Obtaining residual stress is crucial for controlling the machining deformation in annular parts,and can directly influence the performance and stability of key components in advanced equipment.Since existing research ...Obtaining residual stress is crucial for controlling the machining deformation in annular parts,and can directly influence the performance and stability of key components in advanced equipment.Since existing research has achieved global residual stress field inference for components by using the deformation force-based method where the deformation force is monitored during the machining process,reliable acquisition of deformation force stll remains a significant challenge under complex machining conditions.This paper proposes a hierarchical optimization method for the layout of deformation force monitoring of annular parts.The proposed method establishes two optimization objectives by analyzing the relationship between the deformation force and the residual stress in annular parts,i.e.,equivalence and ilconditioning of solving process.Specifically,the equivalence of the monitored deformation force and residual stress in terms of effect on caused machining deformation is evaluated by local deformation,and the illconditioning is also optimized to enhance the stability of residual stress inference.Verification is implemented in both simulation and actual machining experiments,demonstrating effectiveness of the proposed layout optimization method in inferring residual stress field of annular parts with deformation force.展开更多
Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to en...Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.展开更多
The rapid expansion of offshore wind energy necessitates robust and cost-effective electrical collector system(ECS)designs that prioritize lifetime operational reliability.Traditional optimization approaches often sim...The rapid expansion of offshore wind energy necessitates robust and cost-effective electrical collector system(ECS)designs that prioritize lifetime operational reliability.Traditional optimization approaches often simplify reliability considerations or fail to holistically integrate them with economic and technical constraints.This paper introduces a novel,two-stage optimization framework for offshore wind farm(OWF)ECS planning that systematically incorporates reliability.The first stage employs Mixed-Integer Linear Programming(MILP)to determine an optimal radial network topology,considering linearized reliability approximations and geographical constraints.The second stage enhances this design by strategically placing tie-lines using a Mixed-Integer Quadratically Constrained Program(MIQCP).This stage leverages a dynamic-aware adaptation of Multi-Source Multi-Terminal Network Reliability(MSMT-NR)assessment,with its inherent nonlinear equations successfully transformed into a solvable MIQCP form for loopy networks.A benchmark case study demonstrates the framework’s efficacy,illustrating how increasing the emphasis on reliability leads to more distributed and interconnected network topologies,effectively balancing investment costs against enhanced system resilience.展开更多
An increasing number of researchers have researched fixture layout optimization for thin-walled part assembly during the past decades.However,few papers systematically review these researches.By analyzing existing lit...An increasing number of researchers have researched fixture layout optimization for thin-walled part assembly during the past decades.However,few papers systematically review these researches.By analyzing existing literature,this paper summarizes the process of fixture layout optimization and the methods applied.The process of optimization is made up of optimization objective setting,assembly variation/deformation modeling,and fixture layout optimization.This paper makes a review of the fixture layout for thin-walled parts according to these three steps.First,two different kinds of optimization objectives are introduced.Researchers usually consider in-plane variations or out-of-plane deformations when designing objectives.Then,modeling methods for assembly variation and deformation are divided into two categories:Mechanism-based and data-based methods.Several common methods are discussed respectively.After that,optimization algorithms are reviewed systematically.There are two kinds of optimization algorithms:Traditional nonlinear programming and heuristic algorithms.Finally,discussions on the current situation are provided.The research direction of fixture layout optimization in the future is discussed from three aspects:Objective setting,improving modeling accuracy and optimization algorithms.Also,a new research point for fixture layout optimization is discussed.This paper systematically reviews the research on fixture layout optimization for thin-walled parts,and provides a reference for future research in this field.展开更多
Ship pipe layout optimization is one of the difficulties and hot spots in ship intelligent production design.A high-dimensional vector coding is proposed based on the research of related pipe coding and ship pipe rout...Ship pipe layout optimization is one of the difficulties and hot spots in ship intelligent production design.A high-dimensional vector coding is proposed based on the research of related pipe coding and ship pipe route features in this paper.The advantages of this coding method are concise structure,strong compatibility,and independence from the gridding space.Based on the proposed coding,the particle swarm optimization algorithm is implemented,and the algorithm is improved by the pre-selected path strategy and the branch-pipe processing strategy.Finally,two simulation results reveal that the proposed coding and algorithm have feasibility and engineering practicability.展开更多
The layout optimization design of a natural gas gathering pipeline network is a multi-objective optimization problem because the extant theories are unable to meet the different decision preferences in scheme design,w...The layout optimization design of a natural gas gathering pipeline network is a multi-objective optimization problem because the extant theories are unable to meet the different decision preferences in scheme design,which restricts the intelligentization of gas gathering pipeline layout optimization.Currently,there are no generic design studies on the loop-star pipeline network.Therefore,this paper proposes a generic layout optimization model containing a large number of discrete and continuous variables,such as pipe connection relationships,pipe sizes,pipe length,and pipe specifications.In the solution section,drawing inspiration from the hormone regulation mechanism and local foraging rule in bionics,an improved particle swarm optimization algorithm based on hormone regulation(HRPSO)is proposed,and it obtains the favorable parameters range of the HRPSO algorithm.The results illustrate that the HRPSO algorithm exhibits convergence to the global optimum with a probability of 1.In comparison to manual design,the comprehensive costs of the optimized scheme are saved by 22.71%with the HRPSO algorithm.Compared to the four PSO variants in the paper,it can save costs by 5.38%,4.95%,4.09%,and 3.65%,respectively.展开更多
Energy issues have always been one of the most significant concerns for scientists worldwide.With the ongoing over exploitation and continued outbreaks of wars,traditional energy sources face the threat of depletion.W...Energy issues have always been one of the most significant concerns for scientists worldwide.With the ongoing over exploitation and continued outbreaks of wars,traditional energy sources face the threat of depletion.Wind energy is a readily available and sustainable energy source.Wind farm layout optimization problem,through scientifically arranging wind turbines,significantly enhances the efficiency of harnessing wind energy.Meta-heuristic algorithms have been widely employed in wind farm layout optimization.This paper introduces an Adaptive strategy-incorporated Integer Genetic Algorithm,referred to as AIGA,for optimizing wind farm layout problems.The adaptive strategy dynamically adjusts the placement of wind turbines,leading to a substantial improvement in energy utilization efficiency within the wind farm.In this study,AIGA is tested in four different wind conditions,alongside four other classical algorithms,to assess their energy conversion efficiency within the wind farm.Experimental results demonstrate a notable advantage of AIGA.展开更多
With the growing need for renewable energy,wind farms are playing an important role in generating clean power from wind resources.The best wind turbine architecture in a wind farm has a major influence on the energy e...With the growing need for renewable energy,wind farms are playing an important role in generating clean power from wind resources.The best wind turbine architecture in a wind farm has a major influence on the energy extraction efficiency.This paper describes a unique strategy for optimizing wind turbine locations on a wind farm that combines the capabilities of particle swarm optimization(PSO)and artificial neural networks(ANNs).The PSO method was used to explore the solution space and develop preliminary turbine layouts,and the ANN model was used to fine-tune the placements based on the predicted energy generation.The proposed hybrid technique seeks to increase energy output while considering site-specific wind patterns and topographical limits.The efficacy and superiority of the hybrid PSO-ANN methodology are proved through comprehensive simulations and comparisons with existing approaches,giving exciting prospects for developing more efficient and sustainable wind farms.The integration of ANNs and PSO in our methodology is of paramount importance because it leverages the complementary strengths of both techniques.Furthermore,this novel methodology harnesses historical data through ANNs to identify optimal turbine positions that align with the wind speed and direction and enhance energy extraction efficiency.A notable increase in power generation is observed across various scenarios.The percentage increase in the power generation ranged from approximately 7.7%to 11.1%.Owing to its versatility and adaptability to site-specific conditions,the hybrid model offers promising prospects for advancing the field of wind farm layout optimization and contributing to a greener and more sustainable energy future.展开更多
Kirigami metamaterials have gained increasing attention due to their unusual mechanical properties under largestretching.However,most metamaterial designs obtained with trial-and-error approaches tend to lose their de...Kirigami metamaterials have gained increasing attention due to their unusual mechanical properties under largestretching.However,most metamaterial designs obtained with trial-and-error approaches tend to lose their desirable properties under large tensile strains due to occurrence of instability caused by out-of-plane buckling.To cope with this limitation,this paper presents a systematic approach of cut layout optimizing for designingkirigami metamaterials working at large tensile strains by fully exploiting their out-of-plane buckling behaviors.This method can also mitigate the local stress concentration issue at the hinges of conventional kirigami designsworking at in-plane deformation modes.The effectiveness of the proposed method is demonstrated through several examples regarding metamaterial design with negative Poisson’s ratio and specified flip angle pattern.It isshown that the proposed method is capable of addressing the highly nonlinear deformation impacts on the mechanical performance under large stretching,to meet the growing and diverse demands in the field of kirigamimetamaterials.展开更多
Over the past decade, Graphics Processing Units (GPUs) have revolutionized high-performance computing, playing pivotal roles in advancing fields like IoT, autonomous vehicles, and exascale computing. Despite these adv...Over the past decade, Graphics Processing Units (GPUs) have revolutionized high-performance computing, playing pivotal roles in advancing fields like IoT, autonomous vehicles, and exascale computing. Despite these advancements, efficiently programming GPUs remains a daunting challenge, often relying on trial-and-error optimization methods. This paper introduces an optimization technique for CUDA programs through a novel Data Layout strategy, aimed at restructuring memory data arrangement to significantly enhance data access locality. Focusing on the dynamic programming algorithm for chained matrix multiplication—a critical operation across various domains including artificial intelligence (AI), high-performance computing (HPC), and the Internet of Things (IoT)—this technique facilitates more localized access. We specifically illustrate the importance of efficient matrix multiplication in these areas, underscoring the technique’s broader applicability and its potential to address some of the most pressing computational challenges in GPU-accelerated applications. Our findings reveal a remarkable reduction in memory consumption and a substantial 50% decrease in execution time for CUDA programs utilizing this technique, thereby setting a new benchmark for optimization in GPU computing.展开更多
The general layout of 6th generation semi-submersible drilling platforms is the main factor impacting the efficiency of their drilling operations. This paper provides a compound/integrated algorithm based on process f...The general layout of 6th generation semi-submersible drilling platforms is the main factor impacting the efficiency of their drilling operations. This paper provides a compound/integrated algorithm based on process flow that is aimed at improving efficiency, while giving attention to stability and safety at the same time. The paper describes the process flow of dual drilling centers and a hierarchical division of rigs based on the different modes of transportation of various drilling support systems. The general layout-centripetal overall arrangement spatially was determined based on drilling efficiency. We derived our modules according to drilling functionality; the modules became our basic layout units. We applied different layout algorithm to mark out the upper and lower decks. That is, the upper deck was designed based on the lowest transportation cost while the lower deck＇s calculations were based on the best-fit scope. Storage configurations in columns and pontoons were also considered for the layout design. Finally the center of gravity was taken into consideration and the general layout was adjusted accordingly, to result in an optimal center of gravity. The methodology of the general layout can provide a reference for implementation of domestic designs of semi-submersible rigs.展开更多
Volcanic terrains exhibit a complex structure of pyroclastic deposits interspersed with sedimentary processes,resulting in irregular lithological sequences that lack lateral continuity and distinct stratigraphic patte...Volcanic terrains exhibit a complex structure of pyroclastic deposits interspersed with sedimentary processes,resulting in irregular lithological sequences that lack lateral continuity and distinct stratigraphic patterns.This complexity poses significant challenges for slope stability analysis,requiring the development of specialized techniques to address these issues.This research presents a numerical methodology that incorporates spatial variability,nonlinear material characterization,and probabilistic analysis using a Monte Carlo framework to address this issue.The heterogeneous structure is represented by randomly assigning different lithotypes across the slope,while maintaining predefined global proportions.This contrasts with the more common approach of applying probabilistic variability to mechanical parameters within a homogeneous slope model.The material behavior is defined using complex nonlinear failure criteria,such as the Hoek-Brown model and a parabolic model with collapse,both implemented through linearization techniques.The Discontinuity Layout Optimization(DLO)method,a novel numerical approach based on limit analysis,is employed to efficiently incorporate these advances and compute the factor of safety of the slope.Within this framework,the Monte Carlo procedure is used to assess slope stability by conducting a large number of simulations,each with a different lithotype distribution.Based on the results,a hybrid method is proposed that combines probabilistic modeling with deterministic design principles for the slope stability assessment.As a case study,the methodology is applied to a 20-m-high vertical slope composed of three lithotypes(altered scoria,welded scoria,and basalt)randomly distributed in proportions of 15%,60%,and 25%,respectively.The results show convergence of mean values after approximately 400 simulations and highlight the significant influence of spatial heterogeneity,with variations of the factor of safety between 5 and 12 in 85%of cases.They also reveal non-circular and mid-slope failure wedges not captured by traditional stability methods.Finally,an equivalent normal probability distribution is proposed as a reliable approximation of the factor of safety for use in risk analysis and engineering decision-making.展开更多
This research systematically investigates urban three-dimensional greening layout optimization and smart ecocity construction using deep learning and remote sensing technology.An improved U-Net++ architecture combined...This research systematically investigates urban three-dimensional greening layout optimization and smart ecocity construction using deep learning and remote sensing technology.An improved U-Net++ architecture combined with multi-source remote sensing data achieved high-precision recognition of urban three-dimensional greening with 92.8% overall accuracy.Analysis of spatiotemporal evolution patterns in Shanghai,Hangzhou,and Nanjing revealed that threedimensional greening shows a development trend from demonstration to popularization,with 16.5% annual growth rate.The study quantitatively assessed ecological benefits of various three-dimensional greening types.Results indicate that modular vertical greening and intensive roof gardens yield highest ecological benefits,while climbing-type vertical greening and extensive roof gardens offer optimal benefit-cost ratios.Integration of multiple forms generates 15-22% synergistic enhancement.Compared with traditional planning,the multi-objective optimization-based layout achieved 27.5% increase in carbon sequestration,32.6% improvement in temperature regulation,35.8% enhancement in stormwater management,and 42.3% rise in biodiversity index.Three pilot projects validated that actual ecological benefits reached 90.3-102.3% of predicted values.Multi-scenario simulations indicate optimized layouts can reduce urban heat island intensity by 15.2-18.7%,increase carbon neutrality contribution to 8.6-10.2%,and decrease stormwater runoff peaks by 25.3-32.6%.The findings provide technical methods for urban three-dimensional greening optimization and smart eco-city construction,promoting sustainable urban development.展开更多
The paper proposes four indicators to guide sensors layout in practical experiment on explosion overpressure filed construction based on tomographic method with high reconstruction accuracy and the least sensors. Firs...The paper proposes four indicators to guide sensors layout in practical experiment on explosion overpressure filed construction based on tomographic method with high reconstruction accuracy and the least sensors. First, genetic algorithm is adopted to conduct global search and sensor layout optimization method is selected to satisfy four indicators. Then, by means of Matlab, the variation of these four indicators with different sensor layouts and reconstruction accuracy are analyzed and discussed. The results indicate that the sensor layout method proposed by this paper can reconstruct explosion overpressure field at the highest precision by a minimum number of sensors. It will guide actual explosion experiments in a cost-effective way.展开更多
A two-level layout optimization strategy is proposed in this paper for large-scale composite wing structures. Design requirements are adjusted at the system level according to structural deformation, while the layout ...A two-level layout optimization strategy is proposed in this paper for large-scale composite wing structures. Design requirements are adjusted at the system level according to structural deformation, while the layout is optimized at the subsystem level to satisfy the constraints from system level. The approaching degrees of various failure critical loads in wing panels are employed to gauge the structure’s carrying efficiency. By optimizing the efficiency as an objective, the continuity of the problem could be guaranteed. Stiffened wing panels are modeled by the equivalent orthotropic plates, and the global buckling load is predicted by energy method. The nonlinear effect of stringers’ support elasticity on skin local buckle resistance is investigated and approximated by neural network （NN） surrogate model. These failure predictions are based on analytical solutions, which could effectively save calculation resources. Finally, the integral optimization of a large-scale wing structure is completed as an example. The result fulfills design requirements and shows the feasibility of this method.展开更多
Stiffened plates or shells are widely used in engineering structures as primary or secondary load-bearing components.How to design the layout and sizes of the stiffeners is of great significance for structural lightwe...Stiffened plates or shells are widely used in engineering structures as primary or secondary load-bearing components.How to design the layout and sizes of the stiffeners is of great significance for structural lightweight.In this work,a new topology optimization method for simultaneously optimizing the layout and cross-section topology of the stiffeners is developed to solve this issue.The stilfeners and base plates are modeled by the beam and shell elements,respectively,significantly reducing the computational cost.The Giavotto beam theory,instead of the widely employed Euler or Timoshenko beam theory,is applied to model the stiffeners for considering the warping deformation in evaluating the section stiffness of the beam.A multi-scale topology optimization model is established by simultaneously optimizing the layout of the beam and the topology of the cross-section.The design space is significantly expanded by optimizing these two types of design variables.Several numerical examples are applied to illustrate the validity and effectiveness of the proposed method.The results show that the proposed two-scale optimization approach can generate better designs than the single-scale method.展开更多
Wind energy has been widely applied in power generation to alleviate climate problems.The wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that red...Wind energy has been widely applied in power generation to alleviate climate problems.The wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that reduces the power outputs of wind turbines located in downstream.Wind farm layout optimization(WFLO)aims to reduce the wake effect for maximizing the power outputs of the wind farm.Nevertheless,the wake effect among wind turbines increases significantly as the number of wind turbines increases in the wind farm,which severely affect power conversion efficiency.Conventional heuristic algorithms suffer from issues of low solution quality and local optimum for large-scale WFLO under complex wind scenarios.Thus,a chaotic local search-based genetic learning particle swarm optimizer(CGPSO)is proposed to optimize large-scale WFLO problems.CGPSO is tested on four larger-scale wind farms under four complex wind scenarios and compares with eight state-of-the-art algorithms.The experiment results indicate that CGPSO significantly outperforms its competitors in terms of performance,stability,and robustness.To be specific,a success and failure memories-based selection is proposed to choose a chaotic map for chaotic search local.It improves the solution quality.The parameter and search pattern of chaotic local search are also analyzed for WFLO problems.展开更多
The plate-shell structures with stiffeners are widely used in a broad range of engineering structures. This study presents the layout optimization of stiffeners. The minimum weight of stiffeners is taken as the object...The plate-shell structures with stiffeners are widely used in a broad range of engineering structures. This study presents the layout optimization of stiffeners. The minimum weight of stiffeners is taken as the objective function with the global stiffness constraint. In the layout optimization, the stiffeners should be placed at the locations with high strain energy/or stress. Conversely, elements of stiffeners with a small strain energy/or stress are considered to be used inefficiently and can be removed. Thus, to identify the element efficiency so that most inefficiently used elements of stiffeners can be removed, the element sensitivity of the strain energy of stiffeners is introduced, and a search criterion for locations of stiffeners is presented. The layout optimization approach is given for determining which elements of the stiffeners need to be kept or removed. In each iterative design, a high efficiency reanalysis approach is used to reduce the computational effort. The present approach is implemented for the layout optimization of stiffeners for a bunker loaded by the hydrostatic pressure. The numerical results show that the present approach is effective for dealing with layout optimization of stiffeners for plate-shell structures.展开更多
Urban park is one of the few green spaces that retain natural traces and are easy for the elderly to reach,and it has become the main place for the elderly to carry out leisure,entertainment,physical fi tness and inte...Urban park is one of the few green spaces that retain natural traces and are easy for the elderly to reach,and it has become the main place for the elderly to carry out leisure,entertainment,physical fi tness and interpersonal activities.The reasonable layout of urban parks is of great signifi cance to improve the quality of life of the elderly in old age.In this article,using the network analysis method in ArcGIS software,the accessibility of urban parks in Xihu District of Nanchang City based on elderly walking was evaluated and analyzed,and three strategies for optimizing the layout of the urban parks were proposed,including increasing the number of urban parks,improving the urban traffic network,and strengthening urban greenway planning and construction.The research results can provide reference for the optimal layout of urban parks in Xihu District,Nanchang City,so that the urban parks can better meet the outdoor leisure needs of the elderly,and the elderly’s sense of well-being and gain is enhanced.展开更多
To improve the global search ability of particle swarm optimization （PSO）, a multi-population PSO based on pyramid model （PPSO） is presented. Then, it is applied to solve the layout optimization problems against t...To improve the global search ability of particle swarm optimization （PSO）, a multi-population PSO based on pyramid model （PPSO） is presented. Then, it is applied to solve the layout optimization problems against the background of an international commercial communication satellite （INTELSAT-Ⅲ） module. Three improvements are developed, including multi-population search based on pyramid model, adaptive collision avoidance among particles, and mutation of degraded particles. In the numerical examples of the layout design of this simplified satellite module, the performance of PPSO is compared to global version PSO and local version PSO （ring and Neumann PSO）. The results show that PPSO has higher computational accuracy, efficiency and success ratio.展开更多
基金supported in part by the General Program of the National Natural Science Foundation of China(No.52175467)the National Key R&D Program of China(No.2022YFB3402600).
文摘Obtaining residual stress is crucial for controlling the machining deformation in annular parts,and can directly influence the performance and stability of key components in advanced equipment.Since existing research has achieved global residual stress field inference for components by using the deformation force-based method where the deformation force is monitored during the machining process,reliable acquisition of deformation force stll remains a significant challenge under complex machining conditions.This paper proposes a hierarchical optimization method for the layout of deformation force monitoring of annular parts.The proposed method establishes two optimization objectives by analyzing the relationship between the deformation force and the residual stress in annular parts,i.e.,equivalence and ilconditioning of solving process.Specifically,the equivalence of the monitored deformation force and residual stress in terms of effect on caused machining deformation is evaluated by local deformation,and the illconditioning is also optimized to enhance the stability of residual stress inference.Verification is implemented in both simulation and actual machining experiments,demonstrating effectiveness of the proposed layout optimization method in inferring residual stress field of annular parts with deformation force.
基金supported by the National Natural Science Foundation of China(No.92371206)the Postgraduate Scientific Research Innovation Project of Hunan Province,China(No.CX2023063).
文摘Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.
基金supported by the Science and Technology Project of China South Power Grid Co.,Ltd.,Grant Nos.036000KK52222044,GDKJXM20222430。
文摘The rapid expansion of offshore wind energy necessitates robust and cost-effective electrical collector system(ECS)designs that prioritize lifetime operational reliability.Traditional optimization approaches often simplify reliability considerations or fail to holistically integrate them with economic and technical constraints.This paper introduces a novel,two-stage optimization framework for offshore wind farm(OWF)ECS planning that systematically incorporates reliability.The first stage employs Mixed-Integer Linear Programming(MILP)to determine an optimal radial network topology,considering linearized reliability approximations and geographical constraints.The second stage enhances this design by strategically placing tie-lines using a Mixed-Integer Quadratically Constrained Program(MIQCP).This stage leverages a dynamic-aware adaptation of Multi-Source Multi-Terminal Network Reliability(MSMT-NR)assessment,with its inherent nonlinear equations successfully transformed into a solvable MIQCP form for loopy networks.A benchmark case study demonstrates the framework’s efficacy,illustrating how increasing the emphasis on reliability leads to more distributed and interconnected network topologies,effectively balancing investment costs against enhanced system resilience.
基金Supported by National Natural Science Foundation of China(Grant No.52005371)Shanghai Municipal Natural Science Foundation of China(Grant No.22ZR1463900)+1 种基金Fundamental Research Funds for the Central Universities of China(Grant No.22120220649)State Key Laboratory of Mechanical System and Vibration of China(Grant No.MSV202318).
文摘An increasing number of researchers have researched fixture layout optimization for thin-walled part assembly during the past decades.However,few papers systematically review these researches.By analyzing existing literature,this paper summarizes the process of fixture layout optimization and the methods applied.The process of optimization is made up of optimization objective setting,assembly variation/deformation modeling,and fixture layout optimization.This paper makes a review of the fixture layout for thin-walled parts according to these three steps.First,two different kinds of optimization objectives are introduced.Researchers usually consider in-plane variations or out-of-plane deformations when designing objectives.Then,modeling methods for assembly variation and deformation are divided into two categories:Mechanism-based and data-based methods.Several common methods are discussed respectively.After that,optimization algorithms are reviewed systematically.There are two kinds of optimization algorithms:Traditional nonlinear programming and heuristic algorithms.Finally,discussions on the current situation are provided.The research direction of fixture layout optimization in the future is discussed from three aspects:Objective setting,improving modeling accuracy and optimization algorithms.Also,a new research point for fixture layout optimization is discussed.This paper systematically reviews the research on fixture layout optimization for thin-walled parts,and provides a reference for future research in this field.
基金the Foundation of State Key Laboratory of Structural Analysis for Industrial Equipment(No.S18315)。
文摘Ship pipe layout optimization is one of the difficulties and hot spots in ship intelligent production design.A high-dimensional vector coding is proposed based on the research of related pipe coding and ship pipe route features in this paper.The advantages of this coding method are concise structure,strong compatibility,and independence from the gridding space.Based on the proposed coding,the particle swarm optimization algorithm is implemented,and the algorithm is improved by the pre-selected path strategy and the branch-pipe processing strategy.Finally,two simulation results reveal that the proposed coding and algorithm have feasibility and engineering practicability.
基金funding provided by the National Natural Science Foundation of China,China(Grant No.52104065,52074090)the Heilongjiang Provincial Natural Science Foundation of China,China(Grant No.LH2021E019)+3 种基金the China Postdoctoral Science Foundation,China(Grant Nos.2022T150089 and 2020M681064)the Heilongjiang Postdoctoral Foundation,China(Grant No.LBH-Z20101)the Scientific Research Personnel Training Foundation of Northeast Petroleum University,China(Grant No.XNYXLY202103)Northeast Petroleum University Scientific Research Foundation,China(Grant No.2019KQ54).
文摘The layout optimization design of a natural gas gathering pipeline network is a multi-objective optimization problem because the extant theories are unable to meet the different decision preferences in scheme design,which restricts the intelligentization of gas gathering pipeline layout optimization.Currently,there are no generic design studies on the loop-star pipeline network.Therefore,this paper proposes a generic layout optimization model containing a large number of discrete and continuous variables,such as pipe connection relationships,pipe sizes,pipe length,and pipe specifications.In the solution section,drawing inspiration from the hormone regulation mechanism and local foraging rule in bionics,an improved particle swarm optimization algorithm based on hormone regulation(HRPSO)is proposed,and it obtains the favorable parameters range of the HRPSO algorithm.The results illustrate that the HRPSO algorithm exhibits convergence to the global optimum with a probability of 1.In comparison to manual design,the comprehensive costs of the optimized scheme are saved by 22.71%with the HRPSO algorithm.Compared to the four PSO variants in the paper,it can save costs by 5.38%,4.95%,4.09%,and 3.65%,respectively.
基金supported by the Japan Society for the Promotion of Science(JSPS)KAKENHI under Grant JP22H03643,Japan Science and Technology Agency(JST)Support for Pioneering Research Initiated by the Next Generation(SPRING)under Grant JPMJSP2145JST through the Establishment of University Fellowships towards the Creation of Science Technology Innovation under Grant JPMJFS2115.
文摘Energy issues have always been one of the most significant concerns for scientists worldwide.With the ongoing over exploitation and continued outbreaks of wars,traditional energy sources face the threat of depletion.Wind energy is a readily available and sustainable energy source.Wind farm layout optimization problem,through scientifically arranging wind turbines,significantly enhances the efficiency of harnessing wind energy.Meta-heuristic algorithms have been widely employed in wind farm layout optimization.This paper introduces an Adaptive strategy-incorporated Integer Genetic Algorithm,referred to as AIGA,for optimizing wind farm layout problems.The adaptive strategy dynamically adjusts the placement of wind turbines,leading to a substantial improvement in energy utilization efficiency within the wind farm.In this study,AIGA is tested in four different wind conditions,alongside four other classical algorithms,to assess their energy conversion efficiency within the wind farm.Experimental results demonstrate a notable advantage of AIGA.
文摘With the growing need for renewable energy,wind farms are playing an important role in generating clean power from wind resources.The best wind turbine architecture in a wind farm has a major influence on the energy extraction efficiency.This paper describes a unique strategy for optimizing wind turbine locations on a wind farm that combines the capabilities of particle swarm optimization(PSO)and artificial neural networks(ANNs).The PSO method was used to explore the solution space and develop preliminary turbine layouts,and the ANN model was used to fine-tune the placements based on the predicted energy generation.The proposed hybrid technique seeks to increase energy output while considering site-specific wind patterns and topographical limits.The efficacy and superiority of the hybrid PSO-ANN methodology are proved through comprehensive simulations and comparisons with existing approaches,giving exciting prospects for developing more efficient and sustainable wind farms.The integration of ANNs and PSO in our methodology is of paramount importance because it leverages the complementary strengths of both techniques.Furthermore,this novel methodology harnesses historical data through ANNs to identify optimal turbine positions that align with the wind speed and direction and enhance energy extraction efficiency.A notable increase in power generation is observed across various scenarios.The percentage increase in the power generation ranged from approximately 7.7%to 11.1%.Owing to its versatility and adaptability to site-specific conditions,the hybrid model offers promising prospects for advancing the field of wind farm layout optimization and contributing to a greener and more sustainable energy future.
基金supported by the National Natural Science Foundation of China(Grant Nos.12172075 and 12332007)the Fundamental Research Funds for the Central Universities(Grant No.DUT23RC(5)004).
文摘Kirigami metamaterials have gained increasing attention due to their unusual mechanical properties under largestretching.However,most metamaterial designs obtained with trial-and-error approaches tend to lose their desirable properties under large tensile strains due to occurrence of instability caused by out-of-plane buckling.To cope with this limitation,this paper presents a systematic approach of cut layout optimizing for designingkirigami metamaterials working at large tensile strains by fully exploiting their out-of-plane buckling behaviors.This method can also mitigate the local stress concentration issue at the hinges of conventional kirigami designsworking at in-plane deformation modes.The effectiveness of the proposed method is demonstrated through several examples regarding metamaterial design with negative Poisson’s ratio and specified flip angle pattern.It isshown that the proposed method is capable of addressing the highly nonlinear deformation impacts on the mechanical performance under large stretching,to meet the growing and diverse demands in the field of kirigamimetamaterials.
文摘Over the past decade, Graphics Processing Units (GPUs) have revolutionized high-performance computing, playing pivotal roles in advancing fields like IoT, autonomous vehicles, and exascale computing. Despite these advancements, efficiently programming GPUs remains a daunting challenge, often relying on trial-and-error optimization methods. This paper introduces an optimization technique for CUDA programs through a novel Data Layout strategy, aimed at restructuring memory data arrangement to significantly enhance data access locality. Focusing on the dynamic programming algorithm for chained matrix multiplication—a critical operation across various domains including artificial intelligence (AI), high-performance computing (HPC), and the Internet of Things (IoT)—this technique facilitates more localized access. We specifically illustrate the importance of efficient matrix multiplication in these areas, underscoring the technique’s broader applicability and its potential to address some of the most pressing computational challenges in GPU-accelerated applications. Our findings reveal a remarkable reduction in memory consumption and a substantial 50% decrease in execution time for CUDA programs utilizing this technique, thereby setting a new benchmark for optimization in GPU computing.
基金Supported by the National High Technology Research and Development Program of China (863 Program) under Grant No.2006AA09A104
文摘The general layout of 6th generation semi-submersible drilling platforms is the main factor impacting the efficiency of their drilling operations. This paper provides a compound/integrated algorithm based on process flow that is aimed at improving efficiency, while giving attention to stability and safety at the same time. The paper describes the process flow of dual drilling centers and a hierarchical division of rigs based on the different modes of transportation of various drilling support systems. The general layout-centripetal overall arrangement spatially was determined based on drilling efficiency. We derived our modules according to drilling functionality; the modules became our basic layout units. We applied different layout algorithm to mark out the upper and lower decks. That is, the upper deck was designed based on the lowest transportation cost while the lower deck＇s calculations were based on the best-fit scope. Storage configurations in columns and pontoons were also considered for the layout design. Finally the center of gravity was taken into consideration and the general layout was adjusted accordingly, to result in an optimal center of gravity. The methodology of the general layout can provide a reference for implementation of domestic designs of semi-submersible rigs.
基金the project PID2022-139202OB-I00Neural Networks and Optimization Techniques for the Design and Safe Maintenance of Transportation Infrastructures:Volcanic Rock Geotechnics and Slope Stability(IA-Pyroslope),funded by the Spanish State Research Agency of the Ministry of Science,Innovation and Universities of Spain and the European Regional Development Fund,MCIN/AEI/10.13039/501100011033/FEDER,EU。
文摘Volcanic terrains exhibit a complex structure of pyroclastic deposits interspersed with sedimentary processes,resulting in irregular lithological sequences that lack lateral continuity and distinct stratigraphic patterns.This complexity poses significant challenges for slope stability analysis,requiring the development of specialized techniques to address these issues.This research presents a numerical methodology that incorporates spatial variability,nonlinear material characterization,and probabilistic analysis using a Monte Carlo framework to address this issue.The heterogeneous structure is represented by randomly assigning different lithotypes across the slope,while maintaining predefined global proportions.This contrasts with the more common approach of applying probabilistic variability to mechanical parameters within a homogeneous slope model.The material behavior is defined using complex nonlinear failure criteria,such as the Hoek-Brown model and a parabolic model with collapse,both implemented through linearization techniques.The Discontinuity Layout Optimization(DLO)method,a novel numerical approach based on limit analysis,is employed to efficiently incorporate these advances and compute the factor of safety of the slope.Within this framework,the Monte Carlo procedure is used to assess slope stability by conducting a large number of simulations,each with a different lithotype distribution.Based on the results,a hybrid method is proposed that combines probabilistic modeling with deterministic design principles for the slope stability assessment.As a case study,the methodology is applied to a 20-m-high vertical slope composed of three lithotypes(altered scoria,welded scoria,and basalt)randomly distributed in proportions of 15%,60%,and 25%,respectively.The results show convergence of mean values after approximately 400 simulations and highlight the significant influence of spatial heterogeneity,with variations of the factor of safety between 5 and 12 in 85%of cases.They also reveal non-circular and mid-slope failure wedges not captured by traditional stability methods.Finally,an equivalent normal probability distribution is proposed as a reliable approximation of the factor of safety for use in risk analysis and engineering decision-making.
文摘This research systematically investigates urban three-dimensional greening layout optimization and smart ecocity construction using deep learning and remote sensing technology.An improved U-Net++ architecture combined with multi-source remote sensing data achieved high-precision recognition of urban three-dimensional greening with 92.8% overall accuracy.Analysis of spatiotemporal evolution patterns in Shanghai,Hangzhou,and Nanjing revealed that threedimensional greening shows a development trend from demonstration to popularization,with 16.5% annual growth rate.The study quantitatively assessed ecological benefits of various three-dimensional greening types.Results indicate that modular vertical greening and intensive roof gardens yield highest ecological benefits,while climbing-type vertical greening and extensive roof gardens offer optimal benefit-cost ratios.Integration of multiple forms generates 15-22% synergistic enhancement.Compared with traditional planning,the multi-objective optimization-based layout achieved 27.5% increase in carbon sequestration,32.6% improvement in temperature regulation,35.8% enhancement in stormwater management,and 42.3% rise in biodiversity index.Three pilot projects validated that actual ecological benefits reached 90.3-102.3% of predicted values.Multi-scenario simulations indicate optimized layouts can reduce urban heat island intensity by 15.2-18.7%,increase carbon neutrality contribution to 8.6-10.2%,and decrease stormwater runoff peaks by 25.3-32.6%.The findings provide technical methods for urban three-dimensional greening optimization and smart eco-city construction,promoting sustainable urban development.
基金Natural Science Foudation of Shanxi Province of China(No.2013011017-8)
文摘The paper proposes four indicators to guide sensors layout in practical experiment on explosion overpressure filed construction based on tomographic method with high reconstruction accuracy and the least sensors. First, genetic algorithm is adopted to conduct global search and sensor layout optimization method is selected to satisfy four indicators. Then, by means of Matlab, the variation of these four indicators with different sensor layouts and reconstruction accuracy are analyzed and discussed. The results indicate that the sensor layout method proposed by this paper can reconstruct explosion overpressure field at the highest precision by a minimum number of sensors. It will guide actual explosion experiments in a cost-effective way.
基金National Natural Science Foundation of China （10872091）
文摘A two-level layout optimization strategy is proposed in this paper for large-scale composite wing structures. Design requirements are adjusted at the system level according to structural deformation, while the layout is optimized at the subsystem level to satisfy the constraints from system level. The approaching degrees of various failure critical loads in wing panels are employed to gauge the structure’s carrying efficiency. By optimizing the efficiency as an objective, the continuity of the problem could be guaranteed. Stiffened wing panels are modeled by the equivalent orthotropic plates, and the global buckling load is predicted by energy method. The nonlinear effect of stringers’ support elasticity on skin local buckle resistance is investigated and approximated by neural network （NN） surrogate model. These failure predictions are based on analytical solutions, which could effectively save calculation resources. Finally, the integral optimization of a large-scale wing structure is completed as an example. The result fulfills design requirements and shows the feasibility of this method.
基金The authors gratefully acknowledge the financial support to this work from the National Natural Science Foundation of China(Grants 11802164 and U1808215)Shandong Provincial Natural Science Foundation(Grant ZR2019BEE005)the project funded by China Postdoctoral Science Foundation.
文摘Stiffened plates or shells are widely used in engineering structures as primary or secondary load-bearing components.How to design the layout and sizes of the stiffeners is of great significance for structural lightweight.In this work,a new topology optimization method for simultaneously optimizing the layout and cross-section topology of the stiffeners is developed to solve this issue.The stilfeners and base plates are modeled by the beam and shell elements,respectively,significantly reducing the computational cost.The Giavotto beam theory,instead of the widely employed Euler or Timoshenko beam theory,is applied to model the stiffeners for considering the warping deformation in evaluating the section stiffness of the beam.A multi-scale topology optimization model is established by simultaneously optimizing the layout of the beam and the topology of the cross-section.The design space is significantly expanded by optimizing these two types of design variables.Several numerical examples are applied to illustrate the validity and effectiveness of the proposed method.The results show that the proposed two-scale optimization approach can generate better designs than the single-scale method.
基金partially supported by the Japan Society for the Promotion of Science(JSPS)KAKENHI(JP22H03643)Japan Science and Technology Agency(JST)Support for Pioneering Research Initiated by the Next Generation(SPRING)(JPMJSP2145)JST through the Establishment of University Fellowships towards the Creation of Science Technology Innovation(JPMJFS2115)。
文摘Wind energy has been widely applied in power generation to alleviate climate problems.The wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that reduces the power outputs of wind turbines located in downstream.Wind farm layout optimization(WFLO)aims to reduce the wake effect for maximizing the power outputs of the wind farm.Nevertheless,the wake effect among wind turbines increases significantly as the number of wind turbines increases in the wind farm,which severely affect power conversion efficiency.Conventional heuristic algorithms suffer from issues of low solution quality and local optimum for large-scale WFLO under complex wind scenarios.Thus,a chaotic local search-based genetic learning particle swarm optimizer(CGPSO)is proposed to optimize large-scale WFLO problems.CGPSO is tested on four larger-scale wind farms under four complex wind scenarios and compares with eight state-of-the-art algorithms.The experiment results indicate that CGPSO significantly outperforms its competitors in terms of performance,stability,and robustness.To be specific,a success and failure memories-based selection is proposed to choose a chaotic map for chaotic search local.It improves the solution quality.The parameter and search pattern of chaotic local search are also analyzed for WFLO problems.
基金Project supported by the Foundation of University's Doctorial Subjects of China (No.20010183013)985-Automotive Engineering of Jilin University.
文摘The plate-shell structures with stiffeners are widely used in a broad range of engineering structures. This study presents the layout optimization of stiffeners. The minimum weight of stiffeners is taken as the objective function with the global stiffness constraint. In the layout optimization, the stiffeners should be placed at the locations with high strain energy/or stress. Conversely, elements of stiffeners with a small strain energy/or stress are considered to be used inefficiently and can be removed. Thus, to identify the element efficiency so that most inefficiently used elements of stiffeners can be removed, the element sensitivity of the strain energy of stiffeners is introduced, and a search criterion for locations of stiffeners is presented. The layout optimization approach is given for determining which elements of the stiffeners need to be kept or removed. In each iterative design, a high efficiency reanalysis approach is used to reduce the computational effort. The present approach is implemented for the layout optimization of stiffeners for a bunker loaded by the hydrostatic pressure. The numerical results show that the present approach is effective for dealing with layout optimization of stiffeners for plate-shell structures.
基金General Project of Humanities and Social Science Research of Ministry of Education,China(16YJC760073)Social Science Planning Project of Jiangxi Province(18YS07)+1 种基金Humanities and Social Science Research Project for Colleges and Universities in Jiangxi Province(YS1531)Innovation and Entrepreneurship Training Program for Undergraduates in Jiangxi Agricultural University(S202010410015).
文摘Urban park is one of the few green spaces that retain natural traces and are easy for the elderly to reach,and it has become the main place for the elderly to carry out leisure,entertainment,physical fi tness and interpersonal activities.The reasonable layout of urban parks is of great signifi cance to improve the quality of life of the elderly in old age.In this article,using the network analysis method in ArcGIS software,the accessibility of urban parks in Xihu District of Nanchang City based on elderly walking was evaluated and analyzed,and three strategies for optimizing the layout of the urban parks were proposed,including increasing the number of urban parks,improving the urban traffic network,and strengthening urban greenway planning and construction.The research results can provide reference for the optimal layout of urban parks in Xihu District,Nanchang City,so that the urban parks can better meet the outdoor leisure needs of the elderly,and the elderly’s sense of well-being and gain is enhanced.
基金This project is supported by National Natural Science Foundation of China (No.50275019, No.50335040, No.50575031).
文摘To improve the global search ability of particle swarm optimization （PSO）, a multi-population PSO based on pyramid model （PPSO） is presented. Then, it is applied to solve the layout optimization problems against the background of an international commercial communication satellite （INTELSAT-Ⅲ） module. Three improvements are developed, including multi-population search based on pyramid model, adaptive collision avoidance among particles, and mutation of degraded particles. In the numerical examples of the layout design of this simplified satellite module, the performance of PPSO is compared to global version PSO and local version PSO （ring and Neumann PSO）. The results show that PPSO has higher computational accuracy, efficiency and success ratio.
