Actual Packing Research Articles

Development of geopolymer cold-bonded artificial aggregate for UHPC applications based on fresh packing density

Geopolymer cold-bonded aggregate (GCBA) is a new eco-friendly artificial aggregate manufactured by powder agglomeration. So far the scientific understanding of the powder particle packing within GCBA remains insufficient, which is theoretically crucial for enhancing its mechanical properties. In this study, a high-strength GCBA as the coarse aggregate for ultra-high performance concrete (UHPC) applications was developed using a high dosage of waste brick powder, and a new concept on fresh packing density was innovatively proposed to reflect the actual packing state of the precursor powders within GCBA. The effects of fresh packing density on GCBA's physico-mechanical properties and microstructure were investigated. Results show that the GCBA with the highest fresh packing density had the highest bulk crushing strength over 40 MPa at a reaction degree of only 8.1 %, and the resulting UHPC with this GCBA exhibited an impressive unconfined compressive strength of 162.9 MPa. The increase in fresh packing density led to a significant increase in the hardened GCBA's strength and density, despite it causing a reduction in the GCBA's reaction degree. The microstructural densification of GCBA with higher fresh packing density was formed by closed-packing unreacted precursor powder particles and fewer cyclic-shaped gels, resulting in a significant reduction in the required dosage of activator solution and consequently a decrease in GCBA's cost. A guideline of mixture design for GCBA was finally proposed, which differed from traditional methods by optimizing fresh pellets' performance rather than hardened pellets' performance, promising to be a more economical and efficient avenue for improving GCBA performance.

Research on intelligent nesting algorithm for irregular ship parts based on no-fit-polygon

In order to improve the utilization ratio of steel plates for parts nesting in ship manufacturing, this paper proposes a new intelligent nesting algorithm for irregular parts based on NFP, which can effectively solve the three difficult problems of selection, locating and collision for irregular parts nesting, and further improve the utilization rate of steel plates. The new method directly uses irregular parts as nested objects, establishes the NFP generating algorithm based on the trajectory and Minkowski method to solve the collision problem, proposes a hybrid strategy of the TOPOS principle and BL principle to solve the locating problem, and builds ISAGA to optimize the sequence and rotation angle of the irregular part to solve the selection problem. Finally, the feasibility and effectiveness of the new method are verified by comparing with the existing nesting softwares of Nest Lib and Sigma Nest based on 10 standard test cases of irregular parts in the ESICUP, the experimental results show that the plate utilization of the proposed new method increased on average by 9.76 % and 13.56 %. And compared with the actual packing results of irregular parts in a shipyard, the plate utilization rate of the new method increased by 5.04 % on average.The new method will play an important role in further improving the utilization rate of steel plate in the shipbuilding industry.

Tomography-based digital twin of Nd-Fe-B permanent magnets

Many functional materials have been designed at the multiscale level. To properly simulate their physical properties, large and sophisticated computer models that can replicate microstructural features with nanometer-scale accuracy are required. This is the case for permanent magnets, which exhibit a long-standing problem of a significant offset between the simulated and experimental coercivities. To overcome this problem and resolve the Brown paradox, we propose an approach to construct large-scale finite element models based on the tomographic data from scanning electron microscopy. Our approach reconstructs a polycrystalline microstructure with actual shape, size, and packing of the grains as well as the individual regions of thin intergranular phase separated by triple junctions. Such a micromagnetic model can reproduce the experimental coercivity of ultrafine-grained Nd-Fe-B magnets along with its mechanism according to the angular dependence of coercivity. Furthermore, a remarkable role of thin triple junctions as nucleation centers for magnetization reversal is revealed. The developed digital twins of Nd-Fe-B permanent magnets can assist their optimization toward the ultimate coercivity, while the proposed tomography-based approach can be applied to a wide range of polycrystalline materials.

A Novel Transfer Learning-Based Cell SOC Online Estimation Method for a Battery Pack in Complex Application Conditions

The crucial function of a battery management system is to estimate the state of charge (SOC). However, complex application conditions like different temperatures, aging, and inconsistency between cells would cause a distribution difference between data domains and lead to a significant estimation error in the established SOC model. Transfer learning's domain adaptation offers a way to lessen the disparity in distribution across the data domains, and yet it also requires that the target space be the same across domains, which is challenging to do with the SOC online estimate. This article proposes a SOC differential processing method as well as designs a combined transfer learning method. As experiments of actual battery pack provided by China First Automobile Work-based confirmed, the proposed method can obtain precise and robust SOC estimation results with a mean absolute percentage error of less than 1.5%.

High-fidelity computational micromechanics of composite materials using image-based periodic representative volume element

This study presents numerical investigations into the progressive damages in unidirectional carbon fiber reinforced plastics (CFRP) using computational micromechanics with an image-based periodic representative volume element (RVE). This RVE modeling approach combines the methodologies of image-based RVE and artificial periodic RVE to reflect the actual fiber packing while retaining geometrical periodicity. For high-fidelity modeling, the RVE model is prepared with more than 200 carbon fibers extracted from X-ray computed tomography images. Both homogenization simulation for elastic properties and unidirectional loading simulations for plastic and fracture properties are carried out. The constitutive model parameters are identified and verified by comparing the experimental and predicted effective (spatially averaged) elastic and plastic properties, and ultimate strength. Then, with the identified parameters, the local damage initiation and propagation mechanism, and their influence on the macroscopic nonlinear stress–strain behaviors are discussed. Finally, the influences of fiber packing on the predicted accuracy of computational micromechanics are discussed by comparing the results between the proposed image-based RVE and simple RVEs with the square and hexagonal arrays.

Optimizing e-commerce warehousing through open dimension management in a three-dimensional bin packing system.

In the field of e-commerce warehousing, maximizing the utilization of packing bins is a fundamental goal for all major logistics enterprises. However, determining the appropriate size of packing bins poses a practical challenge for many logistics companies. Given the limited research on the open-size 3D bin packing problem as well as the high complexity and lengthy computation time of existing models, this study focuses on optimizing multiple-bin sizes within the e-commerce context. Building upon existing research, we propose a hybrid integer programming model, denoted as the three dimensional multiple option dimensional rectangular packing problem (3D-MODRPP), to address the multiple-bin size 3D bin packing problem. Additionally, we leverage well-established hardware and software technologies to propose a 3D bin packing system capable of accommodating multiple bin types with open dimensions. To reduce the complexity of the model and the number of constraints, we introduce a novel assumption method for 0-1 integer variables in the overlap and rotation constraints. By applying this approach, we significantly streamline the computational complexity associated with the model calculations. Furthermore, we refine the dataset by developing a customized version based on the classical Three-Dimensional One-Size Dependent Rectangular Packing Problem (3D-ODRPP) dataset, leading to improved outcomes. Through comprehensive analysis of the research results, our model exhibits remarkable advancements in addressing the strong heterogeneous bin packing problem, the weak heterogeneous bin packing problem, the actual bin packing problem, and the bin packing problem with multiple bin types and open sizes. Specifically, it significantly reduces model complexity and computation time and increases space utilization. The system designed in this study paves the way for practical applications based on the proposed model, providing researchers with broader research prospects and directions to expand the scope of investigation in the field of 3D bin packing. Consequently, this system contributes to solving complex 3D packing problems, reducing space waste, and enhancing transportation efficiency.

A Combinatorial Optimization Approach for Air Cargo Palletization and Aircraft Loading

The current air cargo loading plan handles the Air Cargo Palletization Problem (ACPP) and the Aircraft Weight and Balance Problem (WBP) separately, which has an impact on the optimization of the payload and the aircraft’s center of gravity (CG). Thanks to improvements in computer processing power, the joint combinatorial optimization of ACPP and WBP is now feasible. Three integer linear programming models are proposed: a Bi-objective Optimization Model (BOM), a Combinatorial Optimization Model (COM), and an Improved Combinatorial Optimization Model (IOM). The objectives of the models are the maximum loading capacity and the lowest CG deviation from a specified target CG. The models also consider a wide range of restrictions in the actual packing and stowage procedures, such as volume, weight, loading position, aircraft balance, and other aspects of aircraft and unit load devices. Four scenarios with various conditional metrics for three models are solved for the B777F aircraft using Gurobi. The results of the computations demonstrate that the BOM has the fastest solution speed, but the CG deviation is the largest, and in several cases the CG deviation results are unacceptable. The COM has the longest solution time, which is difficult to tolerate in practice. Despite taking a little longer to solve computationally than the BOM, the IOM offers the best optimization solution.

A novel air-cooled Li-ion battery (LIB) array thermal management system – a numerical analysis

Li-ion battery cell operations, as an energy storage device, are delicate to changes in temperature. Extreme environmental conditions affect their cycle life and performance. Therefore, effective cell temperature management is necessary for secure and dependable battery operation. Additionally, the high production costs of electric cars must be countered by the adaptability of the battery pack design for Electric Vehicles. As the reactions generate more heat and raise the battery's temperature, it may cause the battery to explode and cause fires in the workplace. The CFD simulation is used to study the influence of a novel design of an efficient Air-cooling system to improve the performance of lithium-ion batteries by reducing the operating temperatures under a different coolant flow rate. The operational performance and service life of lithium-ion batteries are greatly affected by operating temperature. The different spacing between the batteries is investigated (S = 2, 4, and 6 mm) by using air as a cooling fluid to dissipate the heat from lithium-ion batteries by flowing the air inside flow air inside the cooling pack. The Reynolds numbers (Re) vary from 15,000 to 30,000 in the current analysis. The increasing Re number increases the average Nusselt numbers (Nu). The findings demonstrate that the thermal performance (Nu) rises as the spacing rises. The outcome of this study will provide helpful information for the designer for proper modeling of the actual battery pack design of modern, efficient EVs.

A nonlinear particle packing model for micro-aggregate

The nonlinear packing model for micro-aggregate was constructed to predict the actual packing density of micro-aggregate based on the nonlinear packing model for granular soil. The characteristics, differences and applications of five common calculation models of packing density were discussed. The correlation coefficients of the particle packing model based on granular soil were modified to derive packing density relationship for the micro-aggregates. The average relative error of the measured results is less than 0.4% and the root mean square error is within 3.2 × 10−3, which means the validity of the derived relationship is verified.

A 3D Offline Packing Algorithm considering Cargo Orientation and Stability

The box packing problem can be generalized as placing a batch of cargos with a specified number of different physical characteristics into a specified box. Suppose that a batch of cuboid cargos of different sizes are to be loaded into a batch of boxes of the same type, the cargos have constraints such as orientation and stability. Taking the mean value of the reciprocal of space utilization as the objective function, this paper designs a hybrid genetic algorithm that combines genetic algorithm and tabu search algorithm. Aiming at the information of the packing sequence and the rotating state of the box in the packing scheme, a two-stage real number encoding method and decoding method based on random keys are designed, and a crossover operation based on partial random keys and uniform crossover is designed. In order to convert the solution searched by the optimization algorithm into the actual packing scheme, a heuristic loading algorithm is designed while using the positioning rule of the lower left corner, the space selection rule of the minimum space, and the division and merging rules of the remaining space. In the early stage, the roulette method was used to strengthen the global search ability, and in the later stage, the optimal preservation strategy was used to speed up the algorithm convergence. To make up for the shortcomings of the genetic algorithm’s weak local search ability and slow convergence speed, the tabu search algorithm was used as a mutation operation in the genetic algorithm. The solution in the generation is used as the initial solution of the tabu search algorithm, and the search process is carried out. Finally, this paper tests the proposed hybrid algorithm on 6 groups of weakly heterogeneous and strongly heterogeneous data in the BR dataset. The results prove that the proposed algorithm can reduce the usage of boxes.

Virtual Battery Pack-Based Battery Management System Testing Framework

The battery management system (BMS) is a core component to ensure the efficient and safe operation of electric vehicles, and the practical evaluation of key BMS functions is thus of great importance. However, the testing of a BMS with actual battery packs suffers from a poor testing repeatability and a long status transition time due to the uncontrollable degradation of battery systems and testing environment variations. In this paper, to overcome this challenge, we propose an efficient BMS testing framework that uses virtual battery packs rather than actual ones, thus enabling a rapid and accurate evaluation of a BMSs key functions. A series-connected virtual battery pack model through leveraging Copula’s method is formulated to capture the dynamics and inconsistency of individual batteries in the pack. The developed lithium iron phosphate model features low computational efforts and is experimentally validated with different dynamical profiles, implying a high-precision virtual battery pack that is capable of reproducing the actual one. Furthermore, this framework includes a closed-loop testing platform, which can provide the state-of-charge/state-of-power references and thus automatically test and evaluate the states of the battery packs estimated from the BMS. Particularly, we consider the initial polarization that often exists in the batteries during the operation to accurately calibrate the available state-of-power benchmark of battery packs in the real world. The performed BMS testing results using the proposed framework illustrate that the tested BMS cannot adapt to the varied operation conditions, thus leading to high state estimation errors, which may result in the over-charge/discharge or over-temperature of the batteries. Therefore, this work highlights the value of effective BMS testing, providing the promising potential to achieve reliability and durability for battery systems.

Accurate SOC Prediction and Monitoring of Each Cell in a Battery Pack Considering Various Influencing Factors

Accurate prediction and monitoring of the state of charge (SOC) of each cell in a battery pack are of great significance for safe driving of electric vehicles. Subject to the factors of differences between cells, temperature, and aging, the accuracy of existing SOC prediction methods may be affected in applications. This article employs a data-driven method with a proposed novel transfer learning framework considering conditional probability distribution adaptation to solve the impact of the aforementioned influencing factors on SOC prediction and obtains a favorable SOC prediction result for each cell. As experiments of actual battery pack provided by China First Automobile Work based demonstrated, the proposed method can accurately predict SOC of each cell under different influencing factors by using only one cell modeling data. Moreover, the proposed method is robust against model parameter uncertainties, sensor noise, etc.

Supramolecular assemblies in the molecular complexes of 4-cyanophenylboronic acid with different N-donor ligands.

Molecular complexes of 4-cyanophenylboronic acid (CB) with various N-donor compounds having different conformational features, for example, rigid (1,10-phenanthroline (110phen), 4,7-phenanthroline (47phen), 1,7-phenanthroline (17phen) and acridine (acr)) and linear (1,2-bis(4-pyridyl)ethane (bpyea), 1,2-bis(4-pyridyl)ethene (bpyee) and 4,4'-azopyridine (azopy)), have been reported. In all complexes, the -B(OH)2 moiety is found to be in a syn-anti confirmation, with the exception of structures containing 110phen, bpyee, and azopy, wherein, syn-syn conformation is observed. Further, CB molecules remain intact in all structures except in the complexes with some linear N-donor ligands, wherein -B(OH)2 transforms to monoester (-B(OH)(OCH3)) prior to the formation of corresponding molecular complexes. In such boronic monoester complexes, the conformation of -B(OH)(OCH3) is syn-anti with respect to the -OH and -OCH3 groups. Also, complexes mediated by azopy and bpyee exist in both hydrated and anhydrous forms. In these anhydrous structures, the recognition pattern is through homomeric (juxtaposed -CN and -B(OH)2) as well as heteromeric (between hetero N-atom and -B(OH)2) O-H⋯N hydrogen bonds, while only heteromeric O-H⋯N hydrogen bonds hold co-formers in all other structures. Depending upon the conformational features of both co-formers, molecules are packed in crystal lattices in the form of stacked layers, helical chains, and crossed ribbons. All structures are fully characterized by single-crystal X-ray diffraction and phase purity is established by powder X-ray diffraction. Additionally, correlation among structures is explained by calculating a similarity index and performing a Hirshfeld surface analysis to quantify the strength and effectiveness of different types of intermolecular bonds that stabilize these structures along with the presentation of energy frameworks, representing the strength of the interactions in the form gradient cylinders. Also, the morphology of each complex was computed by BFDH methodology to correlate with the actual crystal morphology and packing arrangement.

Experimentally Validated Coulomb Counting Method for Battery State-of-Charge Estimation under Variable Current Profiles

Battery state of charge as an effective operational indicator is expected to play a crucial role in the advancement of electric vehicles, improving the battery capacity and energy utilization, avoiding battery overcharging and over-discharging, extending the battery’s useful lifespan, and extending the autonomy of electric vehicles. In context, this article presents a computationally efficient battery state-of-charge estimator based on the Coulomb counting technique with constant and variable discharging current profiles for an actual battery pack in real time. A dedicated experimental bench is developed for validation purposes, where pivotal measurements such as current, voltage, and temperature are initially measured during the charging/discharging cycle. The state of charge thus obtained via these measurements is then compared with the value estimated through the battery generic model. Detailed analysis with conclusive outcomes is finally presented to exhibit the flexible nature of the proposed method in terms of the precise state-of-charge estimation for a variety of batteries, ranging from lead–acid batteries for domestic applications to Li-ion batteries inside electric vehicles.

Joint state-of-health and remaining-useful-life prediction based on multi-level long short-term memory model prognostic framework considering cell voltage inconsistency reflected health indicators

Battery-health prognostics for the state-of-health (SOH) and remaining-useful-life (RUL) are necessary to ensure the safety and reliability of system operation. However, the aging of an actual battery pack is related to internal electrochemical reactions in the constituent cells and the inter-cell inconsistency effect, which cannot be measured directly. Thus, reasonable feature selection and an effective prognostic framework are indispensable for achieving feasible and accurate results in actual systems. This paper proposes a prognostic framework for the joint prediction of the SOH and RUL of a battery pack. The proposed method utilizes indirect health indicators (HIs) during the charging stage. Moreover, Pearson correlation analysis and incremental capacity analysis are used to recognize the inconsistency effect and optimize the sampling interval of the HIs that can be adapted to the battery pack. A multi-level long short-term memory (LSTM) model is designed to jointly predict the SOH and RUL for a battery pack. The first-level LSTM model is applied to short-term SOH estimation using the optimized HIs. For the RUL prediction, the second-level LSTM is established iteratively to extend the degradation pattern from the current step to end of life using the SOH estimation result and previous output. The robustness of the extracted features and predictive capability of the multi-level LSTM model are demonstrated by accelerated aging tests for series battery packs. The experimental results confirm that the proposed prognostic framework can provide robust, reliable, and accurate SOH and RUL predictions.

Multiple time scale state-of-charge and capacity-based equalisation strategy for lithium-ion battery pack with passive equaliser

The cycle life of a lithium-ion battery pack is much shorter than that of a single cell because of their different external operating environments and internal characteristic parameters. Equalisation management is essential for reducing the difference between battery cells, improving capacity, and prolonging the cycle life. Passive equalisation is also widely used in electric vehicles and energy storage systems because of its comparative advantages including the low cost, simple structure, and easy control. Compared with the mature development of the passive equalisation hardware circuits, the evolution of high-performance equalisation strategies is relatively backward. Along these lines, a multiple time scale state-of-charge (SOC) and capacity-based equalisation strategy for lithium-ion battery packs with passive equalisers was proposed in this work. Firstly, the minimum-capacity differential model (MCDM) consisting of a cell minimum-capacity model (CMCM) and a cell differential model (CDM) was established to improve the computational efficiency and accuracy of the dynamic behaviour of the minimum-capacity cell, as well as all the other cells in a battery pack. Secondly, the SOC and capacity of a battery pack were estimated on multiple time scales with the use of a dual extended Kalman filter and MCDM. The fast time scale based on CMCM and the slow time scale based on CDM can balance the efficiency and accuracy of the estimation algorithm. Based on the accurate estimation of both SOC and capacity, a SOC-and-capacity-based equalisation strategy was designed. Finally, a high-fidelity multicell model was used to simulate and verify the proposed equalisation strategy under the guidance of actual battery pack degradation data. The extracted experimental results show that the proposed method can efficiently and accurately estimate the SOC and capacity of new and aged batteries while a high-accuracy estimation in the entire life cycle is achieved. Compared with the traditional voltage-based and SOC-based strategies, the proposed SOC-and-capacity-based strategy can reconcile the maximization of battery pack capacity, the minimization of equalisation energy consumption, and the-equalisation duration to achieve high-efficiency balancing in the entire life cycle.

Jamming of Nano-Ellipsoids in a Microsphere: A Quantitative Analysis of Packing Fraction by Small-Angle Scattering

The packing of particles is ubiquitous, and it is of fundamental importance, particularly in materials science in the nanometric length scale. It becomes more intriguing when constituent particles deviate from spherical symmetry owing to the inherent complexity in quantifying their positional and rotational correlation. For quantitative estimation of packing fraction, it requires a thorough analysis of the positional correlation of jammed particles. This article adopts a novel approach for determination of the packing fraction of strongly correlated nano-ellipsoids in a microsphere using small-angle scattering. The method has been elucidated through a quantitative analysis of structural correlation of nano-hematite ellipsoids in 3D micrometric granules, which are realized using rapid evaporative assembly. Owing to the deviation from spherical symmetry, the conventional analysis of scattering data fails to interpret the actual packing fraction of the anisotropic particles. The structural correlation gets smeared out because of orientation distribution among the packed anisotropic particles, which leads to an anomaly in the estimation of packing fraction using the conventional analysis approach. It is illustrated that consideration of an interparticle distance distribution function of the correlated nano-ellipsoids becomes indispensable in determining their packing fraction.

Workflow for computational fluid dynamics modeling of fixed‐bed reactors packed with metal foam pellets: Hydrodynamics

AbstractIn recent years, the catalyst pellets made of open‐cell metallic foams have been identified as a promising alternative in fixed‐bed reactors. A reliable modeling tool is necessary to investigate the suitability of different foam properties and the shapes of foam pellets. In this article, a workflow for a detailed computational fluid dynamics (CFD) model is presented, which aims to study the flow characteristics in the slender packed beds made of metal foam pellets. The CFD model accounts for the actual random packing structure and the fluid flow throughout the interstitial regions is fully resolved, whereas flow through the porous foam pellets is represented by the closure equations for the porous media model. The bed structure is generated using rigid body dynamics (RBD) and the influence of the catalyst loading method is also considered. The mean bed voidage and the pressure drop predicted by the simulations show good agreement with the experimental data.

Vehicle Packing Layout Optimization Based on Genetic Taboo Hybrid Search Algorithm

According to the characteristics of the actual transportation packing problem, the three-dimensional vehicle packing problem with path constraints is studied, and the optimal model of the three-dimensional vehicle packing problem with path constraints is established, and then taboo search is added to the genetic algorithm to solve the problem. The performance of the algorithm is verified by example analysis. The experimental result shows that the hybrid algorithm can obtain an approximate optimal solution of higher quality than the ordinary genetic algorithm, indicating that the algorithm has a better solution to the three-dimensional packing problem. The algorithm has a great help in improving vehicle utilization and work efficiency.

Evaluation of Mechanical Properties of Composite Solid Propellant by Genetic Engineering of Materials: Part B: Modeling of Minimum Representative Unit for Multi‐Modal Particle Packing Structure Based on Formulation of Composite Solid Propellant

AbstractTo show the actual particle packing structure of AP/Al/HTPB propellant with multi‐modal fillers, a minimum representative unit of particle packing structure is established. In the minimum representative unit, the particle size of fillers in each mode is identical to the value in the propellant formulation. Meanwhile, on the basis of the amount series of fillers in the per unit propellant, the amount of fillers in each mode is respectively divided by that of the fewest filler, thus the amount series of fillers in the minimum representative unit is obtained. Further, with the condition that the average density of the unit is the same as that of the propellant, the side length of the minimum representative unit could be uniquely determined. In order to generate the particle packing structure with particles evenly distributed in the unit, a two‐step algorithm is put forward. In the first step, the genetic algorithm is adopted to search for an evenly and non‐overlapping distribution for the few and coarser particles. In the second step, the residual finer particles are randomly placed in the voids among the coarser particles by a modified L−S algorithm. Results showed that the particles are distributed more evenly in the packs generated by the two‐step algorithm than in packs generated by the classic L−S algorithm.