, where particle methods can eventually take over. [ABSTRACT FROM AUTHOR]

We present a multi-layer mathematical model to describe the transdermal drug release from an iontophoretic system. The Nernst-Planck equation describes the basic convection-diffusion process, with the electric potential obtained by solving the Laplace's equation. These equations are complemented with suitable interface and boundary conditions in a multi-domain. The stability of the mathematical problem is discussed in different scenarios and a finite-difference method is used to solve the coupled system. Numerical experiments are included to illustrate the drug dynamics under different conditions. [ABSTRACT FROM AUTHOR]

We study the effects of a controlled gas flow on the dynamics of electrified jets in the electrospinning process. The main idea is to model the air drag effects of the gas flow by using a nonlinear Langevin-like approach. The model is employed to investigate the dynamics of electrified polymer jets at different conditions of air drag force, showing that a controlled gas counterflow can lead to a decrease of the average diameter of electrospun fibers, and potentially to an improvement of the quality of electrospun products. We probe the influence of air drag effects on the bending instabilities of the jet and on its angular fluctuations during the process. The insights provided by this study might prove useful for the design of future electrospinning experiments and polymer nanofiber materials. [ABSTRACT FROM AUTHOR]

We present a nonlinear Langevin model to investigate the early-stage dynamics of electrified polymer jets in electrospinning experiments. In particular, we study the effects of air drag force on the uniaxial elongation of the charged jet, right after ejection from the nozzle. Numerical simulations show that the elongation of the jet filament close to the injection point is significantly affected by the nonlinear drag exerted by the surrounding air. These results provide useful insights for the optimal design of current and future electrospinning experiments. [ABSTRACT FROM PUBLISHER]

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 2 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 2 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 2 -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. [ABSTRACT FROM AUTHOR]

We present a lattice Boltzmann model for charged leaky dielectric multiphase fluids in the context of electrified jet simulations, which are of interest for a number of production technologies including electrospinning. The role of nonlinear rheology on the dynamics of electrified jets is considered by exploiting the Carreau model for pseudoplastic fluids. We report exploratory simulations of charged droplets at rest and under a constant electric field, and we provide results for charged jet formation under electrospinning conditions. [ABSTRACT FROM AUTHOR]