Optimal Packing Research Articles

Numerical investigation on the densification of granulated porous indium tin oxide powders before compaction

Dense and uniform initial packing structures of powders are of key significance in powder metallurgy process, which can provide the precondition for the fabrication of high-performance components with low cost and high efficiency. In this paper, the packing densification of indium tin oxide (ITO) granulated micro powders with continuous size distributions and internal porosity under three-dimensional vibrations was numerically investigated by discrete element method (DEM). Firstly, the DEM model was validated by physical experiments, and the contact parameters between particles and the porosity therein were determined. Then the effects of operating parameters on the macro/micro properties (e.g., packing density, coordination number (CN), pores, and forces) of the packing structures were analyzed. Results show that the mean CN cannot intuitively reflect the packing structure due to the complex and varied inter-particle contacts caused by the inhomogeneity of the particle size in a multi-sized granular system. Additionally, the excellent packing structure of porous ITO particles is always obtained from low amplitude when the vibration intensity is the same. Corresponding mechanism analyses reveal that higher amplitude normally causes strong convective diffusion, leading to more pores in the packing. This increases the probability of wedging effect occurring during settling, thereby reducing the packing density. However, optimal packing quality with low amplitude can only be achieved when coupled with high frequency. Therefore, applying vibration is an effective way to achieve dense packing of porous spherical particles, especially at low amplitude and high-frequency operating conditions.

Optimization of Fischer–Tropsch microchannel reactor using computational fluid dynamics and enveloped Bayesian optimization

We propose computational fluid dynamics (CFD)-enveloped Bayesian optimization (EBO), a novel optimizer that integrates EBO with CFD to reduce the required CFD simulations by utilizing previous optimization data. The proposed optimizer was applied to determine the optimal catalyst packing ratio of the Fischer–Tropsch microchannel reactor that minimizes the maximum temperature and maximizes the productivity of long-chain hydrocarbons by utilizing the CFD model. The obtained results indicate that the number of iterations required to reach the optimal points is lower than that of BO, and the optimal result exhibits a 5% improvement from the initial condition. The optimizer was evaluated across various catalyst packing cases to assess its robustness. Nevertheless, the proposed optimizer was consistently able to reach optimal points that BO could not achieve. We anticipate that this optimizer can be widely applied to optimize the operating condition of a chemical reactor in the presence of previous optimization data.

Additively manufactured structures with powder inclusions for controllable dissipation: The critical influence of packing density

Particle dampers represent a simple yet effective means to reduce unwanted oscillations when attached to structural components. Powder bed fusion additive manufacturing of metals allows to integrate particle inclusions of arbitrary shape, size, and spatial distribution directly into bulk material, giving rise to novel metamaterials with controllable dissipation without the need for additional external damping devices. At present, however, it is not well understood how the degree of dissipation is influenced by the properties of the enclosed powder packing. In the present work, a two-way coupled discrete element - finite element model is proposed allowing for the first time to consistently describe the interaction between oscillating deformable structures and enclosed powder packings, while existing works have only considered rigid enclosures so far. As fundamental test case, the free oscillations of a hollow cantilever beam filled with various powder packings differing in packing density, particle size, and surface properties are considered to systematically study these factors of influence. Critically, it is found that the damping characteristics strongly depend on the packing density of the enclosed powder and that an optimal packing density exists at which the dissipation is maximized. Moreover, it is found that the influence of (absolute) particle size on dissipation is rather small. First-order analytical models for different deformation modes of such powder cavities are derived to shed light on this observation.

Toward the Ultimate Limit of Analyte Detection, in Graphene-Based Field-Effect Transistors.

The ultimate sensitivity of field-effect-transistor (FET)-based devices for ionic species detection is of great interest, given that such devices are capable of monitoring single-electron-level modulations. It is shown here, from both theoretical and experimental perspectives, that for such ultimate limits to be approached the thermodynamic as well as kinetic characteristics of the (FET surface)-(linker)-(ion-receptor) ensemble must be considered. The sensitivity was probed in terms of optimal packing of the ensemble, through a minimal charge state/capacitance point of view and atomic force microscopy. Through the fine-tuning of the linker and receptor interaction with the sensing surface, a record limit of detection as well as specificity in the femtomolar range, orders of magnitude better than previously obtained and in excellent accord with prediction, was observed.

Evolving topological order in the postnatal visceral pleura.

Changes in epithelial cell shape reflects optimal cell packing and the minimization of surface free energy, but also cell-cell interactions, cell proliferation, and cytoskeletal rearrangements. Here, we studied the structure of the rat pleura in the first 15 days after birth. After pleural isolation and image segmentation, the analysis demonstrated a progression of epithelial order from postnatal day 1 (P1) to P15. The cells with the largest surface area and greatest shape variability were observed at P1. In contrast, the cells with the smallest surface area and most shape consistency were observed at P15. A comparison of polygonal cell geometries demonstrated progressive optimization with an increase in the number of hexagons (six-sided) as well as five-sided and seven-sided polygons. Analysis of the epithelial organization with Voronoi tessellations and graphlet motif frequencies demonstrated a developmental path strikingly distinct from mathematical and natural reference paths. Graph Theory analysis of cell connectivity demonstrated a progressive decrease in network heterogeneity and clustering coefficient from P1 to P15. We conclude that the rat pleura undergoes a striking change in pleural structure from P1 to P15. Further, a geometric and network-based approach can provide a quantitative characterization of these developmental changes.

Modelling Water Absorption Index and Compressive Strength of Paving Stone Composites with Polyethylene Terephthalate (PET) as Total Binder Replacement

Packing density method of mix design of concrete is new method to design different types of concrete. The mix design obtained from the packing density method has suitable workability, maximum packing density and minimum voids ratio. The geometrical characteristics like shape, size and proportion of fine aggregate and coarse aggregate affect packing density. The objective of this research is to study the mix design of concrete using packing density method and develop the correlation between bulk density, packing density and voids ratio. In this work large number of trail to decide the proportion of aggregate for that optimum bulk density and packing density calculated for different varying proportion of 20 mm: 12.5 mm coarse aggregate (i.e. 90:10, 80:20,70:30, 65:35, 60:40 and 50:50) and for varying proportion coarse aggregate: fine aggregate (i.e. 90:10, 80:20, 70:30, 60:40, 55:45 and 50:50). To finalize the mix design using packing density method also varies the percentage of excess cement paste (i.e. 5%, 7%, 9%, 10%, 11% and 12%). Tests were performed for the properties of fresh concrete like workability test (Slump cone) and hardened concrete like compressive strength, split- tensile strength, pull-out test, rebound hammer and flexural Test etc. tests were determined at 7, 14 and 28 days. The obtained results for above mentioned test using packing density method at 9% of excess cement paste are satisfying the standard results.

Experimental Studyof M20 Grade Concrete Using Packing Density Method

Packingdensitymethodof mixdesign of concreteisnewmethodtodesigndifferent types of concrete. The mix design obtained from the packing density method hassuitableworkability,maximumpackingdensityandminimumvoidsratio.Thegeometrical characteristics like shape, size and proportion of fine aggregate and coarseaggregate affect packing density. The objective of this research is to study the mix designof concrete using packing density method anddevelop thecorrelation between bulkdensity, packing density and voids ratio. In this work large number of trail to decide theproportion of aggregate for that optimum bulk density and packing density calculated fordifferentvaryingproportionof20mm:12.5mmcoarseaggregate(i.e.90:10,80:20,70:30,65:35,60:40and50:50)andforvaryingproportioncoarse aggregate:fine aggregate (i.e. 90:10, 80:20, 70:30, 60:40, 55:45 and50:50). To finalize the mix designusing packing density method also varies the percentage of excess cement paste (i.e. 5%,7%, 9%, 10%, 11% and 12%). Tests were performed for the properties of fresh concretelike workability test (Slump cone) and hardened concrete like compressive strength, split-tensilestrength,pull-outtest,reboundhammerandflexuralTestetc.testsweredetermined at 7, 14 and 28 days. The obtained results for above mentioned test usingpackingdensitymethodat9%ofexcesscementpastearesatisfyingthestandardresults.

Heuristics Integrated Deep Reinforcement Learning for Online 3D Bin Packing

Online 3D Bin Packing Problem (3D-BPP) has a wide range of industrial applications and there is an emerging research interest in learning optimal bin packing policy and deploying it for real logistics applications. From the heuristic methods to the deep reinforcement learning (DRL) methods, the previous works have proposed many solutions to solve the online 3D-BPP. However, none of them have studied what and how heuristics can be modelled into DRL to build a more effective and practical bin packing pipeline. In this work, we thoroughly investigate what heuristics can be used in online 3D-BPP and how to effectively integrate the heuristics with the DRL. First, we design 3 different heuristics based on the physical rules of the real world and the experiences of the human packers, including the Physics-Heuristics, the Packing-Heuristics and the Unpacking-Heuristics. Second, we model the 3 types of heuristics into the DRL framework and propose a novel heuristic DRL method to solve the online 3D-BPP. Extensive experimental results show that our method achieves state-of-the-art bin packing performance and the resulting real-world system is able to reliably finish the bin packing task in real logistics scenarios. Supplementary video is available at https://www.youtube.com/watch?v=x8GpmEELq18. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The rapid growth of e-commerce has significantly increased the burden of human packers in logistic warehouses, where the workers need to pick the products from a conveyor and pack them into bins (i.e. the online 3D bin packing). Thus it is of great importance to develop intelligent robotic systems to replace human labor, which is a long-standing topic in the field of control and automation science. This paper makes a substantial contribution to the related field by studying the online 3D bin packing in terms of both the theory and practice. On the one hand, the simulated experiments suggest that the presented algorithm significantly improves the space utilization of bin packing. On the other hand, the robotic system developed based on the proposed method can favourably finish the bin packing task in real logistics scenarios, demonstrating the practical use of our approach. Consequently, the approach proposed in this paper is totally applicable in logistic warehouses and is promising to drastically improve the working efficiency of the product packing in real warehouses. In the future, we will extend the presented approach to pack irregular-shaped objects and then facilitate more logistics applications.

Brownian motors powered by nonreciprocal interactions.

Traditional models for molecular (Brownian) motors predominantly depend on nonequilibrium driving, while particle interactions rigorously adhere to Newton's third law. However, numerous living and natural systems at various scales seem to defy this well-established law. In this study, we investigated the transport of mixed Brownian particles in a two-dimensional ratchet potential with nonreciprocal interactions. Our findings reveal that these nonreciprocal interactions can introduce a zero-mean nonequilibrium driving force. This force is capable of disrupting the thermodynamic equilibrium and inducing directed motion. The direction of this motion is determined by the asymmetry of the potential. Interestingly, the average velocity is a peaked function of the degree of nonreciprocity, while the effective diffusion consistently increases with the increase of nonreciprocity. There exists an optimal temperature or packing fraction at which the average velocity reaches its maximum value. We share a mechanism for particle rectification, devoid of particle-autonomous nonequilibrium drive, with potential usage in systems characterized by nonreciprocal interactions.

Study on the optimisation of cement and binder content to develop a sustainable high-performance concrete

According to ACI concrete terminology, “High Performance concrete (HPC) is defined as a concrete meeting special combinations of performance and uniformity requirements that cannot always be achieved routinely using conventional constituents and normal mixing, placing, and curing practices”. It is a new generation of concrete that has been evolved recently and are well received for its exceptional performance of strength and durability. Due to these characteristics, HPC are utilized in the construction of bridges, hydropower structures, pavements, mass concrete projects etc. HPC is a cementitious composite material composed of an optimized gradation of granular constituents, a water-to-binder ratio of less than 0.3, a high percentage of discontinuous internal fiber reinforcement and HPC can hold compressive strength of more than 60 MPa. HPC considered in this study is made with high-strength steel fibers, fine sand, cement, mineral and chemical admixtures and water. The conventional mix design used for normal concrete cannot be adopted for HPC because of the high compressive strength to be achieved. The main objective is to develop a sustainable HPC mix with optimum particle packing density to achieve the desired strength with the lowest cement and binder content. As part of the investigation, the impact of different constituents of HPC such as cement content and binder content on the compressive strength were examined. Also, the optimum particle packing density of mixes with different amount of cement and binder content were studied using EMMA software. From the results it is observed that the increase in cement or binder content beyond a particular level will have minimal impact on the compressive strength.

Optimal Sizing and Analysis of Hybrid Battery Packs for Electric Racing Cars

The electrification trend is spreading not only in the field of traditional vehicles, but also in the racing one, with challenging power and range demands. As an example, the next generation of Formula E will increase power up to 600 kW, almost triplicating the actual standards. In this paper, we investigate the potential benefits of hybrid battery packs in a racing scenario. Hybrid battery packs combine different cell technologies aiming at exploiting their respective high-power and high-energy densities. To this purpose, we formulate a co-design optimization problem that finds the optimal hybrid battery pack configuration to minimize the race time, on a given circuit. As a case study, the next generation of Formula E on the 2021 Rome Formula E ePrix is considered. Results show a significant gain in terms of race time, compared to a single cell chemistry battery pack. Moreover, the analysis points out interesting results on the optimal power split strategy and the role of mechanical brakes.

Proportions of conical motifs: Optimal packing via the spherical image.

We present a scheme for calculating the shape of two well-known conical motifs: the d-Cone and the e-Cone. Each begins as a thin, flat disk, before buckling during loading into a deformed shape with distinctive, asymmetrical conical features and a localised apex. Various deformed equilibrium models rightly assume a developable shape, with a particular focus on determining how much of the disk detaches from how it is supported during buckling; they are, nevertheless, extensively curated analytically, and must confront (some, ingeniously) the question of singular, viz., infinite properties at the conical apex. In this study, we find an approximate description of shape that reveals the extent of detachment, from an analogous mobile vertex that packages optimally according to its constraints. To this end, we further develop the usage of Gauss's Mapping and the associated spherical image, which has been used previously, but only to confirm known properties of deformed shape. Despite the simplicity of our approach, remarkably good predictions are availed, perhaps because such problems of extreme deformation are geometrically (rather than equilibrium) dominated.

Hemodynamic analysis of coil filled patient-specific middle cerebral artery aneurysm using porous medium approach

Cerebral aneurysms are bulges of an artery, which could be life-threatening when ruptured. Depending on their size, shape, and location, they need to be managed either through clipping or an endovascular coiling intervention. When coiled, reduced hemodynamic activity enables the coil to get thrombosed and achieve flow stasis. However, some coils delivered into the aneurysm tend to prolapse into the parent vessel and cause stroke due to obstruction and embolization. The recurrence of an aneurysm after endovascular coiling is of concern in the treatment of wide necked aneurysms. The initial packing density or improper coiling of the aneurysm and its relation to recurrence remains uncertain. This study investigates the influence of reduction in coil fill volume and packing density on the aneurysm recurrence using hemodynamic parameters by analyzing its flow features. Finite element method based commercial computational fluid dynamics solver is employed for performing patient-specific simulations for the coil filled aneurysm. The present approach uses porous medium based formulation. The numerical simulations show that any reduction below the optimal coil fill volume and packing density inside the aneurysm increases the velocity magnitude, wall shear stress, time-averaged wall shear stress, and spatial gradient of wall shear stress and reduces the relative residence time. The hemodynamic parameters and flow features suggest that a reduction in the coil packing density inside the aneurysm increases the chances of aneurysm recurrence. Hence, an assessment on how to achieve optimal coil fill volume and packing density is critical in reducing the risk of aneurysm recurrence.

On the problem of the densest packing of spherical segments into a sphere

The paper considers a particular variant of the classical optimal packing problem when the container is a sphere, the packed elements are equal spherical caps, and the optimality criterion is to maximize their geodesic radius. At the same time, we deal with a special integral metric to determine the distance between points, which becomes Euclidean in the simplest case. We propose a heuristic numerical algorithm based on the construction of spherical Voronoi diagrams, which makes it possible to obtain a locally optimal solution to the problem under consideration. Numerical calculations show the operability and effectiveness of the proposed method and allow us to draw some conclusions about the properties of packings.

Modeling and Experimental Study of Packing Density of Foam Glass Concrete

The paper deals with the process of production and properties of foam glass concrete – a composite material consisting of modified gypsum binder and granulated foam glass as an aggregate. Foam glass concrete has high strength, durability, environmental friendliness and low thermal conductivity, which makes it a promising material for wall structures. Optimal packing densities were obtained by using programs developed by the authors in the Python programming language to calculate the optimal diameters of three fractions of granular foam glass in an orthogonal or hexagonal packing model. The compressive and flexural strength and thermal conductivity coefficient were investigated on samples of different compositions. The value of the thermal conductivity coefficient of the foam glass concrete was reduced due to the air-entraining additive. In conclusion, the optimum composition of foam glass concrete was proposed taking into account all investigated parameters.

Porous-media model based particle-resolved simulation of a fixed bed with olefin catalytic cracking reaction

Understanding the interstitial reacting flow in particle packing structure and its relation with the reaction performance is crucial to designing a highly efficient fixed bed reactor. This study uses a particle-resolved approach with a porous-media model-based immersed boundary method to simulate an olefin catalytic cracking (OCC) fixed bed with 150 cylindrical particles by combining a six-lump kinetic model and mass transfer model. The effects of weight hourly space velocity (WHSV) and particle shape are further investigated in analyzing conversion, concentration variation, flow structure and pressure drop. The study reveals a three-stage pattern in variation of product concentration along the packing structure height with different WHSVs, providing the basis for determining the optimal packing height. It also finds that the hollow-cylindrical particle manifests the best reaction performance while the cylindrical particle the worst. The coordination of surface area and pressure drop of the packing structure determines the OCC reaction effectiveness.

Design and practice of an emergency blood allocation system based on radiofrequency identification technology.

Currently, blood allocation is solely done by scanning barcode labels for each bag of blood, with low efficiency. However, the rapid allocation of emergency blood is required owing to the rapid increase in blood consumption during unconventional emergencies. This study aimed to design and apply radiofrequency identification (RFID) technology for the rapid allocation of blood in batches with advantages in time, efficiency and accuracy. A blood emergency allocation system based on RFID technology was designed using a multi-label anti-collision algorithm and tested with automatic information check, a comparative study of scanning speed and accuracy, data analysis and other methods. The optimal packing quantities of suspended red blood cells and fresh frozen plasma were 40 and 50 bags per box, respectively. The application of rapid batch allocation of blood using RFID technology was performed, and the data sent and received by RFID scanning and barcode scanning were compared. The designed RFID blood emergency allocation system could effectively achieve the rapid and batch allocation of emergency blood and has the advantages of stability, efficiency and accuracy in blood emergency allocation and management.

Optimal Online Bin Packing Algorithms for Some Cases with Two Item Sizes

Optimal Online Bin Packing Algorithms for Some Cases with Two Item Sizes

Influence of Packing Density on Electrochemical Performance of Lignin

Abstract Bio-based carbonaceous materials such as lignin are taking up increasingly important roles in energy storage. However, the wide spectrum of molecular identity and structure of lignin lead to inconsistent structure, properties, and performance because of its source, extraction, and pretreatment processes. In this research, we investigate the effects of packing density on the electrochemical performance for lignin. Samples were synthesized using lignin as the electroactive material. Four distinct types of lignin, i.e., alkali lignin microparticles (MP), alkali lignin nanoparticles (NP), dealkaline lignin MP, and dealkaline lignin NP, were used in this study. They were studied through varying the packing density and its subsequent effects on the electrochemical performance. The scanning electron microscopy and Fourier-transform infrared spectrometry analysis were conducted to obtain morphology and chemical compositions. Cyclic voltammetry and cyclic charge discharge tests were conducted to evaluate the polarization resistance. The optimal packing density was found to be in the range of 0.35–0.50 g cm−3 for all samples. In the optimal packing density range, dealkaline lignin NP displayed the highest electrochemical performance with a maximum specific capacitance of 1,159 mF g−1, an energy density of 19.097 Wh kg−1, and a power density of 11.05 kW kg−1. The optimization of packing density would enable further development of lignin-containing energy storage devices such as supercapacitors.

Cold recycling mix design approach targeting permanent deformation resistance

Cold recycling technology offers a sustainable construction pathway for pavements with a potential of recycling up to 100% of the existing in-situ materials. This study introduces a new approach to design emulsion-based cold recycling mixtures. The proposed mix design approach channels the design efforts to optimize the mixture’s components and evaluate its long-term performance. The elements of the proposed design include altering aggregate gradation to achieve optimum packing; a method to selecting an optimum moisture content that is derived from both liquid components in the mixture; and selecting the residual asphalt content based on the mixture’s resistance to permanent deformation. To evaluate the reliability of the proposed mix design method, an optimum mixing water content was derived for an optimum RAP gradation. The effect of varying the emulsion content while keeping the mixing water content constant was evaluated on the mixture’s volumetrics, strength, and permanent deformation behavior. A conventional triaxial compression stress path was applied on samples compacted with several residual asphalt contents. The triaxial test results were compared with results obtained from Hamburg Wheel Tracking Test and Asphalt Pavement Analyzer rutting device. The increase in residual asphalt content at optimum mixing water content led to a systematic increase in strength and reduction in voids that stabilizes as the content increases. The permanent deformation experiments showed the importance of imposing a deformation criterion to select the residual asphalt content in the mix design stage. Additionally, the triaxial repeated load tests demonstrated the importance of using anticipated stress states to characterize permanent deformation resistance which cannot be captured by simple performance tests. Representative stress states in the CR layer should be taken into account during the mix design stage where higher stresses are anticipated in the recycled layer.