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A new kind of surface material is found and defined in the Balmer–Kapteyn (B-K) cryptomare region, Mare-like cryptomare deposits (MCD), representing highland debris mixed by mare deposits with a certain fraction. This postulates the presence of surface materials in the cryptomare regions. In this study, to objectively verify the existence of the MCD in the cryptomare regions, based on the Chang'E-2 microwave radiometer (MRM) data, the support vector machine (SVM) method was adopted, where the K-means algorithm was used to optimize the training samples and the random forest algorithm was used to select the proper band features. Finally, the extracted MCD is identified with the datasets including Lunar Reconnaissance Orbiter Wide Angle Camera, Diviner, and Clementine UV–VIS. The main findings are as follows: (1) Compared to the range outlined via the TB counter, the range of the MCD is objectively extracted using the SVM method in the B-K cryptomare region, which is reasonably indicated by the FeO abundance, TiO2 abundance, and rock abundance distributions. (2) The MCDs were extracted in the Dewar, Lomonosov–Fleming (L-F), and Schiller–Schickard (S-S) regions, indicating that the MCDs are widely distributed in the cryptomaria. (3) The presence of MCDs is concentrated in a limited region, accounting for 64.9%, 52.3%, 76.4%, and 64%, respectively, in the range of Dewar, L-F, S-S, and B-K regions identified using the optical data. The occurrence of the MCD gives a new understanding of the surface evolution in the cryptomare regions. [ABSTRACT FROM AUTHOR]

Lunar cryptomare records both early-stage mare volcanisms and large-scale impact cratering, which can provide important information about the thermal evolution of the Moon. We built a mixing dielectric constant model to represent the cryptomare deposits mixed by highland debris and mare deposits, and the proper radiative transfer simulation was constructed to evaluate the thermal emission features of surface deposits in the cryptomare region. The microwave radiometer (MRM) data in the Balmer-Kapteyn region were extracted, and the linear interpolation method was used to generate brightness temperature (TB) maps at noon and at night. To enhance the correlation between cryptomare deposits and TB performances, normalized TB (nTB) and TB difference (dTB) maps were also generated. Combined with the datasets, including Lunar Reconnaissance Orbiter Wide Angle Camera, Lunar Orbiter Laser Altimeter, and Diviner and Clementine UV–VIS, the main findings are as follows: (1) The mare-like cryptomare deposits were discovered and identified according to the nTB and dTB performances. Combined with the surface compositions, at least two kinds of buried mare deposits were identified in the B-K region, which erupted during different episodes. (2) A construct-like volcanic feature was suggested by the nTB and dTB performances. (3) The results of our analysis indicated the presence of materials with low dTB anomalies in the northern and southwestern parts of the cryptomare region and in the mare unit within the Vendelinus crater, which illustrates the heterogeneity of the lunar crust in the vertical direction. [ABSTRACT FROM AUTHOR]

Motivation Phage–host associations play important roles in microbial communities. But in natural communities, as opposed to culture-based lab studies where phages are discovered and characterized metagenomically, their hosts are generally not known. Several programs have been developed for predicting which phage infects which host based on various sequence similarity measures or machine learning approaches. These are often based on whole viral and host genomes, but in metagenomics-based studies, we rarely have whole genomes but rather must rely on contigs that are sometimes as short as hundreds of bp long. Therefore, we need programs that predict hosts of phage contigs on the basis of these short contigs. Although most existing programs can be applied to metagenomic datasets for these predictions, their accuracies are generally low. Here, we develop ContigNet, a convolutional neural network-based model capable of predicting phage–host matches based on relatively short contigs, and compare it to previously published VirHostMatcher (VHM) and WIsH. Results On the validation set, ContigNet achieves 72–85% area under the receiver operating characteristic curve (AUROC) scores, compared to the maximum of 68% by VHM or WIsH for contigs of lengths between 200 bps to 50 kbps. We also apply the model to the Metagenomic Gut Virus (MGV) catalogue, a dataset containing a wide range of draft genomes from metagenomic samples and achieve 60–70% AUROC scores compared to that of VHM and WIsH of 52%. Surprisingly, ContigNet can also be used to predict plasmid-host contig associations with high accuracy, indicating a similar genetic exchange between mobile genetic elements and their hosts. Availability and implementation The source code of ContigNet and related datasets can be downloaded from https://github.com/tianqitang1/ContigNet. [ABSTRACT FROM AUTHOR]

Fluidized beds are widely used in many chemical engineering industrials, and flow behaviors of multiphase flow have always been the focus of research. This paper presented how rolling friction and cohesive liquid affected wet particle flow behaviors by experiments, simulation, and data analyses. Experiments were conducted for applying a suitable range of rolling friction coefficient. A discrete element method, well validated by comparing experimental data, was developed and demonstrated that μr = 0.125 was a good choice for particles applied in this work. Data from numerical results were well analyzed by factor analysis, where explained interaction effect of two factors on fluidized beds. Factor analyses reflected that dominating factor and interaction effect on flow behaviors vary instead of single control factor in different regions. These findings have clearly showed importance and effect of microscopic parameter collision characteristics on the hydrodynamic behaviors in fluidized beds. Thus, rolling friction constitutes should be carefully considered to ensure that theoretical results are accurate. [ABSTRACT FROM AUTHOR]

The emerging resistive random-access memory (RRAM) has been widely applied in accelerating the computing of deep neural networks. However, it is challenging to achieve high-precision computations based on RRAM due to the limits of the resistance level and the interfaces. Low bit-width convolutional neural networks (CNNs) provide promising solutions to introduce low bit-width RRAM devices and low bit-width interfaces in RRAM-based computing system (RCS). While open questions still remain regarding: 1) how to make matrix splitting when a single crossbar is not large enough to hold all parameters of one weight matrix; 2) how to design a pipeline to accelerate the inference based on line buffer structure; and 3) how to reduce the accuracy drop due to the parameter splitting and data quantization. In this paper, we propose an RRAM crossbar-based low bit-width CNN (LB-CNN) accelerator. We make detailed discussion on the system design, including the matrix splitting strategies to enhance the scalability, and the pipelined implementation based on line buffers to accelerate the inference. In addition, we propose a splitting and quantizing while training method to incorporate the actual hardware constraints with the training. In our experiments, low bit-width LeNet-5 on RRAM show much better robustness than multibit models with device variation. The pipeline strategy achieves approximately $6.0\times $ speedup to process each image on ResNet-18. For low-bit VGG-8 on CIFAR-10, the proposed accelerator saves 54.9% of the energy consumption and 48.3% of the area compared with the multibit VGG-8 structure. [ABSTRACT FROM AUTHOR]

The hydrodynamics of binary particles in bubbling fluidized beds are of significance to a wide range of industries, and acceptable agreement has been found between experimental measurement and numerical calculation of the vertical velocity distribution of binary particles. Calculations using different drag models and restitution coefficients have been used to model a variety of superficial gas velocities, particle components, and particle diameters. Fluctuation of the void fraction is regular, but its periodicity is complex. Small and large particles are segregated in the bed, and the segregation is more obvious when the restitution coefficient is increased. This demonstrates the complex nature of gas–solid and solid–solid interactions, and the behaviour of binary solids; these results should aid further study of multi-solid behavioural mechanisms. Unlabelled Image • PIV is used to obtain the movement of binary solids in bubbling fluidized bed. • Distribution rules of large and small particles in the bed is studied. • Effects of drag models and restitution coefficient on particle behaviours are investigated. [ABSTRACT FROM AUTHOR]

[Display omitted] • A gas–liquid-solid quasi-three-phase DEM model was developed for spray fluidized bed. • Effect of liquid bridge force was analyzed in particle coating growth process. • A growth mechanism of liquid properties and particle fluidization was revealed. • The homogeneity of coated particles was significantly affected by gas velocity. Wet granulation processes are widely used in chemical, food, pharmaceutical, and various other industrial processes. During the process of particle coating growth, particle fluidization state and liquid characteristics have an impact on the layer's thickness and homogeneity. In this study, discrete element, liquid bridge, and droplet motion models were combined to investigate the impact of injection gas velocity, droplet diameter and injection liquid viscosity on the particle growth ratio and coating thickness distribution, and the particle growth mechanism was investigated. The particle growth area, which is the area where the number of coated particles exceeds 0, was significantly expanded by decreasing the droplet diameter and increasing the gas velocity at appropriate ranges. The frequency of droplet and particle collisions increased, and average relative liquid content decreased with increasing particle growth area. By accelerating the injection of gas and reducing the viscosity of the injected liquid, the fluidization condition of the particles, which primarily affected the particle cycle frequency, could be enhanced. The improvement of particle growth area and particle cycle frequency allowed more particles to participate in growth and improved the quality of the grown particles. The proportion of small particles dramatically grew while the proportion of large particles reduced. The standard deviation of particle diameter decreased, and particle growth became more uniform. [ABSTRACT FROM AUTHOR]

Spout fluidized beds are important for industrial processing, and multiple-spout fluidized beds play an important role in chemical reactions. However, particle flow behaviors in multiple-spout fluidized beds are not well known in wet particle systems. In this study, the flow behaviors of particles were investigated in dry and humid multiple-spout fluidized beds using a discrete element method (DEM). The simulated spout fluidized beds are similar to the ones used in the Buijtenen et al.’s experiment (published in Chemical Engineering Science, 2011, 66(11): 2368–2376). In the reference, particle flow behaviors were measured and investigated by PIV and PEPT in multiple spout fluidized beds. In this work, the simulated results are compared with the experimental data in single and double spout fluidized beds from Buijtenen et al., and the time-averaged particle velocities are compared to validate the simulation method. In contrast, simulated results with a liquid content of 1% in the bed showed good agreement with the data in the experimental results with an air relative humidity of 50%. Different liquid contents of the particles were applied to investigate the particle flow behaviors in wet granular systems. The liquid bridge force had a strong influence on the flow behaviors of the particles in the dense region, which resulted in different hydrodynamic characteristics between the dry and wet particles. In addition, the drag force dominated the particle flow behavior in the dry and wet particle systems. Moreover, in a wet granular system, the mass particle fluxes decreased, and the fluctuation of the pressure drops increased with an increasing influence of the liquid bridge force on the particles. Furthermore, with an increasing liquid content, the energy fluctuation of the particles weakened gradually with less active motions. A comparison of the hydrodynamic flow behaviors in single-spout and double-spout fluidized beds was carried out as well. Comparisons of the solid circulation rate and the colliding characteristics between single-spout and double-spout fluidized beds were conducted. Particularly, a comparison of the mixing characteristics demonstrated that the particles were mixed more completely in a double-spout fluidized bed. Therefore, the double-spout fluidized bed could provide more adequate space for mass and heat transfer under the same condition. This was important in providing a theory for designing the industrial reactor. [ABSTRACT FROM AUTHOR]

The diversity in grain shapes results in complex interactions among flow, heat and mass transfer, which constrains the rational selection of process conditions to improve the uniformity of grain drying and enhance drying quality. A Computational Fluid Dynamics Discrete Element Method model, suitable for describing wet grains drying was developed. The effects of particle shape and fluid velocity on the interaction of flow, heat, and mass transfer were revealed, and an evaluation system for drying quality was established. The results showed that a nonlinear trend can be observed in the drying non-uniformity. The presence of localised high temperature regions within the bed and the differential interphase heat transfer under mesoscale structures were the primary factors contributing to the drying non-uniformity. When the aspect ratio increases from 1 to 3 and the fluid velocity increases from 3.3 m s−1 to 4.2 m s−1, the temperature lumped parameter increases by 13.16% and 17.48%, respectively. The mixing mechanism of particles underwent a transformation from a convective to a diffusive, which enhanced the circulation capacity of particles and improved the uniformity of the drying process. These research findings can provide guidance for improving the design, control and operation of fluidised drying equipment. • Cohesion and falling rate drying period were introduced into the grain drying model. • An evaluation system was established for the drying quality of wet grains. • The competition mechanism of particle mixing motion affects the drying quality. • Reducing the aspect ratio can promote the diffusion mixing of particles. [ABSTRACT FROM AUTHOR]

Hydrodynamic characteristics of dry particles and wet particles are obviously different in a dense gas-solid two-phase flow system. In this work, a modified LES-DEM (Large Eddy Simulation and Discrete Element Method) coupling approach has been used to investigate the flow behaviors of wet particles in a riser. The model of the simulated riser is similar to the one in Mathiesen et al.'s experiment with binary mixture of particles, and simulated results show good agreement with the experimental data. Different liquid contents of the particles are applied to find out the influence on particle behaviors. By contrast, bigger clusters are formed at the riser inlet and near the walls with increasing the liquid contents for the strong influence of liquid bridge on the cohesion between particles. Meanwhile, the translational axial velocities reduce with increasing the liquid content of the wet particles. Besides, the rotational axial velocity fluctuates in a wider range and distribute more randomly for the wet particles than that in the dry particle system. Moreover, the fluctuation of pressure drop increases with increasing the liquid content. Compared with the dry system, translational and rotational cluster granular temperatures for the wet particles distribute in a much wider range and the peak values increase. However, for both big particles and small particles the diameter distribution and particle volume fraction have a similar trend in both dry particle and wet particle systems. [ABSTRACT FROM AUTHOR]

Instantaneous force distributions at bubble boundaries in non-spherical dry and wet particle systems. [Display omitted] • The force distributions at bubble boundaries are analyzed to explain the influence mechanism of different shapes of bubbles in non-spherical dry and wet particle systems. • The effects of particle aspect ratio and liquid viscosities are investigated through the force analysis and factorial analysis. • With the increase of the particle aspect ratio, the bubble equivalent diameter gradually increases. • As the liquid viscosity increases, the bubble frequency gradually decreases. Fluidized beds with non-spherical dry and wet particles are widely used in industrial processes, and the mesoscale structure in the bed has an important influence. In this study, CFD-DEM simulations are performed to evaluate the flow behaviors and mesoscale structure in fluidized beds with non-spherical dry and wet particles. The accuracy of the model is validated by comparison with the results of the particle image velocimetry experiment. The force distributions at bubble boundaries are analyzed to explain the influence mechanism of different shapes of bubbles in non-spherical dry and wet particle systems. The factor analysis indicates the interaction of particle shape and viscous liquid on the translational and rotational kinetic energy of particles. When the bed height is low, as the particle aspect ratio increases, the bubble equivalent diameter gradually increases. In addition, as the liquid viscosity increases, the particle and bubble granular temperature gradually decrease, indicating the reduction of particle velocity fluctuate and the decrease of turbulent kinetic energy of bubble. These findings have guiding significance for the fluidization of non-spherical dry and wet particles and can be used to optimize related industrial processes. [ABSTRACT FROM AUTHOR]

The analysis of the flow hydrodynamics in jetting fluidized beds is of considerable significance as it is widely applied in pharmaceutical, food, chemical, and petrochemical industries. In this study, an improved sub grid scale (SGS) model in the large eddy simulation (LES) method has been used based on a discrete hard sphere particle model in a jetting fluidized bed. The comparison of simulation results with experimental data shows that the improved SGS method, with a mutative Smagorinsky constant SGS approach, results in a better agreement than when using an invariant constant. Furthermore the particle behavior was analyzed by a region-dependent analysis method to obtain the spatial relationship of the particle behavior. The tendency of macro particle behavior is quite different from that at the micro level, which means that the fluctuation in the bed is not related to the overall particle movement. The particle macro characteristics can be affected by multiple factors such as the type of gas in the jet, the type of particles and the bubbles in the bed. The granular temperature distribution, which is related to the micro turbulent movement, presents strong anisotropic features, and specific rules of bubble and particle behavior are deduced. The region-dependent analysis method is also discussed in this work. [ABSTRACT FROM AUTHOR]

As a radiant light source within the dynamic range of most spacecraft payloads, the Moon provides an excellent reference for on-orbit radiometric calibration. This research hinges on the precise simulation of lunar spectral irradiances and Earth-based Moon observation geometry. The paper leverages the Hapke model to simulate the temporal changes in lunar spectral irradiances, utilizing datasets obtained from the Lunar Reconnaissance Orbiter Camera (LROC). The research also details the transformation process from the lunar geographic coordinate system to the instantaneous projection coordinate system, thereby delineating the necessary observational geometry. The insights offered by this study have the potential to enhance future in-orbit spacecraft calibration procedures, thereby boosting the fidelity of data gathered from satellite observations. [ABSTRACT FROM AUTHOR]

In industrial processes, external energy fields are used to efficiently regulate the segregation behaviors of fluidized beds containing binary mixtures. We added a magnetic field force model to the discrete element method used to numerically investigate the segregation behaviors of binary component particles in magnetic field-fluidized beds. We explored the effects of magnetic field intensity, gradient, and superficial gas velocity on the extent of segregation. Bi -dispersed particles were separated under moderate magnetic field intensities, and segregation increased as the field intensity rose. However, if the field intensity became excessive, magnetic particles tended to agglomerate, reducing segregation. Thus, control of the magnetic field gradient within a certain range is key for effective particle segregation. [Display omitted] • A magnetic force field model is implemented into DEM model • The segregation degree of binary mixture can be enhanced under limited magnetic field intensities • The critical gas velocity for particle segregation is increased as the magnetic field gradient increases • A reasonable control of magnetic field gradient in a certain range is key to achieve the particle segregation. [ABSTRACT FROM AUTHOR]

[Display omitted] • Synthetic Fe 3 O 4 nanoparticles modified with bilayer surfactant obtained satisfying water dispersibility. • SO&SDS@Fe 3 O 4 produced significant promoting effects on methane hydrate formation rate and gas storage capacity. • The presence of magnetic field distinctly shortened the induction period of hydrate formation. Natural gas hydrate technologies have been perceived as promising methods of natural gas storage and transportation. Rapid achievement of high gas storage is the primary objective of hydrate applications. In this work, Fe 3 O 4 nanoparticles were synthesized and surface-modified by sodium oleate (SO) and sodium dodecyl sulfate (SDS) bilayer molecules (hereafter SDS&SO@Fe 3 O 4). The prepared SDS&SO@Fe 3 O 4 nanosuspensions exhibited sufficient water dispersion stability and were utilized as promoters for hydrate-based methane storage. The composite promoter exerted apparent higher methane storage capacity and hydrate formation rate than pure SDS solution. The maximum storage capacity with SDS&SO@Fe 3 O 4 was 156.5 v/v and the whole hydration process could be completed in only 32 min. Moreover, magnetic fields were successfully introduced to the reaction system, which exhibited a further promotion to hydrate formation due to the regular arrangement of SDS&SO@Fe 3 O 4. In the presence of a magnetic field, the major superiority was that induction period together with the total hydration period were prominently shortened and the induction time was negligible when the magnetic intensity exceeded 193 mT. In summary, the bilayer surfactant-coated Fe 3 O 4 nanoparticles developed in this study led to substantial improvements in methane hydrate formation kinetics and therefore have great potential in industrial applications involving hydrate-based energy storage. [ABSTRACT FROM AUTHOR]

• HPC smart window photothermal performance was enhanced by adding rGO. • Each additional milliliter of rGO increased the stable temperature by 3.13 °C. • HPC solution exhibits high luminous transmittance and solar modulation ability. • NaCl lowered the lower critical solution temperature of HPC by 2.50 °C/1 wt%. The ability of hydroxypropyl cellulose (HPC)-based smart windows to dynamically regulate incident light and reduce energy consumption is of great interest. Although HPC is environmentally friendly due to its nontoxicity and biodegradability, commercial applications are limited due to its relatively high lower critical solution temperature (LCST). In this paper, reduced graphene oxide (rGO) was added to HPC solutions to enhance photothermal performance and access higher solution temperatures under sunlight illumination. For a 5-mL volume of HPC solution (0.5 wt%), each milliliter of rGO solution (0.1 mg/mL) increased the solution temperature by 3.13 °C. The tadpole-shaped fibers were hydrophilic below the LCST but became hydrophobic and tended to form net structures above the phase-change temperature. Water molecules were expelled from the web structure at the same time. Our work broadens the applications of HPC in smart windows. [ABSTRACT FROM AUTHOR]

• Flow and heat mass performance of wet particles was analyzed using discrete element method. • Enhancing momentum exchange could increase heat and mass transfer capabilities. • Different heat transfer pathways during the drying process have been analyzed quantitatively. • Underlying mechanism of weaker heat transfer characteristic near walls is investigated. • Graphical abstract. • Probability distribution function (PDF) of the particle concentration. Drying treatment of wet particle system is an important industrial process, involving flow and heat mass transfer multiple process. It has been widely used in many industrial processes, such as renewable energy usage and food storage. In this work, we numerically analyzed the heat mass transfer and flow behaviors of a wet particle system. Liquid volume-varying bridge force model was developed, and taking into account water evaporation. The numerical model data were validated, and we found good agreement with respect to hot particles during the cooling period. For a better explanation of the interactions among flow, heat and mass transfer, we calculated the distribution of particle Nusselt (Nu p) and Sherwood (Sh p) as a function of Reynolds (Re p), and interaction relationships between multiple processes were evaluated. Moreover, near the walls, lower gas velocity and higher particle volume fraction resulted in weak heat transfer characteristics. If this heat transfer coefficient is improved and addressed appropriately, emulsion phase, which accounts for major proportion as potential underlying cause of forming agglomeration structure, will be effectively avoided and kept to accepted level. This is important for the wet particle handling in actual industrial processes. Probability distribution function (PDF) of the particle concentration Image, graphical abstract [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN) was adopted to predict and analyze the relationship of drag coefficient, Reynolds number, and sphericity for non-spherical particles in dense gas–solid two-phase flow. We first employed Back Propagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN) to predict drag coefficients based on experimental results (Pettyjohn, 1948; Yow et al., 2005). Comparisons between simulation and experimental results indicate that RBFNN efficiently predicts the drag coefficients with as high precision as BPNN. Furthermore, we made predictions and analyses of drag coefficients under different sphericities employing Radial basis function neural network. Results reveal that artificial neural network is applicable in predicting and investigating drag coefficient in gas–solid non-spherical particulate systems. Based on the predicted results of drag coefficients, we conducted a curve fitting of drag coefficient, Reynolds number, and particle sphericity, obtaining a correlation on drag coefficient. Incorporating the drag coefficient correlation with Syamlal-O'Brien and Gidaspow-blend model, simulation on gas–solid two-phase flow by Eulerian–Eulerian model was carried out from fixed beds to bubbling fluidized beds. Simulation pressure drop are compared with experimental results obtaining a good agreement with each other, which indicates that the drag force of non-spherical particles in a gas–solid system could be predicted reasonably by an artificial neural network method. This work provides a reference for predicting drag coefficients of particles with complex shape in gas–solid two-phase system. Unlabelled Image • Artificial neural network was applied to predict drag coefficient. • Comparisons between Back propagation neural network and Radial basis function neural network were made. • A curve fitting of drag coefficient, Reynolds number, and sphericity was conducted by artificial neural network. • A drag correlation combined with Syamlal-O'Brien and Gidaspow-blend model was proposed. • Hydrodynamic behaviors of spherical and non-spherical particles were simulated using the proposed model. [ABSTRACT FROM AUTHOR]

Batch normalization (BN) enables us to train various deep neural networks faster. However, the training accuracy will be significantly influenced with the decrease of input mini-batch size. To increase the model accuracy, a global mean and variance among all the input batch can be used, nevertheless communication across all devices is required in each BN layer, which reduces the training speed greatly. To address this problem, we propose progressive batch normalization, which can achieve a good balance between model accuracy and efficiency in multiple-GPU training. Experimental results show that our algorithm can obtain significant performance improvement over traditional BN without data synchronization across GPUs, achieving up to 18.4% improvement on training DeepLab for semantic segmentation task across 8 GPUs. [ABSTRACT FROM AUTHOR]

Memristor-based computation provides a promising solution to boost the power efficiency of the neuromorphic computing system. However, a behavior-level memristor-based neuromorphic computing simulator, which can model the performance and realize an early stage design space exploration, is still missing. In this paper, we propose a simulation platform for the memristor-based neuromorphic system, called MNSIM. A hierarchical structure for memristor-based neuromorphic computing accelerator is proposed to provides flexible interfaces for customization. A detailed reference design is provided for large-scale applications. A behavior-level computing accuracy model is incorporated to evaluate the computing error rate affected by interconnect lines and nonideal device factors. Experimental results show that MNSIM achieves over 7000 times speed-up than SPICE simulation. MNSIM can optimize the design and estimate the tradeoff relationships among different performance metrics for users. [ABSTRACT FROM PUBLISHER]

In this work, a hard sphere particle method for non-spherical particles is applied to study particle behaviors in a bubbling fluidized bed. The influence of the non-spherical particle method and the restitution coefficient are examined. The particle behaviors, including the overall fluidization state (distributions of particle translational and rotational velocities, void fraction, etc.), bubble and particle fluctuations (particle and bubble granular temperatures), are studied. Specific variation rules which are difficult to measure experimentally are presented. As the bubble boundaries in the non-spherical particle system are not as clear as in the spherical case, there is a stronger fluctuation movement of bubbles, which also dominates the particle turbulent movement. The absolute values of particle translational and rotational velocities increase when the restitution coefficient decreases. And it affects the periodic fluctuation characteristics of gas-solid interactions. The variation rules for bubbles and particles as a function of the restitution coefficient are different. [ABSTRACT FROM AUTHOR]

Gas hydrates have essential industrial and climate implications in energy exploitation and novel technologies. Hydrate nucleation and growth typically develop under substrate-contact conditions; surface properties affect hydrate formation pathways. Microscopic insights into the effects of solid surfaces on hydrate formation would improve reservoir exploitation and the synthesis and regulation of promoters for hydrate-based methane storage. We thus performed microsecond-scale molecular dynamic simulations to investigate methane hydrate formation in CH 4 –H 2 O-substrate systems varying substrate surface hydrophilicity. The evolution of several parameters suggested that the methane storage capacity was controlled by the proportions of surface methyl and hydroxyl groups. The mutually coordinated guest (MCG) was a superior indicator of hydrate nuclei since it recognized all methane separated by water rings in hydrate structures. As surface hydrophilicity decreased, the size of the crystal nucleus shrank, and the amount of captured methane decreased; nevertheless, the nucleation pathways shifted from amorphous to crystalline. Moreover, given the crucial interactions between MCG/four-body order parameters and interaction energy/host-guest local distributions, the underlying mechanisms of hydrate formation were well-represented using two- and three-step microscopic models. Our findings show how solid surface properties influence multi-stage hydrate nucleation and will contribute to developing additives that efficiently promote hydrate formation. [Display omitted] • Multi-stage microscopic models for CH 4 –H 2 O-substrate systems were proposed. • Strong surface hydrophilicity tends to yield large but amorphous hydrate nuclei. • A crystalline nucleus was formed via layer-by-layer growth between –CH 3 -silicas. • Host-guest's adsorption and distribution are governed by surface hydrophilicity. [ABSTRACT FROM AUTHOR]

The dynamic behavior of wet cohesive particles is greatly different from that of dry particles in a spout–fluid bed. A modified discrete element model (DEM) for wet particles is established by adding an additional module to consider the presence of the liquid bridge. DEM simulations are conducted for the dry and wet granular systems and the geometry of the bed is the same as the experimental one of Link et al. The influences of drag models and liquid contents are investigated. It is found that the Koch–Hill drag model predicts a dominant peak, the same as the measured results of Link et al., in the frequency domain of the dynamic pressure drop under Case A. However, the Gidaspow drag model fails to catch this phenomenon. With an increase of the liquid content, the fluctuation range of the pressure drop increases firstly and then decreases rapidly under Case A, but it monotonically increases under Case B. In the spout–fluid bed, dead zones, where the particles are de-fluidized, appear at the corners. The inclined angle and the area of the dead zone expand with the increase of the liquid content, which makes the value of the final Lacey mixing index decrease. [ABSTRACT FROM AUTHOR]

During convective drying of wet granular materials like germinated brown rice (GBR), excessively high drying rates can lead to crack formation in the particles. However, cracking characteristics of GBR during exposure to different drying stages, unclear especially the critical conditions for cracking. In this study, experiments and numerical simulations were performed in an attempt to predict the cracking behavior of GBR under different drying stages through intragranular drying kinetics. Our results showed that the variations in cracking probability could be divided into three different stages. This phenomenon was dominated by the close relationship between particle strength and moisture gradient. The cumulative effect of moisture gradient is one of the important leading to crack initiation and evolution. Moreover, a prediction model of cracking probability as a function of drying time was proposed. Finally, the critical water evaporation coefficient was introduced to quantify the cracking safe region of GBR. [Display omitted] • Cracking mechanism of GBR was obtained under various convection drying stages. • Cracking characteristics of GBR was predicted based on intragranular drying kinetics. • An empirical model of cracking probability as a function with time was proposed. • Critical water evaporation coefficient was proposed to evaluate cracking safe region. [ABSTRACT FROM AUTHOR]

Emerging resistive random access memory (RRAM) has shown the great potential of in-memory processing capability, and thus attracts considerable research interests in accelerating memory-intensive applications, such as neural networks (NNs). However, the accuracy of RRAM-based NN computing can degrade significantly, due to the intrinsic statistical variations of the resistance of RRAM cells. In this article, we propose SIGHT, a synergistic algorithm-architecture fault-tolerant framework, to holistically address this issue. Specifically, we consider three major types of faults for RRAM computing: 1) nonlinear resistance distribution; 2) static variation; and 3) dynamic variation. From the algorithm level, we propose a resistance-aware quantization to compel the NN parameters to follow the exact nonlinear resistance distribution as RRAM, and introduce an input regulation technique to compensate for RRAM variations. We also propose a selective weight refreshing scheme to address the dynamic variation issue that occurs at runtime. From the architecture level, we propose a general and low-cost architecture accordingly for supporting our fault-tolerant scheme. Our evaluation demonstrates almost no accuracy loss for our three fault-tolerant algorithms, and the proposed SIGHT architecture incurs performance overhead as little as 7.14%. [ABSTRACT FROM AUTHOR]

A spouted bed containing non-spherical particles was investigated numerically using the non-spherical discrete hard-sphere method, and experimentally using particle image velocimetry. The experimental and simulated particle velocity distributions were in agreement. Different operating conditions were used to investigate the instability of the spouted bed. Certain conditions created an S-shaped gas channel in the center of the bed, which proved the instability of non-spherical particles. The motion of micro-scale particles and bubbles was studied to determine their potential relationship with instability. It was found that the granular temperature distribution of the particles and bubbles was associated with the gas channel region. The behaviors of bubbles differed from those of particles. These results contribute to an understanding of instability mechanisms in spouted beds. Unlabelled Image • Experiment based on PIV system was conducted for non-spherical particles. • Non-spherical particle behaviors were simulated by DPM. • Instability phenomenon was studied in spouted bed with non-spherical particles. [ABSTRACT FROM AUTHOR]

• A two-fluid model with sound field was established. • The sound field enhances the frequency of interparticle collisions. • The sound field strengthens the momentum exchange between gas and solid. Sound field assisted fluidized bed is a promising approach to regulate particle behaviors in gas-solid two-phase flows. In this study, an acoustic model was built and coupled with Euler-Euler two-fluid model to study macroscopic and microscopic flow characteristics of Geldart-A particles. The influence of acoustic frequencies and sound pressure levels on particle behavior was analyzed. The sound field has little influence on the macroscopic flow characteristics of the particles, but a significant influence on the microscopic characteristics. With the increasing acoustic frequencies and sound pressure levels, the bubble granular temperature at the top of the fluidized bed decreases, and the periodicity of it is becoming irregular. Particle granular temperature increases with the increasing acoustic frequencies and sound pressure levels. In the near wall region, the sound field affected the particles to a smaller extent. This study provides a reference for exploring the sound-field regulation of particles and bubble behaviors. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new strategy of pre-freezing yields AC with negligible hydrate induction period. • Novel hydrate morphologies of massive centimeter-scale fibers were first observed. • Exceptional high and fast methane uptake in clathrate hydrates was achieved. • Combined effect of ice induction and nanoconfinement facilitates hydrate synthesis. Clathrate hydrates are crystalline solids distinguished by their capacity to host many guest molecules. Natural gas stored in hydrate form, also known as solidified natural gas, is gaining popularity considering its non-explosive nature, long-term storage capacity, and high energy density. Wet substrates of high specific areas with abundant pores are widely used for hydrate synthesis. Here, we investigate the hydrate morphologies and kinetics of pre-humidified activated carbon (AC) systems. Inspired by the hydrate-favorable environment of natural permafrost, we subjected water-containing activated carbon samples to freezing pretreatment. Intriguingly, the resulting hydrates exhibited a novel fiber-dominated growth pattern, without an (or with a negligible) induction period. The effects of water content, carbon particle size, and pressure driving force were comprehensively evaluated to determine the optimal hydrate kinetics. Utilizing the combined effects of ice induction and nanoconfinement, we successfully produced hydrates with an exceptional high methane storage capacity (168 ± 3.54 v/v) using a short duration (90% completion within 30.67 ± 4.25 min), which was achieved in 2.5–3 mm activated carbons with 1.2 water/AC mass ratio using an overpressure of 5.5 MPa. This innovation may represent an important advance that could facilitate the development of solidified natural gas technology. [ABSTRACT FROM AUTHOR]

• A two-step K-means cluster identification method is proposed. • Captured the phenomenon of particles attaching and detaching a cluster. • The basic laws of cluster identification are summarized. Cluster evolution dominates the gas–solid interaction and heat and mass transfer in risers, but cluster identification is still a challenge. Based on the unsupervised artificial intelligence approach, a two-step K-means cluster identification method is proposed to distinguish the clusters in a riser. With the consideration of the instantaneous particle motion and position information, it is able to capture the phenomenon of particles attaching and detaching a cluster. The estimated solid fraction threshold is located in the region where the slope of the solid fraction is the smallest, and approximately makes the numbers of particles detaching and attaching the clusters equal. The solid fraction threshold is about 0.204. It is found that the particles penetrating a cluster are in a more violent turbulent movement. Larger gas velocity and solid flux result in stronger interactions between clusters. The proposed method provides a new way to identify the cluster. [ABSTRACT FROM AUTHOR]

In this work, a gas–solid two-phase riser was analyzed using a discrete hard sphere particle model (DPM) for the solid phase and an improved sub-grid scale stress (SGS) model using the large eddy simulation (LES) method applied for the gas phase. A binary mixture of small and large particles was used in the simulation. Simulation results were compared with experimental data by Mathiesen et al. and showed good agreement. A new parameter, cluster granular temperature, was proposed to analyze the particle macro-scale energy fluctuation. Various drag models, restitution coefficients and superficial gas velocities were applied to find out the influence on particle behaviors. It is found that the simulation by the mutative Smagorinsky constant model has advantages over invariant Smagorinsky constant model. The drag models and the restitution coefficients affect the particle movement and cluster granular temperature to some extent. The superficial gas velocity has a great effect on cluster granular temperature, especially for large particles. The distributions of rotational particle characteristics, such as rotational velocity and rotational cluster granular temperature, were specifically influenced by different SGS methods. [ABSTRACT FROM AUTHOR]