Sequential Packing Research Articles

Nitrogen removal performance and C, N, S transformation under dry-wet alterations of heterotrophic-autotrophic sequential bioretention system amended with waste reed and sulfur

Mixotrophic denitrification (combining heterotrophic denitrification and autotrophic denitrification) based bioretention system is a promising sustainable technology for nitrate nitrogen removal from stormwater runoff due to the ease of start-up and long operational cycle. However, the initial release of organic matter and ammonia nitrogen from most biomass carbon sources and the ineffectiveness for real-time treatment during rewetting period limit its engineering application. In this study, a novel heterotrophic-autotrophic sequential bioretention system was constructed by sequential packing waste reeds and sulfur in the upper and lower layers of the submerged zone. The removal of nitrate nitrogen, total nitrogen, ammonia nitrogen, and chemical oxygen demand maintained at 92.45%–99.54%, 84.80%–95.60%, 57.27%–82.86%, and 67.79%–85.82%, respectively, under different influent nitrate nitrogen loads of 20 mg/L, 14 mg/L, and 8 mg/L, respectively. The system solved the massive leaching of ammonia nitrogen and organic matter from waste reeds in the early phases and achieved low production rate of nitrite nitrogen (0.0004–0.0048 kg/(m3·d)) and sulfate (0.09–0.27 kg/(m3·d)). For 3 and 5 antecedent dry days, the real-time nitrate nitrogen removal kept at 99.66 ± 0.25% and 91.26 ± 7.91%. Microbial communities and functional genes revealed the C, N, S transformation driven by the system improved the responds to dry-wet alterations. The synergy of heterotrophic layer (heterotrophic denitrification and sulfide-driven autotrophic denitrification) and autotrophic layer (sulfur-driven autotrophic denitrification, dissimilatory nitrate reduction to ammonia, and sulfate reduction) promoted the complete denitrification and reduced by-products formation.

Cache eviction for SSD-HDD hybrid storage based on sequential packing

Hybrid storage systems consist of NAND flash-based solid-state drives (SSDs) and conventional hard disk drives (HDDs). In which, the SSD device commonly acts as a cache for HDDs, to not only reduce overall power consumption, but also enhance responsiveness of a system. Cache eviction, which is responsible for moving the data out of SSD during garbage collection (GC) so as to maximize the use efficiency of cache, may greatly affect normal I/O processing. This paper proposes a technique of sequential packing-based cache eviction, which groups the flushed data pages in the GC block, with the (cold) sequential address data located in other SSD blocks. Then, the packed data pages can be flushed together onto the lower level storage of HDDs, to fully use their high sequential bandwidth. As a result, our approach can cut down negative effects of write amplification on the SSD cache, and contribute to better I/O performance of SSD-HDD hybrid storage.

Method of cutting metal ingot with changing direction of deformation

The paper considers the priority problem of cutting an ingot with a changing direction of rolling which can be presented as a priority problem of packaging with incompatible categories and dynamically changing bin sizes. This problem is a NP-complete optimization problem which is often found in the production of metalwork. Priorities reflect the “importance” of manufacturing elements for production and incompatible categories show that elements of different thicknesses cannot be made from the same bin. In addition, we use guillotine restrictions to take into account the specifics of production. The problem of cutting the ingot is characterized by the fact that the bin sizes are not known in advance. Thus, the task is to find the optimal bin sizes, the sequence of actions and the placement of elements in these bins. We propose a sequential tree metaheuristic to solve this problem. It is a deterministic method. This method is based on sequential metaheuristic which was proposed by the authors earlier. The proposed method also has the ideas of dividing a set of elements into groups and subgroups by thickness and priorities, as well as their sequential packing. However, it differs from previous one in that it uses a tree to represent options for rolling operations. The best subtree from the point of view of any criterion, formed as a path from the root to one of the nodes, will be the solution. The ability to find several better solutions with the same value of the criterion is another advantage of the proposed method. Such a situation may arise, for example, for a square ingot. In this case, two solutions are possible with accuracy up to 90 degrees rotation of the workpieces. The priority heuristic is used as a heuristic to solve the packaging subproblem. The results of computational experiments are based on test cases which were compiled specially. Test instances differ in the number and characteristics of elements, categories, priorities. The results show that the metaheuristic proposed by the authors solves the problem effectively.

A MILP model and two heuristics for the Bin Packing Problem with Conflicts and Item Fragmentation

Bin Packing Problem with Conflicts and Item Fragmentation (BPPC-IF) is a variant of the classical bin packing problem in which fragments of the same item can be packed in different bins (item fragmentation), and some pairs of items cannot be packed in the same bin (item conflicts). To solve the problem, we propose a new mixed-integer linear programming (MILP) model and two heuristics. The MILP model represents BPPC-IF as a generalized transportation problem in which sources correspond to items, and sinks are defined for all maximal independent sets in the graph representing conflicts between items. The proposed heuristics, called Sequential Packing Heuristics 1 and 2 (SPH1 and SPH2, respectively) are modifications of the previously proposed Sequential Maximum Degree Packing Heuristic (SMDPH) for BPPC-IF, which fills bins by solving a series of MILP subproblems. Both new heuristics use improved criteria for selecting items to pack. Additionally, in SPH1, bins are filled heuristically without solving any MILP subproblems. Computational experiments carried out on a large set of benchmark problem instances from the literature show that (i) SPH1 obtains good quality solutions and is orders of magnitude faster than the MILP-based heuristics, (ii) compound heuristic SPH1+SPH2 finds on average better solutions than any other heuristic and is on average faster than any other MILP-based heuristic, and (iii) the proposed MILP model greatly outperforms the best previously proposed MILP model.

Criticality Analysis of HTR-10 using an Open Source Monte Carlo code OpenMC

As one of advance nuclear reactor design, capability to model and analyze the pebble bed reactor is important. Availability and capability of an accurate open source and open access software for pebble bed reactor analysis is strategic. It can broaden the involvement of more student and researcher which finally may improve the research and development in this field. Current study aim to develop the pebble bed core model and perform a criticality study of HTR-10 core design. This study exploit the capability of openMC software to model a double heterogenety geometry using a TRISO Pack Model Building based on random sequential packing and closed random packing. Physical parametric surveys of the developed model show an expected results in which the model able to reflect the negative reactivity feedback of HTR-10 design. The critical height of HTR-10 model with helium from current model, VSOP, and MCNP are 125.881 cm, 125.804 cm, 126.116 cm, respectively. A code-to-code criticality analysis comparison of current model with VSOP and MCNP code reported by INET shows a good comparison and suggest that current method can be used for further pebble bed analysis.

Last-Mile Shared Delivery: A Discrete Sequential Packing Approach

We propose a model for optimizing the last-mile delivery of n packages from a distribution center to their final recipients, using a strategy that combines the use of ride-sharing platforms (e.g., Uber or Lyft) with traditional in-house van delivery systems. The main objective is to compute the optimal reward offered to private drivers for each of the n packages such that the total expected cost of delivering all packages is minimized. Our technical approach is based on the formulation of a discrete sequential packing problem, in which bundles of packages are picked up from the warehouse at random times during the interval [Formula: see text]. Our theoretical results include both exact and asymptotic (as [Formula: see text]) expressions for the expected number of packages that are picked up by time T. They are closely related to the classical Rényi’s parking/packing problem. Our proposed framework is scalable with the number of packages.

A geometric algorithm based on the advancing front approach for sequential sphere packing

The arrangement of calculation samples with a high initial solid fraction and lower computation times are the primary concern of discrete element simulations. When generating thousands of particles, the advancing front algorithm, as one of the effective constructive packing methods, provides a time-saving and non-overlapping packing methodology compared to dynamic approaches, which are extremely time consuming in these situations. This paper proposes a sequential packing algorithm based on the advancing front approach. New particles with random sizes are sequentially inserted into a predefined void space to make contact with at least three existing neighboring particles by analytically solving the trilateration equations. The algorithm allows for an arrangement of particles with random sizes to obtain a low-porosity particle assembly. To increase the algorithm efficiency, a feasible measure of the spatial gridding is proposed to simplify the detection of the advancing fronts. The generated packings can be isotropic, and the number of contacts per particle is sufficiently high to reach a stable state. When the generated packing structures are filled with particles, the physical properties, including the average coordination number, solid fraction, second-order fabric tensor, contact orientation and particle size distribution, are analyzed with different size ratios. Examples of practical geometric models for real industrial applications to railway ballast and a gear are presented.

Random aggregate model for mesoscopic structures and mechanical analysis of fully-graded concrete

The realization of approximate practical aggregate models is a crucial link to the mesoscopic mechanical analysis for fully-graded concrete. This study systematically presents the establishment of mesostructure models of three-phase fully-graded concrete composed of anisotropic high-contented aggregates, cement paste, and interfaces bonding between them. A convex extension method is demonstrated in details to construct convex polygons and polyhedrons represented as crushed coarse aggregates in two- and three-dimensional spaces. The Walraven formula combined with the definition of an equivalent diameter is adopted to ensure the equivalence between the gradation and content of circle and spherical aggregates and that of the actual anisotropic geometric aggregates. We also propose a so-called “occupation and removal method (ORM)” to improve the efficiency of generating mesostructure models with a high aggregate content. Combining the ORM, the inter-particle overlap detection, and the random sequential packing scheme, random aggregate models (RAMs) of polygonal and polyhedral particles are realized to characterize the mesostructure models of concrete. Moreover, by the present RAMs applied, we implement a mesoscopic finite element simulation to investigate the flexural failure process of fully-graded concrete. The numerical results for dynamical flexural strength and dynamic increasing factors show a good in agreement with the experimental data reported in the literature. The proposed RAMs can simulate and explain qualitatively the effects of the mesoscopic structural properties on the macroscopic dynamic characteristics of fully-graded concrete.

Sequential sphere packing by trilateration equations

Specimen generation by packing of particles is the initial step in most numerical simulations for discrete media. In many cases, especially for virtual soil compaction, filtration, or penetration tests, this preparation work is essential for the success of the tests because the discrete particles are not able to be redistributed during the simulation. This paper presents a novel sphere sequential packing method for specimen generation using the trilateration method and its relevant equations. The method is developed on an assumption that spherical particles must be in contact with at least three neighbour particles to be kept in balance. This semi-analytical method has three advantages: quick generation speed, adjustable porosity, and good control over the spatial distribution of particles at local scale. This paper is a study on two typical spatial distributions: (1) layer-wise, where particles with similar sizes have priority of being positioned next to each other; and (2) discrete, where small particles are located preferentially in between large particles.

Evaluation of Single or Double Hurdle Sanitizer Applications in Simulated Field or Packing Shed Operations for Cantaloupes Contaminated with Listeria monocytogenes

Listeria monocytogenes contamination of cantaloupes has become a serious concern as contaminated cantaloupes led to a deadly outbreak in the United States in 2011. To reduce cross-contamination between cantaloupes and to reduce resident populations on contaminated melons, application of sanitizers in packing shed wash water is recommended. The sanitizing agent of 5% levulinic acid and 2% sodium dodecyl sulfate (SDS) applied as a single hurdle in either a simulated dump or dip treatment significantly reduced L. monocytogenes to lower levels at the stem scar compared to a simulated dump treatment employing 200 ppm chlorine; however pathogen reductions on the rind tissue were not significantly different. Double hurdle approaches employing two sequential packing plant treatments with different sanitizers revealed decreased reduction of L. monocytogenes at the stem scar. In contrast, application of sanitizers both in the field and at the packing plant led to greater L. monocytogenes population reductions than if sanitizers were only applied at the packing plant.

Primary fabric fraction analysis of granular soils

Granular soil can be considered as a composition of two fractions of particles; an immobile part called the primary fabric, and loose particles located in the voids formed by the immobile part considered to be potentially mobile. The primary fabric transfers momentum through force chains formed by interconnected force chains. These force chains form pores where loose particles are located. As a consequence, loose particles can be mobilised very easily under the influence of seepage flow and transported away if the geometrical conditions of the pore structure allows it. Therefore, the determination of the primary fabric fraction, as well as loose particle fraction, is of vital importance especially in soil suffusion predictions, which must be thoroughly considered in the design of hydraulic structures or their risk assessment. This paper presents a new method to simulate the behaviour of soils under stress and introduces a numerical analysis to define the primary fabric fraction. To achieve this, soil specimens are built by a new sequential packing method, which employs trilateration equations for packing. Later, specimens are compacted under oedometric conditions using the discrete element method to observe how the loading force is distributed across the solid matrix and to identify the fraction of the soil sustaining the external force. The primary fabric fraction analysis is conducted on two types of soil particle arrangements with several grain size distributions. A striking finding of this study is that the portion of the soil belonging to the primary fabric greatly depends on the structural packing of the granular particles. This finding should be used as evidence for the formulation of more accurate criteria for the prediction of suffusion and erosion in the future.

Packing degree optimization of arbitrary circle arrangements by genetic algorithm

Selection of optimal aggregate proportions is the main part of the concrete mix design optimization. Assuming circular aggregates, a new Sequential Packing Algorithm (SPA) is proposed to densify arrangement of arbitrary circles. To achieve the densest packing of circles, the porosity packed circle assemblies is optimized by a Genetic Algorithm (GA) search module. Proper adjustment of the algorithm’s parameters and selection of the initial population are effective tools for speeding up the computational progress. The model exactly solves known problems. Finally, Ideal-grading curve is presented after the implementation of GA on the data set.

A Numerical Approach for the Determination of the Primary Fabric of Granular Soils

Granular soil as a porous medium consists of particles, touching each other and forming a solid skeleton with interconnected pores. The transfer of externally applied loads is in most cases not homogeneous, but takes place mostly in a limited number of particles creating so-called force chains. The assembly of force chains is frequently referred to the primary fabric of a soil. The knowledge of the primary fabric is of vital importance for the analysis of many soil behaviours as, for instance, in the assessment of suffusion. Most of the current numerical models, mostly based on a discrete element approach, generate an artificial soil specimen by creating particles randomly. Therefore, particle position is not under control at all, and as a consequence the influence from particle arrangement on the creation of the primary fabric is neglected. This paper presents a sequential packing method, which allows studying two different types of particle arrangements for a given and constant grain size distribution: (1) layer-wise, producing a layered structure and (2) discrete, leading to a rather homogeneous soil structure. The generated soil specimens are compacted using a discrete element model under oedometric boundary conditions and zero gravity to create force chains within the soil structure. These force chains are then analysed to determine the soil fraction contributing to the load transfer. The results of the study provide an evidence of the influence of the particle arrangements on the appearance of the soil skeleton and the fraction of particles involved.

Limit theorems for geometric functionals of Gibbs point processes

On observe un processus ponctuel $\varXi$ dans $\mathbb{R}^{d}$ dans une fenêtre $Q_{\lambda}$ de volume ${\lambda}$. L’observation en un point $x$ que l’on note $\xi(x,\varXi)$ est une fonction des points situés à une distance aléatoire de $x$. Quand $\varXi$ est un processus de Poisson ponctuel ou Binomial, la limite pour ${\lambda}$ grand de la somme totale $\sum_{x\in\varXi\cap Q_{\lambda}}\xi(x,\varXi\cap Q_{\lambda})$ (convenablement recentrée et normalisée) est bien comprise. Dans ce papier, nous étudions cette somme totale quand $\varXi$ est Gibbsien et prouvons la loi des grands nombres, la variance asymptotique et un théorème de la limite centrale. Les preuves reposent sur la simulation parfaite de processus ponctuels Gibbsiens pour établir leurs propriétés de mélange. Ces résultats généraux sont appliqués dans différents contextes comme des modèles de croissance et de percolation, des graphes de Voronoi et des problèmes de quantification pour des entrées Gibbsiennes.

Evaluation of Mesostructure of Particulate Composites by Quantitative Stereology and Random Sequential Packing Model of Mono-/Polydisperse Convex Polyhedral Particles

Random packing of particles has served as a topic of intense research in the chemical, physical, engineering, and material fields. The majority of previous works focused on the random packing models of spherical, cylindrical, and ellipsoidal particles, whereas little is known about polyhedral particles. In this article, a modeling study of the random packing of convex polyhedral particles is presented in detail, using an interparticle contact detection algorithm, a particle-to-container wall intersection detection algorithm, and a random sequential packing algorithm for hard particles, and the accuracy and efficiency of the contact detection algorithm are compared with those of methods from the literature. With the random packing model and a specified particle size distribution, mesostructure models of particulate composites with mono-/polydisperse particles were generated and validated by a sectioning analysis algorithm. Based on quantitative stereological theories and the sectioning analysis algorithm, the effects of particle shape on the mesostructures composed of the monodisperse and polydisperse particles were evaluated. Further, the statistical results were verified by validation against experimental results from the literature and theoretical results.

Statistical Manufacturing Model of Printing Technology

There has been an evident increase in the demand for accurate and complex patterns for particles used in microsized electronic devices. Direct printing technology has been promoted as a solution for these needs, as the development of this technology provides both economical and environmental benefits, as well as being a time and energy saving process. Research in the field of printing technologies is still in the initial stages, involving the study of physical and chemical properties of printing materials. There are several methods currently using direct printing methods: microdispensing deposition write (MDDW), maskless mesoscale materials deposition (M3D), and inkjet printing. This study explores the direct printing methods of sequential and randomized printing associated with MDDW, M3D, and inkjet printing using computer simulations compared with actual experimentations. Sequential printing involves depositing particles onto the substrate in a specific order based on particle size. This method is associated with MDDW, where a relatively high viscous ink is dispensed onto the substrate so that particle sizes maintain an order in relation to one another, effectively producing a higher packing factor. Randomized printing involves the dispensing of various sizes of particles onto the substrate in a random order, as in inkjet printing. With this process, the probability of obtaining an efficient packing factor is unlikely and decreases even more with particle size. Therefore, the monolayer method, involving the deposition of individual particles, was developed to increase the packing factor when using the inkjet process. The results presented in this study proved that monolayering methods coincide with the projections predicted by the computer simulation. Sequential packing (MDDW) provides a shorter and higher range of packing factors than that of random packing sequences (ink jet); thus showing sequential packing to be the more efficient method. Sequential packing is closely related to the printing of high viscosity ink because of the higher packing factor that this method provides. An ink with increased viscosity allows for better conductivity which is essential in the development of improved nanoprinting technologies. This study provides evidence for the most efficient means of increasing the packing factor of particles; these methods offer the opportunity for technological advancement and commercialization of nanoprinting materials.

Numerical investigation of effect of particle shape and particle size distribution on fresh cement paste microstructure via random sequential packing of dodecahedral cement particles

At the microscopic scale, fresh cement paste is composed of random packing of irregular cement particles and their packing behavior plays an important role in microstructure evolution. The characterization of fresh cement paste microstructure is of particular interest in its physical properties, such as mechanical and transport properties. The preponderance of previous work has focused on the microstructure model by random packing of ellipsoidal particles, and little is known about three-dimensional (3D) non-ellipsoidal particles. In this paper, a modified cement particle size distribution function is used to facilitate particle size distribution of dodecahedral particles. Based on a random sequential algorithm, the microstructure model of fresh cement paste is simulated by the random sequential packing of dodecahedral particles with various sizes. Applying stereological tools and a serial sectioning analysis technique, the simulated microstructure composed of multi-sized dodecahedral cement particles is characterized and compared with that of ellipsoidal cement particles developed by the preliminary work to evaluate the influence of cement particle shape on the microstructure of fresh cement paste. Moreover, the random packings of multi-sized dodecahedral particles satisfied with three specified particle size distribution functions, respectively, are investigated to assess the effect of particle size distribution on the random packing of the dodecahedral particles. Finally, the reliability of the statistical results is verified by theoretical solutions.

Effects of particle size distribution, shape and volume fraction of aggregates on the wall effect of concrete via random sequential packing of polydispersed ellipsoidal particles

Concrete can be viewed as granular materials at the mesoscopic level. A specific distribution of aggregate particles in boundary layers, known as the wall effect, plays an important role in the mechanical properties and durability of concrete. However, the detailed and systematic experimental and simulated data about the wall effect of concrete is hardly adequate yet. Specially, the modeling study of spherical and two-dimensional (2D) elliptical aggregates distribution for the wall effect has been focused on in previous work, little is known about three-dimensional (3D) ellipsoidal aggregates. In the present work, based on a mesostructure model of concrete, the wall effect of concrete is quantified by configuration parameters such as the volume fraction, the specific surface area and the meaning free spacing of the solid phase. In addition, the influences of ellipsoidal particle size distribution (EPSD), shape and volume fraction (Vf) of ellipsoidal aggregates on the configuration parameters are evaluated by stereological methods and serial section analysis technique. Furthermore, the effect mechanisms of EPSD, shape and Vf are analyzed and discussed in this paper. The reliability of the statistical results is verified by experimental data and theoretical analytical results.

Quantitative characterization of the microstructure of fresh cement paste via random packing of polydispersed Platonic cement particles

On a microscopic scale, fresh cement paste is composed of random packing of irregular cement particles, and their initial packing behavior plays an important role in microstructural evolution. The preponderance of previous works has focused on the microstructure model by random packing of three-dimensional spheroidal particles, and little is known about non-spheroidal particles. In this paper, a modified cement particle size distribution function is used to facilitate the particle size distribution of convex polyhedral cement particles. Based on an overlapping detection algorithm, the microstructure model of fresh cement paste is simulated by the random sequential packing of Platonic cement particles of various sizes. Applying stereological tools and the serial sectioning analysis technique, the modeling microstructure composed of polydispersed Platonic cement particles is characterized and compared with that of ellipsoidal cement particles with various aspect ratios. The statistical results are investigated to evaluate the influence of cement particle shape on the microstructure of fresh cement paste. Finally, with the derived experimental and numerical results of microstructural parameters, the reliability of the statistical results is verified.

Force propagation in isostatic granular packs

We investigate how forces spread through frictionless granular packs at the jamming transition. Previous work has indicated that such packs are isostatic, and thus obey a null stress law which, independent of the packing history, causes rays of stress to propagate away from a point force at oblique angles. Prior verifications of the null stress law have used a sequential packing method which yields packs with anisotropic packing histories. We create packs without this anisotropy, and then later break the symmetry by adding a boundary. Our isotropic packs are very sensitive, and their responses to point forces diverge wildly, indicating that they cannot be described by any continuum stress model. We stabilize the packs by supplying an additional boundary, which makes the response much more regular. The response of the stabilized packs resembles what one would expect in a hyperstatic pack, despite the isostatic bulk. The expected stress rays characteristic of null stress behavior are not present. This suggests that isostatic packs do not need to obey a null stress condition. We argue that the rays may arise instead from more simple geometric considerations, such as preferred contact angles between beads.