二维矩形Strip Packing问题的算法研究与改进

二维矩形Strip Packing问题的约束条件及目标函数与基本型二维矩形Packing问题类似,都是在有限的矩形容器中,有效地摆放各个矩形块,以最大化容器利用率为目标。为了解决这一NP-hard问题,该文在邓见凯、王磊提出的拟人型全局优化算法的基础上进行了深入的算法研究与改进。针对Strip Packing问题特点,提出了QHG(Quasi-Human Group)算法,其核心改进涵盖了多个方面,包括扩充初始点集合、删除和替换评价标准以及扩大邻域空间搜索范围。和单个局部极小值点的迭代相比,对局部极小值点集合进行迭代所生成布局优度更高,跳坑策略用于跳出局部极小值点,将搜索引向有希望的区域,优美度枚举有望进一步提高布局优度。通过这些措施,QHG算法更好地模拟人类决策过程,提高了全局搜索的效率。为评估QHG算法性能,对8组标准问题实例(C组、N组、NT组、CX组、NP组、ZDF组、2sp组、bwmv组)进行了大量实验。实验结果表明,QHG算法生成的布局优度优于当前国际文献中的几种较先进算法,展现了其在Strip Packing问题上的卓越性能。

Electric-controlled pressure relief valve for enhanced safety in liquid-cooled lithium-ion battery packs

The liquid-cooled battery energy sto rage system(LCBESS) has gained significant attention due to its superior thermal management capacity.However,liquid-cooled battery pack(LCBP) usually has a high sealing level above IP65,which can trap flammable and explosive gases from battery thermal runaway and cause explosions.This poses serious safety risks and challenges for LCBESS.In this study,we tested overcharged battery inside a commercial LCBP and found that the conventionally mechanical pressure relief valve(PRV) on the LCBP had a delayed response and low-pressure relief efficiency.A realistic 20-foot model of an energy storage cabin was constructed using the Flacs finite element simulation software.Comparative studies were conducted to evaluate the pressure relief efficiency and the influence on neighboring battery packs in case of internal explosions,considering different sizes and installation positions of the PRV.Here,a newly developed electric-controlled PRV integrated with battery fault detection is introduced,capable of starting within 50 ms of the battery safety valve opening.Furthermore,the PRV was integrated with the battery management system and changed the battery charging and discharging strategy after the PRV was opened.Experimental tests confirmed the efficacy of this method in preventing explosions.This paper addresses the safety concerns associated with LCBPs and proposes an effective solution for explosion relief.

Battery pack capacity estimation for electric vehicles based on enhanced machine learning and field data

Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion batteries.Despite numerous works on battery capacity estimation using laboratory datasets,most of them are applied to battery cells and lack satisfactory fidelity when extended to real-world electric vehicle(EV)battery packs.The challenges intensify for large-sized EV battery packs,where unpredictable operating profiles and low-quality data acquisition hinder precise capacity estimation.To fill the gap,this study introduces a novel data-driven battery pack capacity estimation method grounded in field data.The proposed approach begins by determining labeled capacity through an innovative combination of the inverse ampere-hour integral,open circuit voltage-based,and resistance-based correction methods.Then,multiple health features are extracted from incremental capacity curves,voltage curves,equivalent circuit model parameters,and operating temperature to thoroughly characterize battery aging behavior.A feature selection procedure is performed to determine the optimal feature set based on the Pearson correlation coefficient.Moreover,a convolutional neural network and bidirectional gated recurrent unit,enhanced by an attention mechanism,are employed to estimate the battery pack capacity in real-world EV applications.Finally,the proposed method is validated with a field dataset from two EVs,covering approximately 35,000 kilometers.The results demonstrate that the proposed method exhibits better estimation performance with an error of less than 1.1%compared to existing methods.This work shows great potential for accurate large-sized EV battery pack capacity estimation based on field data,which provides significant insights into reliable labeled capacity calculation,effective features extraction,and machine learning-enabled health diagnosis.

Molecular packing tuning via chlorinated end group enables efficient binary organic solar cells over 18.5%

Designing novel nonfullerene acceptors(NFAs)is of vital importance for the development of organic solar cells(OSC).Modification on the side chain and end group are two powerful tools to construct efficient NFAs.Here,based on the high-performance L8BO,we selected 3-ethylheptyl to substitute the inner chain of 2-ethylhexyl,obtaining the backbone of BON3.Then we introduced different halogen atoms of fluorine and chlorine on 2-(3-oxo-2,3-dihydro-1Hinden-1-ylidene)malononitrile end group(EG)to construct efficient NFAs named BON3-F and BON3-Cl,respectively.Polymer donor D18 was chosen to combine with two novel NFAs to construct OSC devices.Impressively,D18:BON3-Cl-based device shows a remarkable power conversion efficiency(PCE)of 18.57%,with a high open-circuit voltage(V_(OC))of 0.907 V and an excellent fill factor(FF)of 80.44%,which is one of the highest binary PCE of devices based on D18 as the donor.However,BON3-F-based device shows a relatively lower PCE of 17.79%with a decreased FF of 79.05%.The better photovoltaic performance is mainly attributed to the red-shifted absorption,higher electron and hole mobilities,reduced charge recombination,and enhanced molecular packing in the D18:BON3-Cl films.Also,we performed stability tests on two binary systems;the D18:BON3-Cl and D18:BON3-F devices maintain 88.1%and 85.5%of their initial efficiencies after 169 h of storage at 85°C in an N2-filled glove box,respectively.Our work demonstrates the importance of selecting halogen atoms on EG and provides an efficient binary system of D18:BON3-Cl for further improvement of PCE.

A hierarchical enhanced data-driven battery pack capacity estimation framework for real-world operating conditions with fewer labeled data

Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.However,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation.To address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data.Unlike traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory resources.The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation.Using only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating conditions.Its high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.

Effect of the Particle Packing Configuration on Fixed Bed Performance

Fixed-bed reactors are generally considered the optimal choice for numerous multi-phase catalytic reactions due to their excellent performance and stability.However,conventional fixed beds often encounter challenges related to inadequate mass transfer and a high pressure drop caused by the non-uniform void fraction distribution.To enhance the overall performance of fixed beds,the impact of different packing configurations on performance was investigated.Experimental and simulation methods were used to investigate the fluid flow and mass transfer performances of various packed beds under different flow rates.It was found that structured beds exhibited a significantly lower pressure drop per unit length than conventional packed beds.Furthermore,the packing configurations had a critical role in improving the overall performance of fixed beds.Specifically,structured packed beds,particularly the H-2 packing configuration,effectively reduced the pressure drop per unit length and improved the mass transfer efficiency.The H-2 packing configuration consisted of two parallel strips of particles in each layer,with strips arranged perpendicularly between adjacent layers,and the spacing between the strips varied from layer to layer.

Time-resolved characteristics of a nanosecond pulsed multi-hollow needle plate packed bed dielectric barrier discharge

In this paper,self-designed multi-hollow needle electrodes are used as a high-voltage electrode in a packed bed dielectric barrier discharge reactor to facilitate fast gas flow through the active discharge area and achieve large-volume stable discharge.The dynamic characteristics of the plasma,the generated active species,and the energy transfer mechanisms in both positive discharge(PD)and negative discharge(ND)are investigated by using fast-exposure intensified charge coupled device(ICCD)images and time-resolved optical emission spectra.The experimental results show that the discharge intensity,number of discharge channels,and discharge volume are obviously enhanced when the multi-needle electrode is replaced by a multihollow needle electrode.During a single voltage pulse period,PD mainly develops in a streamer mode,which results in a stronger discharge current,luminous intensity,and E/N compared with the diffuse mode observed in ND.In PD,as the gap between dielectric beads changes from 0 to250μm,the discharge between the dielectric bead gap changes from a partial discharge to a standing filamentary micro-discharge,which allows the plasma to leave the local area and is conducive to the propagation of surface streamers.In ND,the discharge only appears as a diffusionlike mode between the gap of dielectric beads,regardless of whether there is a discharge gap.Moreover,the generation of excited states N_(2)^(+)(B^(2)∑_(u)^(+))and N2(C^(3)Π_(u))is mainly observed in PD,which is attributed to the higher E/N in PD than that in ND.However,the generation of the OH(A^(2)∑^(+))radical in ND is higher than in PD.It is not directly dominated by E/N,but mainly by the resonant energy transfer process between metastable N_(2)(A^(3)∑_(u)^(+))and OH(X^(2)Π).Furthermore,both PD and ND demonstrate obvious energy relaxation processes of electron-to-vibration and vibration-to-vibration,and no vibration-to-rotation energy relaxation process is observed.

Enhanced Wolf Pack Algorithm (EWPA) and Dense-kUNet Segmentation for Arterial Calcifications in Mammograms

Breast Arterial Calcification(BAC)is a mammographic decision dissimilar to cancer and commonly observed in elderly women.Thus identifying BAC could provide an expense,and be inaccurate.Recently Deep Learning(DL)methods have been introduced for automatic BAC detection and quantification with increased accuracy.Previously,classification with deep learning had reached higher efficiency,but designing the structure of DL proved to be an extremely challenging task due to overfitting models.It also is not able to capture the patterns and irregularities presented in the images.To solve the overfitting problem,an optimal feature set has been formed by Enhanced Wolf Pack Algorithm(EWPA),and their irregularities are identified by Dense-kUNet segmentation.In this paper,Dense-kUNet for segmentation and optimal feature has been introduced for classification(severe,mild,light)that integrates DenseUNet and kU-Net.Longer bound links exist among adjacent modules,allowing relatively rough data to be sent to the following component and assisting the system in finding higher qualities.The major contribution of the work is to design the best features selected by Enhanced Wolf Pack Algorithm(EWPA),and Modified Support Vector Machine(MSVM)based learning for classification.k-Dense-UNet is introduced which combines the procedure of Dense-UNet and kU-Net for image segmentation.Longer bound associations occur among nearby sections,allowing relatively granular data to be sent to the next subsystem and benefiting the system in recognizing smaller characteristics.The proposed techniques and the performance are tested using several types of analysis techniques 826 filled digitized mammography.The proposed method achieved the highest precision,recall,F-measure,and accuracy of 84.4333%,84.5333%,84.4833%,and 86.8667%when compared to other methods on the Digital Database for Screening Mammography(DDSM).

Simulation and Optimization of Energy Efficiency and Total Enthalpy Analysis of Sand Based Packed Bed Solar Thermal Energy Storage

This study is focused on the simulation and optimization of packed-bed solar thermal energy storage by using sand as a storage material and hot-water is used as a heat transfer fluid and storage as well.The analysis has been done by using the COMSOL multi-physics software and used to compute an optimization charging time of the storage.Parameters that control this optimization are storage height,storage diameter,heat transfer fluid flow rate,and sand bed particle size.The result of COMSOL multi-physics optimized thermal storage has been validated with Taguchi method.Accordingly,the optimized parameters of storage are:storage height of 1.4m,storage diameter of 0.4 m,flow rate of 0.02 kg/s,and sand particle size 12 mm.Among these parameters,the storage diameter result is the highest influenced optimized parameter of the thermal storage fromthe ANOVA analysis.For nominal packed bed thermal storage,the charging time needed to attain about 520 K temperature is more than 3500 s,while it needs only about 2000 s for the optimized storage which is very significant difference.Average charging energy efficiency of the optimized is greater than the nominal and previous concrete-based storage by 13.7%,and 13.1%,respectively in the charging time of 2700 s.

DS Smith北美公司推出新的PackRight 2.0多元协作体验系统

7月26日,DSSmith推出了一种多元协作体验系统——PackRight 2.0。新产品的包装设计以互动研讨会为特色,帮助快消品(FMCG)、电子商务和其他行业的企业创新,使用DS Smith专有的循环设计指标可提高包装的可持续性。

得世推出P5 350 HS Pack瓦楞新产品

作为数字印刷和生产技术制造商,得世通过P 5350 HS Pack的推出扩展了其产品组合,P5 Pack系列计划于2024年第三季度上市。P5 Pack系列是专为瓦楞展示架和包装生产量身定制的产品,P5350 HS Pack将P5混合印刷系统的多功能性与在POP/POS和零售市场使用的各种材料上进行印刷的能力相结合。

动力电池PACK智能制造复合型人才定向培养研究

分析了新能源汽车核心能量源——动力电池PACK制造的现状,根据当前动力电池PACK智能制造人才培养的现状,对动力电池PACK智能制造复合型人才定向培养路径进行了研究,为其他跨专业领域的复合型人才培养提供启示。

Banana-shaped electron acceptors with an electron-rich core fragment and 3D packing capability

The emergence of Y6-type nonfullerene acceptors has greatly enhanced the power conversion efficiency(PCE)of organic solar cells(OSCs).However,which structural feature is responsible for the excellent photovoltaic performance is still under debate.In this study,two Y6-like acceptors BDOTP-1 and BDOTP-2 were designed.Different from previous Y6-type acceptors featuring an A–D–Aʹ–D–A structure,BDOTP-1,and BDOTP-2 have no electron-deficient Aʹfragment in the core unit.Instead,there is an electron-rich dibenzodioxine fragment in the core.Although this modification leads to a marked change in the molecular dipole moment,electrostatic potential,frontier orbitals,and energy levels,BDOTP acceptors retain similar three-dimensional packing capability as Y6-type acceptors due to the similar banana-shaped molecular configuration.BDOTP acceptors show good performance in OSCs.High PCEs of up to 18.51%(certified 17.9%)are achieved.This study suggests that the banana-shaped configuration instead of the A–D–Aʹ–D–A structure is likely to be the determining factor in realizing high photovoltaic performance.

Online 3D Packing Problem Based on Bi-Value Guidance

The online 3D packing problem has received increasing attention in recent years due to its practical value. However, the problem itself possesses some peculiar properties, such as sequential decision-making and the large size of the state space, which have made the use of reinforcement learning with Markov decision processes a popular approach for solving this problem. In this paper, we focus on the problem of high variance in value estimation caused by reward uncertainty in the presence of highly uncertain dynamics. To address this, proposed a solution based on auxiliary tasks and intrinsic rewards for the online 3D bin packing problem, guided by a binary-valued network, to assist the agent in learning the policy within the framework of actor-critic deep reinforcement learning. Specifically, the maintenance of two-valued networks and the utilization of multi-valued network estimates are employed to replace the original value estimates, aiming to provide better guidance for the learning of policy networks. Experimentally, it has been demonstrated that our model can achieve more robust learning and outperform previous works in terms of performance.

动力电池模组和PACK装配线的工艺技术应用研究

随着电动自行车共享换电的市场占有率越来越大,各大新能源电池生产企业纷纷建设了动力电池模组线和PACK装配线。为了提高共享换电的动力电池模组和PACK装配组装的工艺水平和生产效率,笔者以某新能源电池包模组和PACK装配线项目为例,介绍了模组和装配线的结构组成,阐述了模组和装配线工艺技术的应用,并对各模块应用做了简要的概述,为相似项目的解决方案提供了参考和依据。

集值映射下Packing熵的变分原理

为了丰富动力系统中熵理论的研究,在度量空间中定义了半度量函数dn,并引入集值映射,在该条件下定义子集上Packing拓扑熵,得到子集上Parking拓扑熵的变分原理为h^(P)_(top)(f,K)=sup{h_(μ)(f):μ∈M(X),μ(K)=1},其中h^(P)_(top)(f,K)是集合K的Packing拓扑熵.

Pressure drop of structured packing in pilot column and comparison to common correlations

Packed columns are widely used in the chemical industry such as absorption,stripping,distillation,and extraction in the production of e.g.organic chemicals,and pharmaceuticals.Pressure loss and pressure drop correlations are of special interest when it comes to the hydrodynamic properties of a column.The pressure loss across the column is of interest in the design phase when the size of the blower to drive the gas stream through the column has to be decided.The loading point and flooding point are also influenced by the pressure loss and the area of operation is determined from these points.This work examines four different correlations on pressure drop.The correlations are(i)Ergun’s equation(1952),(ii)an improved version of Ergun’s equation by Stichlmair,Bravo,and Fair(1989),(iii)an equation developed by Billet and Schultes(1999),and(iv)an equation by Rocha,Bravo,and Fair(1993).The complexity of the correlations is increasing in the mentioned order,Ergun’s equation being the simplest one.This study investigates if the more complicated correlations give better predictions to pressure drop in packed columns.This is determined by comparing the correlations to experimental data for pressure drop in a packed column with 8.2 m of structured packing using water as the liquid and atmospheric air as the gas.Seven experiments were carried out for determining the pressure drop in the column with liquid flows varying from 0 to 500 kg·h^(-1).At constant liquid flow,the gas flow was varied from approximately 10 to 70 kg·h^(-1).The pressure drop across the non-wetted column was best described by the correlation by Rocha et al.while the pressure drop for liquid flows from 100 to 500 kg·h^(-1)was,in general,best described by Stichlmair’s equation.For an irrigated column,the highest deviation was a predicted pressure drop 69.6%lower than measured.The best prediction was 0.1%higher than the measured.This study shows,surprisingly,that for a system of water and atmospheric air,complicated correlations on pressure drop determination do not provide better estimates than simple equations.

THE VARIATIONAL PRINCIPLE FOR THE PACKING ENTROPY OF NONAUTONOMOUS DYNAMICAL SYSTEMS

Let(X,φ) be a nonautonomous dynamical system.In this paper,we introduce the notions of packing topological entropy and measure-theoretical upper entropy for nonautonomous dynamical systems.Moreover,we establish the variational principle between the packing topological entropy and the measure-theoretical upper entropy.

Investigation on catalytic distillation for ethyl acetate production with different catalytic packing structures

The catalytic packing is the core component of the catalytic distillation,and how the catalyst exists in the packing has significant influence on the process.To investigate the effect of catalyst packings on the catalytic distillation process,the classical ethyl acetate reactive distillation system was utilized,and a supported catalytic packing(SCP)was prepared in comparison with the conventional tea-bag catalytic packing(TBP).Laboratory scale experiments showed that the ethyl acetate conversion of the SCP was superior to the TBP at a low catalyst loading.The effects of reaction kinetics,mass transfer performance and actual catalytic efficiency of the packings on this process were regarded as reasons and studied by combining the experiments and numerical simulation.Results suggested that the relatively immediate“in-situ separation”caused by the rapid reaction kinetics and better mass transfer performance of SCP may be a main reason for the difference of the conversion.

Packed Bed Thermocline Thermal Energy Storage for Medium-Temperature Concentrating Solar Systems: Numerical and Experimental Study

Thermal Energy Storage is becoming a necessary component of sustainable energy production systems as it helps alleviate intrinsic limitations of Renewable Energy Sources, such as intermittent use and mismatch between power demand and supply. This paper discusses a packed bed thermocline tank as a thermal energy storage solution. Firstly, this paper presents the development of a numerical model calculating heat transfers within the tank, based on a discretization over several nodes and the nodal formulation of the heat balance equation. The model considers a filler material and a heat transferring fluid and uses the finite difference method to calculate the temperature evolution of the two media across the tank. The model was validated with two different packed bed systems from the literature during a discharging process, presenting a good fit with the experimental results. Secondly, the experimental packed bed is presented and characterized for a charging cycle from ambient temperature to approximately 180?C. The charging experiment was accurately reproduced with the numerical model requiring minimal computational time. Two additional charging modes were simulated with different inlet HTF conditions: constant temperature and varying temperature following the profile produced by a thermal solar collector field. The temperature profiles obtained from the three charging modes were analysed and compared to each other. The proposed numerical and experimental tools will be used in future studies for a better understanding of the design and operating conditions of packed bed thermal energy storage systems.