Problem Of Distribution Research Articles

Inverse problem for thermal radiation distribution on turbine blade under quartz lamp irradiation

Due to the strong time-varying characteristics and complex geometry of aerospace components, rapid changes in the distribution of radiative heat flux on the test surface are often required during non-uniform aerodynamic heating tests. However, it takes time to adjust the geometric parameters of the lamp array, which cannot meet the requirement for rapidly changing radiative heat flux distribution. To address this issue, a new method for calculating radiant heat flux and a fast linear analysis method of quartz lamp power are proposed which can calculate radiant heat flux distribution of complex surface and meet the need of timeliness and rapidity in radiant heat flux distribution, making it more suitable for engineering applications. Through numerical verification under single lamp and quartz lamp array, the maximum difference between the theoretical analysis method and the Monte Carlo method is less than 2.25% under single lamp, and less than 5% under quartz lamp array. Finally, turbine blade model and plane model are taken as research objects to verify the feasibility and reliability of the fast linear analysis method of quartz lamp power. The results show that the relative average error of the calculated quartz lamp power is 5.86% and 14.83%, respectively, compared with the actual power. This provides a reference and basis for the rapid simulation design of thermal radiation environment during the experiment.

An adversarial-based domain generalization method for the health evaluation of axial piston pumps

Abstract The axial piston pump is the power component in hydraulic systems and evaluating its health status is of great importance to the safe operation of hydraulic systems. Discharge pressure signals are common monitoring signals for axial piston pumps, but it is difficult to obtain satisfactory health evaluation results by directly using raw discharge pressure signals since the degradation information lies in some specific frequency bands. Furthermore, the axial piston pump often operates under different operating conditions and most existing deep learning models have low diagnostic accuracies due to the problems of insufficient degradation data and different data distribution. This paper proposes an adversarial-based domain generalization (DG) method by integrating time-frequency analysis and data augmentation, to accurately predict the health status of axial piston pumps under unknown working conditions. First, discharge pressure signals under various operating conditions are decomposed by using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and effective intrinsic mode functions (IMFs) are selected to train multiple convolutional neural network (CNN) models. Second, a novel data augmentation method based on the modified discrete cosine transform-composite spectrum (DCS) algorithm is introduced to fuse the IMFs from different domains and generate pseudo data sets. Finally, the adversarial training is adopted between the real data and the pseudo data to capture domain-generalized features. The discharge pressure signal of an actual axial piston pump at different health levels were collected on a test bench to verify the effectiveness of the proposed method. Results indicate that the proposed method has a higher prediction accuracy than the comparative methods.

An improved prairie dog optimization algorithm integrating multiple strategies and its application

Aiming at the problems in prairie dog optimization (PDO), such as uneven population distribution at initialization, slow convergence, imbalance between global exploration and local exploitation, and the tendency to get trapped in the local optimum, this study proposes an Improved prairie dog optimisation algorithm integrating multiple strategies (IMSPDO). Firstly, the population is initialized using spatial pyramid matching (SPM) chaotic mapping combined with improved random opposition-based learning (IROL) to solve the problems of uneven distribution and poor diversity of the population. Secondly, the prey escapes energy formula mentioned in the harris hawks optimization (HHO) is introduced to achieve the smooth transition between the exploration phase and the exploitation phase, balancing the algorithm’s global exploration capability and local exploitation capability. Additionally, the idea of the particle swarm optimization (PSO) is applied to enhance the global optimization capability of the algorithm. Finally, the ideas of simulated annealing (SA), polynomial mutation and Cauchy mutation are also introduced to improve the ability that individuals to jump out of the local optimum. The performance of the improved algorithm is verified on a set of 21 classical benchmark functions and 8 CEC2020 test functions. The proposed IMSPDO is also evaluated against original PDO, and six other commonly used algorithms. The result of the Wilcoxon rank-sum test shows that there is a significant difference between the selected algorithms and IMSPDO. Furthermore, 3 engineering examples are used to further test the superiority of IMSPDO in dealing with real-world problems.

Feasibility analysis of the Sanchez-Pomraning method to treat the particle size distribution in particle-dispersed fuel

The non-uniform size of particles appears in the particle-dispersed fuel generally, but this phenomenon is usually neglected. This study aims to evaluate the feasibility of the Sanchez-Pomraning method to solve the particle size distribution problem in particle-dispersed fuel. The Pu-rich agglomerate phenomenon in the MOX fuel and manufacturing tolerance in the Fully Ceramic Microencapsulated (FCM) fuel are calculated by ALPHA-DH code, which implements the Sanchez-Pomraning method based on the MOC transport calculation and improved subgroup resonance calculation. Numerical results show that the Sanchez-Pomraning method has the capability to treat the particle size distribution effect accurately, where the differences between the Sanchez-Pomraning method and the Monte Carlo method are generally lower than 160 pcm. For the Pu-rich agglomerate phenomenon, the results show that ignoring the Pu-rich agglomerate effect introduces the reactivity difference of around 600 pcm, and ignoring the size distribution of the Pu agglomerate will cause the reactivity of up to 200 pcm in the extra-high or extra-low Pu enrichment. The more discrete size distribution and the smaller Pu-spots, the more significant influence will occur in the MOX fuel. For the FCM fuel, the influence brought by the TRISO particle size distribution can be ignored when only TRISO particles are filled. However, ignoring the size distribution when TRISO particles and poison particles are both added into the FCM fuel introduces the differences reaching 600–1000 pcm. These results demonstrate the importance of considering the particle size distribution for analyzing the MOX fuel and FCM fuel in practice. And the Sanchez-Pomraning method is reliable to analyze the effect of particle size distribution.

Literature Review on Fresh Products Cold Chain Logistics and Distribution Issues

In this paper, the literature on the concepts of fresh product cold chain logistics, the container operation problems related to cold chain transport, product order picking, distribution and the integration of the two problems are sorted out to provide theoretical references for the further study of the transport and distribution problems of fresh product cold chain logistics. Through combing the literature at home and abroad, the current fresh product cold chain logistics transport and distribution mainly exists in the container intermodal transport research object is single, mostly focusing on the railway container centre station; order distribution problems related research is less, vehicle path problem solving algorithms and related mathematical models are less; order picking and distribution of the joint research is less, and most of them stay in the theoretical stage, the method of landing is more difficult and other problems.

The method of distributing the costs of growing perennial non-bloodless grasses by types of products

The introduction of modern technologies for growing perennial grasses in feed production has caused the need to improve the methodology for allocating costs by product type. The article describes the current method of distributing the costs of cultivation. The problems of distribution of these costs are revealed. It is proposed to open two analytical accounts for the sowing of a grass mixture of perennial uncovered grasses for a more accurate distribution of costs in the future, in the year of sowing: «The cost of tillage and sowing of perennial grasses », «The cost of caring for crops and harvesting perennial grasses». A new method of cost allocation by types of perennial herb products has been developed.

Multi-scale large kernel convolution and hybrid attention network for remote sensing image dehazing

Remote sensing (RS) image dehazing holds significant importance in enhancing the quality and information extraction capability of RS imagery. The enhancement in image dehazing quality has progressively advanced alongside the evolution of convolutional neural network (CNN). Due to the fixed receptive field of CNN, there is insufficient utilization of contextual information on haze features in multi-scale RS images. Additionally, the network fails to adequately extract both local and global information of haze features. In addressing the above problems, in this paper, we propose an RS image dehazing network based on multi-scale large kernel convolution and hybrid attention (MKHANet). The network is mainly composed of multi-scale large kernel convolution (MSLKC) module, hybrid attention (HA) module and feature fusion attention (FFA) module. The MSLKC module fully fuses the multi-scale information of features while enhancing the effective receptive field of the network by parallel multiple large kernel convolutions. To alleviate the problem of uneven distribution of haze and effectively extract the global and local information of haze features, the HA module is introduced by focusing on the importance of haze pixels at the channel level. The FFA module aims to boost the interaction of feature information between the network's deep and shallow layers. The subjective and objective experimental results on on multiple RS hazy image datasets illustrates that MKHANet surpasses existing state-of-the-art (SOTA) approaches. The source code is available at https://github.com/tohang98/MKHA_Net.

Optimal distribution of limited resource in a context of uncertainty

Object of research: methods of rational distribution of limited resource in a context of uncertainty. It is assumed that the system consists of a set of independently functioning elements, and effectiveness of these elements depends on the level of investment. The complexity of solving the problem using traditional methods depends on and is determined by the type of analytical description of production functions of the system elements. Significant difficulties arise if parameters of production functions are uncertain quantities, specified, for example, in terms of fuzzy mathematics. This circumstance emphasizes the relevance of studying approaches to solving the set problem of resource distribution for the case when its parameters are not clearly defined, and this circumstance also determines the goal of the work. Problems arising from the goal consist in developing mathematical models and methods for rational resource distribution for the main types of production functions with their parameters being fuzzy numbers of (L –R) type. To solve these problems, a method is proposed that transforms the original fuzzy problems into clear ones that can be solved using standard constrained optimization technologies. An important result of the research consists in the fact that the proposed method is universal, that is, it is applied in the same way to solve problems of resource distribution in systems wherein production functions of the elements can be of arbitrary nature. Refs.: 11 titles.

Fine-grained concrete properties in depending on carbon black additive introducing methods

The object of research is fine-grained concrete with carbon black additive obtained from the recycling of rubber tires. This study aims to determine a method for introducing carbon black into fine-grained concrete that achieves the best strength characteristics. The first method involves the preliminary preparation of a suspension by mixing a superplasticizer, carbon black, and part of the water. The second is the granulation of carbon black together with a superplasticizer in the ratio water: superplasticizer = 9: 1, followed by its introduction into the dry mixture and the addition of water at the last stage of mixing the raw material mixture. The addition of carbon black in powder form to the concrete mixture resulted in a decrease in strength from 35.1 MPa (without carbon black) to 34.9–25.6 MPa (with 4–12% carbon black). The effectiveness of the method of introducing granular carbon black was established, with an increase in strength by 40% in relation to the mixture obtained by the first method. The best strength of 50 MPa was demonstrated by the mix with 4% granular carbon black, which was 6% higher than that of the reference mix. Granulation of carbon black makes it possible to reduce its hydrophobicity and solve the problem of uniform distribution of carbon particles during homogenization of the raw material mixture in the presence of water.

Synthesis of sparse rectangular planar arrays with weight function and improved grey wolf optimization algorithm

AbstractThe present article proposes a novel hybrid approach for addressing the synthesis problem of rectangular planar arrays under multiple constraints, through joint optimization of the weight function and the improved grey wolf optimization (IGWO) algorithm. Firstly, the grey wolf optimization (GWO) algorithm is improved by using tent chaotic mapping, nonlinear convergence factor, dominant wolf dynamic belief strategy, and opposition‐based learning strategy to increase the population diversity and the ability to jump out of the local optimum. Secondly, the array elements are weighted using the weight function to generate the position distribution matrix, which reduces the thinned matrix optimization time and improves the optimization efficiency. Finally, the position distribution matrix is used to generate the thinned array, and the IGWO algorithm is applied to perform the sparse optimization with multiple constraints. The effectiveness of the method is verified through numerical simulation and full‐wave simulation experiments, demonstrating its capability to enhance array antenna performance and reduce peak sidelobe level (PSLL). These experimental results hold significant engineering implications and provide valuable references for addressing the array distribution problem under multiple constraints.

A virtual-reality spatial matching algorithm and its application on equipment maintenance support: System design and user study

Equipment maintenance support is an important technical measure to maintain the equipment’s expected performance. However, the current maintenance supports are mainly completed by maintainers under the guidance of technical manual or additional experts, which may be insufficient for some advanced equipment with rapid update rate and complex inner structure. The rising technology of augmented reality (AR) provides a new solution for equipment maintenance support, while one of the key issues limiting the practical application of AR in maintenance field is the spatial matching issue between virtual space and reality space. In this paper, a virtual-reality spatial matching algorithm is designed to accurately superimpose the virtual information to the corresponding actual scene on the AR glasses. In this algorithm, two methods are proposed to help achieve the stable matching of virtual space and reality space. In detail, to obtain the saliency map with less background interference and improved saliency detection accuracy, a saliency detection method is designed based on the super-pixel segmentation. To deal with the problems of uneven distribution on the feature points and weak robustness to the light changes, a feature extraction and matching method is proposed for acquiring the feature point matching set with the utilization of the obtained saliency map. Finally, an immersive equipment maintenance support system (IEMSS) is developed based on this spatial matching algorithm, which provides the maintainers with immediate and immersive guidance to improve the efficiency and safety in the maintenance task, as well as offers maintenance training for inexperienced maintainers with expanded virtual information in case of limited experts. Several comparative experiments are implemented to verify the effectiveness of proposed methods, and a user study of real system application is carried out to further evaluate the superiority of these methods when applied in the IEMSS.

Investigating of Carbon Agent Distribution Using Long-Chain Alkyl in Grafted Binder for High-Capacity SiOx/Graphite Anode

A small amount of the conductive carbon agent in lithium-ion battery (LIB) formulation has a significantly impact on the rate capability performance of battery but the distribution problem of the carbon conductive material is issued in the electrode slurry due to its hydrophobic and inert characteristics. To overcome this problem, the use of dispersant agent was proposed in the electrode fabrication process.In this study, the grafting of adhesive acrylamide monomer (AM) and a long-chain alkyl monomer onto carboxymethyl cellulose (CMC) is applied to binder to improve the conductive agent distribution in a high-capacity SiOx/C anode. The long alkyl chain in the monomer of the graft copolymer binder exhibits good dispersibility of the carbon agent and improved conductive matrix compared to the binder with no alkyl monomer. This may be explained by CH-π integration between the alkyl monomer in graft polymer and carbon agent. The high-capacity SiOx/C anode with the long-alkyl chain introduced graft binder is examined in the slurry level with rheology and dispersion stability, in the electrode sheet level with electric resistance, peel-off, and coin-cell level with various electrochemical performance, charge/discharge performance. Keywords: Silicon oxide; Graphite; Carbon agent distribution, long-chain alkyl monomer; Acrylamide; Graft copolymer Figure 1

Processing correlations of laser direct energy deposited Inconel718 based on multi-field numerical simulation

Laser direct energy deposited (LDED) Inconel718 always have problems of non-uniform temperature distribution because of the complex flow of molten pool and layer-by-layer fabrication characterization which directly affected the microstructure and mechanical properties of materials. In this work, Inconel718 revealed a refined grains and a weave structure exhibiting excellent mechanical properties which were superior to those of the conventional casting process were successfully fabricated by adopt a high-precision and intelligent coaxial powder feeding system during the LDED processing. Moreover, based on multi-field numerical simulation method, a two-phase flow model and a transient thermal mechanical deformation model were established to simulate the LDED processing, while takes into account the evaporated mass loss of the inconel718 alloy. The morphology prediction of laser directed energy deposition by multi-physics numerical simulation, the relationship among the laser process parameters, i.e., the laser power, scanning speed and the amount of powder feeding, the temperature filed and the microstructure and mechanical properties of Inconel718 alloy during LDED were investigated and established. The simulated results consistent well with the experimental results. Results shown that the relative density of sample was sensitive to the laser power, the precision and stability of the powder feeding rate. With increasing laser power, the grain size of Inconel718 is refined, and the microhardness and tensile properties of the materials increased. The optimized LDED processing parameters were determined. Evaluating the mechanical properties of the materials LDEDed at a laser power of 600W and a scanning speed of 8.5mms-1 exhibited a high tensile strength of 944±5 MPa and a good elongation of 29 ± 0.5 %. This work suggests that the processing parameters can be optimized to prepare densified materials with excellent mechanical properties.

A multi-objective brain storm optimization for integrated distributed flexible job shop and distribution problems

Production and distribution are critical components of the furniture supply chain, and achieving optimal performance through their integration has become a vital focus for both the academic and business communities. Moreover, as economic globalization progresses, distributed manufacturing has become a pioneering production technique. Via leveraging a distributed flexible manufacturing system, mass flexible production at lower costs can be achieved. To this end, this study presents an integrated distributed flexible job shop and distribution problem to minimize makespan and total tardiness. In our research, a set of custom furniture orders from different customers are processed among flexible job shops and then delivered by vehicles to customers as the due date. To distinctly show the presented problem, a mixed integer mathematical programming model is created, and a multi-objective brain storm optimization method is introduced considering the problem's features. In comparison to the other three advanced methods, the superiority of the algorithm created is showcased. The findings of the experiments demonstrate that the constructed model and the introduced algorithm have remarkable competitiveness in addressing the problem being examined.

Sedimentary build-ups of pre-salt isolated carbonate platforms and formation of deep-water giant oil fields in Santos Basin, Brazil

In response to the problems of unclear distribution of deep-water pre-salt carbonate reservoirs and formation conditions of large oil fields in the Santos passive continental margin basin, based on comprehensive utilization of geological, seismic, and core data, and reconstruction of Early Cretaceous prototype basin and lithofacies paleogeography, it is proposed for the first time that the construction of pre-salt carbonate build-ups was controlled by two types of isolated platforms: inter-depression fault-uplift and intra-depression fault-high. The inter-depression fault-uplift isolated platforms are distributed on the present-day pre-salt uplifted zones between depressions, and are built on half- and fault-horst blocks that were inherited and developed in the early intra-continental and inter-continental rift stages. The late intra-continental rift coquinas of the ITP Formation and the early inter-continental rift microbial limestones of the BVE Formation are continuously constructed; intra-depression fault-high isolated platforms are distributed in the current pre-salt depression zones, built on the uplifted zones formed by volcanic rock build-ups in the early prototype stage of intra-continental rifts, and only the BVE microbial limestones are developed. Both types of limestones formed into mound-shoal bodies, that have the characteristics of large reservoir thickness and good physical properties. Based on the dissection of large pre-salt oil fields discovered in the Santos Basin, it has been found that both types of platforms could form large-scale combined structural-stratigraphic traps, surrounded by high-quality lacustrine and lagoon source rocks at the periphery, and efficiently sealed by thick high-quality evaporite rocks above, forming the optimal combination of source, reservoir and cap in the form of “lower generation, middle storage, and upper cap”, with a high degree of oil and gas enrichment. It has been found that the large oil fields are all bottom water massive oil fields with a unified pressure system, and they are all filled to the spill-point. The future exploration is recommended to focus on the inter-depression fault-uplift isolated platforms in the western uplift zone and the southern section of eastern uplift zones, as well as intra-depression fault-high isolated platforms in the central depression zone. The result not only provides an important basis for the advanced selection of potential play fairways, bidding of new blocks, and deployment of awarded exploration blocks in the Santos Basin, but also provides a reference for the global selection of deep-water exploration blocks in passive continental margin basins.

Hot spot localization in the field of view of the Kirkpatrick–Baez microscope

The Kirkpatrick–Baez (KB) microscope is an effective instrument for x-ray imaging of hot spot. However, the non-uniform distribution of response efficiency in the field of view is a drawback of the KB microscope. A more accurate hot spot image requires the correction of the measured image by combining the hot spot position and the response efficiency distribution. Here, we describe a method to locate the position of the hot spot in the field of view during hot spot imaging with a KB microscope. The position of the hot spot in the field of view can be obtained by measuring the grazing incidence angle change during hot spot imaging. In the experiment of hot spot self-emission imaging with a four-channel KB microscope, the location of the hot spot with an accuracy of 15 μm was realized, and the intensity corrected hot spot image was obtained. This will solve the problem of the non-uniform distribution of the response efficiency of the KB microscope and enable quantitative measurement of hot spot radiation intensity.

Vehicle routing problem for cold-chain drug distribution with epidemic spread situation

We investigate a vehicle routing problem considering the influence of epidemic spread (VRP-ES) for the design of a novel cold-chain drug distribution system, in which the disease spread model is used to capture virus transmission characteristics and demand fluctuations. To this end, we aim to minimize the total travel time and transmission risk of the distribution network by incorporating realistic features including priority distribution and temperature control. We propose a hybrid tabu search heuristic (HTS) with a specifically designed initial solution, several neighborhood operators, and diversification strategies to solve this problem. A series of numerical experiments are conducted to test the proposed solution methodology. The virus spread model and vehicle routing results are discussed to analyze the VRP-ES optimization strategies through an empirical case study of Chongqing city in China. Sensitivity analysis is conducted to identify the impact of various parameters on the VRP-ES and provide some management implications as well.

Central versus deployed drinking water treatment

Nowadays, the level of urbanization is in rapid increasing, but the freshwater resources still have the problems of unbalanced distribution of regions and time. Besides, the freshwater resources are still in short supply. However, in fact, there is always a hidden resource in the city - the wastewater. The rapid population growth and increasing urbanization have increased the needs for wastewater treatment and recycling to ensure water resources adequacy, and then improve the environmental quality. In this day and age, there are two main ways to treat wastewater: centralized treatment and decentralized treatment. Both of these methods can reduce the pollution of wastewater to water resources and provide domestic water. In this article, we will introduce and compare the two approaches with real-world examples: The Shanghai Bailonggang Sewage Treatment Plant, located in Chaoyang Village, Heqing Town, Pudong New Area, and the decentralized sewage treatment system in Germany.

Digital mobile fronthaul scheme based on diff-delta-sigma modulation and OCT precoding.

In this Letter, we experimentally demonstrate digital mobile fronthaul (MFH) for 65536 quadrature amplitude modulation (65536-QAM) signals based on a diff-delta-sigma modulation (D-DSM) scheme with orthogonal circulant matrix transform (OCT) precoding. By combining the D-DSM scheme with OCT precoding, we successfully solved the problem of uneven distribution of in-band quantization noise (IBN) while bringing about a quantization SNR gain of about 1.5 dB. In addition, we compare two signal combination schemes, Gray coding and power superposition, in the D-DSM scheme. The results show that the power superposition scheme can achieve similar performance to Gray coding with lower computational complexity. At the same time, the power superposition scheme is less affected by the saturation effect. Therefore, the D-DSM solution combined with OCT precoding and power superposition provides an effective solution for future 6 G digital mobile fronthaul.

Research on the Balanced Development of Urban and Rural Compulsory Education Through Education Resource Allocation

With the rapid development of our country’s economy, education has gradually become the focus of social attention. The problem of unbalanced distribution of urban and rural educational resources has become increasingly prominent, urban educational resources are relatively rich, while rural educational resources are relatively scarce, and the balanced development of urban and rural compulsory education has become an urgent task. This paper mainly investigates and studies the distribution of urban and rural educational resources, discusses the unbalanced distribution of urban and rural educational resources and analyzes the reasons, and puts forward a series of corresponding solutions to promote the balanced development of urban and rural compulsory education.