Optimization Design of the Multi-Layer Cross-Sectional Layout of An Umbilical Based on the GA-GLM

Marine umbilical is one of the key equipment for subsea oil and gas exploitation,which is usually integrated by a great number of different functional components with multi-layers.The layout of these components directly affects manufacturing,operation and storage performances of the umbilical.For the multi-layer cross-sectional layout design of the umbilical,a quantifiable multi-objective optimization model is established according to the operation and storage requirements.Considering the manufacturing factors,the multi-layering strategy based on contact point identification is introduced for a great number of functional components.Then,the GA-GLM global optimization algorithm is proposed combining the genetic algorithm and the generalized multiplier method,and the selection operator of the genetic algorithm is improved based on the steepest descent method.Genetic algorithm is used to find the optimal solution in the global space,which can converge from any initial layout to the feasible layout solution.The feasible layout solution is taken as the initial value of the generalized multiplier method for fast and accurate solution.Finally,taking umbilicals with a great number of components as examples,the results show that the cross-sectional performance of the umbilical obtained by optimization algorithm is better and the solution efficiency is higher.Meanwhile,the multi-layering strategy is effective and feasible.The design method proposed in this paper can quickly obtain the optimal multi-layer cross-sectional layout,which replaces the manual design,and provides useful reference and guidance for the umbilical industry.

Development of Fixture Layout Optimization for Thin-Walled Parts:A Review

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.

Web Layout Design of Large Cavity Structures Based on Topology Optimization

Large cavity structures are widely employed in aerospace engineering, such as thin-walled cylinders, blades andwings. Enhancing performance of aerial vehicles while reducing manufacturing costs and fuel consumptionhas become a focal point for contemporary researchers. Therefore, this paper aims to investigate the topologyoptimization of large cavity structures as a means to enhance their performance, safety, and efficiency. By usingthe variable density method, lightweight design is achieved without compromising structural strength. Theoptimization model considers both concentrated and distributed loads, and utilizes techniques like sensitivityfiltering and projection to obtain a robust optimized configuration. The mechanical properties are checked bycomparing the stress distribution and displacement of the unoptimized and optimized structures under the sameload. The results confirm that the optimized structures exhibit improved mechanical properties, thus offering keyinsights for engineering lightweight, high-strength large cavity structures.

Optimizing wind farm layout for enhanced electricity extraction using a new hybrid PSO-ANN method

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.

GNN Representation Learning and Multi-Objective Variable Neighborhood Search Algorithm for Wind Farm Layout Optimization

With the increasing demand for electrical services,wind farm layout optimization has been one of the biggest challenges that we have to deal with.Despite the promising performance of the heuristic algorithm on the route network design problem,the expressive capability and search performance of the algorithm on multi-objective problems remain unexplored.In this paper,the wind farm layout optimization problem is defined.Then,a multi-objective algorithm based on Graph Neural Network(GNN)and Variable Neighborhood Search(VNS)algorithm is proposed.GNN provides the basis representations for the following search algorithm so that the expressiveness and search accuracy of the algorithm can be improved.The multi-objective VNS algorithm is put forward by combining it with the multi-objective optimization algorithm to solve the problem with multiple objectives.The proposed algorithm is applied to the 18-node simulation example to evaluate the feasibility and practicality of the developed optimization strategy.The experiment on the simulation example shows that the proposed algorithm yields a reduction of 6.1% in Point of Common Coupling(PCC)over the current state-of-the-art algorithm,which means that the proposed algorithm designs a layout that improves the quality of the power supply by 6.1%at the same cost.The ablation experiments show that the proposed algorithm improves the power quality by more than 8.6% and 7.8% compared to both the original VNS algorithm and the multi-objective VNS algorithm.

Optimizing Memory Access Efficiency in CUDA Kernel via Data Layout Technique

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.

Applying Force-Directed Placer to Wafer Scale Placement

A placement algorithm for discretionary wiring WSI system, WFDP, is proposed in this paper. WFDP employes force-directed placement algorithm. First the relative locations of macro circuit component are determined. In the phase of determining the real location of the circuit component, it avoids the problem of determining the real location of arbitrary component by employing divide-conquer strategy and removing the hierarchical division. Its computation is much more simpler than other homologous algorithm. A simple model used to estimate the quality of placement is also proposed. Although the WFDP is aimed at WSI, it can also be used in the placement of other redundancy-oriented device or system such as RVLSI, etc.

Application of a Parallel Adaptive Cuckoo Search Algorithm in the Rectangle Layout Problem

The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution.It has good performance in global optimization fields such as maximization.In this paper,a new adaptive parameter strategy and a parallel communication strategy are proposed to further improve the Cuckoo Search(CS)algorithm.This strategy greatly improves the convergence speed and accuracy of the algorithm and strengthens the algorithm’s ability to jump out of the local optimal.This paper compares the optimization performance of Parallel Adaptive Cuckoo Search(PACS)with CS,Parallel Cuckoo Search(PCS),Particle Swarm Optimization(PSO),Sine Cosine Algorithm(SCA),Grey Wolf Optimizer(GWO),Whale Optimization Algorithm(WOA),Differential Evolution(DE)and Artificial Bee Colony(ABC)algorithms by using the CEC-2013 test function.The results show that PACS algorithmoutperforms other algorithms in 20 of 28 test functions.Due to the superior performance of PACS algorithm,this paper uses it to solve the problem of the rectangular layout.Experimental results show that this scheme has a significant effect,and the material utilization rate is improved from89.5%to 97.8%after optimization.

A Chaotic Local Search-Based Particle Swarm Optimizer for Large-Scale Complex Wind Farm Layout Optimization

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.

Semantic Document Layout Analysis of Handwritten Manuscripts

A document layout can be more informative than merely a document’s visual and structural appearance.Thus,document layout analysis(DLA)is considered a necessary prerequisite for advanced processing and detailed document image analysis to be further used in several applications and different objectives.This research extends the traditional approaches of DLA and introduces the concept of semantic document layout analysis(SDLA)by proposing a novel framework for semantic layout analysis and characterization of handwritten manuscripts.The proposed SDLA approach enables the derivation of implicit information and semantic characteristics,which can be effectively utilized in dozens of practical applications for various purposes,in a way bridging the semantic gap and providingmore understandable high-level document image analysis and more invariant characterization via absolute and relative labeling.This approach is validated and evaluated on a large dataset ofArabic handwrittenmanuscripts comprising complex layouts.The experimental work shows promising results in terms of accurate and effective semantic characteristic-based clustering and retrieval of handwritten manuscripts.It also indicates the expected efficacy of using the capabilities of the proposed approach in automating and facilitating many functional,reallife tasks such as effort estimation and pricing of transcription or typing of such complex manuscripts.

A Spacecra Equipment Layout Optimization Method for Diverse and Competitive Design

The spacecraequipment layout optimization design(SELOD)problems with complicated performance con-straints and diversity are studied in this paper.The previous literature uses the gradient-based algorithm to obtain optimized non-overlap layout schemes from randomly initialized cases eectively.However,these local optimal solutions are too dicult to jump out of their current relative geometry relationships,signi􀀀cantly limiting their further improvement in performance indicators.Therefore,considering the geometric diversity of layout schemes is put forward to alleviate this limitation.First,similarity measures,including modi􀀀ed cosine similarity and gaussian kernel function similarity,are introduced into the layout optimization process.Then the optimization produces a set of feasible layout candidates with the most remarkable dierence in geometric distribution and the most representative schemes are sampled.Finally,these feasible geometric solutions are used as initial solutions to optimize the physical performance indicators of the spacecra,and diversi􀀀ed layout schemes of spacecraequipment are generated for the engineering practice.The validity and eectiveness of the proposed methodology are demonstrated by two SELOD applications.

Advanced surface coil layout with intrinsic noise cancellation properties for surface-NMR applications

A recent study demonstrated that in small-scale prepolarized surface nuclear magnetic resonance(SNMR-PP)measurements with a footprint of a few square meters,customized PP switch-off ramps can serve as an efficient excitation mechanism,eliminating the requirement for a conventional oscillating excitation pulse.This approach enables the detection of short relaxation signals from the unsaturated soil zone and can,therefore,be used to directly provide soil moisture and pore geometry information.Because ultimately such small-scale SNMR-PP setups are intended for a mobile application,it is necessary to develop strategies that allow for speedy measurement progress and do not require noise cancellation protocols based on reference stations.Hence,we developed a new concentric figure-of-eight(cFOE)loop layout that combines the direction independence of a circular loop with the intrinsic noise cancellation properties of a classical FOE-loop.This approach significantly decreases the measurement time because suitable signal-to-noise ratios are reached much faster compared to a classical circular loop and will bring us one step further toward fast and non-invasive soil moisture mapping applications.

The Evolution of Landscape Layout Concept of Lingnan Taoist Zuting Temples in the Qing Dynasty

The replacement of Lingnan Taoist Zuting Temples from Lingbao Sect to Quanzhen Sect in the Qing Dynasty has led to the change of the layout of Taoist temples on Luofu Mountain. Through literature review, field research, image comparison and other research methods, the paper explores how the layout of the Taoist temples on Luofu Mountain evolved from the Four Taoist Temples in the Gehong period to the Five Taoist Temples in the Qing Dynasty under the influence of different Taoist ideologies, aiming to explore the cultural implications of the evolution of the layout of Lingnan Taoism Zuting Temple in the Qing Dynasty.

HSCA-Net: A Hybrid Spatial-Channel Attention Network in Multiscale Feature Pyramid for Document Layout Analysis

Document images often contain various page components and complex logical structures,which make document layout analysis task challenging.For most deep learning-based document layout analysis methods,convolutional neural networks(CNNs)are adopted as the feature extraction networks.In this paper,a hybrid spatial-channel attention network(HSCA-Net)is proposed to improve feature extraction capability by introducing attention mechanism to explore more salient properties within document pages.The HSCA-Net consists of spatial attention module(SAM),channel attention module(CAM),and designed lateral attention connection.CAM adaptively adjusts channel feature responses by emphasizing selective information,which depends on the contribution of the features of each channel.SAM guides CNNs to focus on the informative contents and capture global context information among page objects.The lateral attention connection incorporates SAM and CAM into multiscale feature pyramid network,and thus retains original feature information.The effectiveness and adaptability of HSCA-Net are evaluated through multiple experiments on publicly available datasets such as PubLayNet,ICDAR-POD,and Article Regions.Experimental results demonstrate that HSCA-Net achieves state-of-the-art performance on document layout analysis task.

河道斜交桥梁壅水及流场特性分析

圆端桥墩是平原河道内一种常见的桥墩型式,为探究斜交圆端桥墩不同布置方式引起的壅水特性和水流流态变化,建立三维数值模型,采用SIMPLE算法耦合k-ε紊流模型求解N-S方程组,应用VOF法追踪自由液面,分析了单个桥墩壅水特性、绕流流场和升阻力系数随斜交角度变化的规律。研究结果表明:下游墩前壅水高度最大,壅水长度最长,上游墩前冲高值最大;水流绕流经过上游桥墩后偏向下游桥墩侧,桥墩垂直桥轴线布置方式绕流形成的壅水叠加效应更明显;斜交桥梁布置采取圆端桥墩时,桥墩顺水流布置,可有效减小阻水宽度与壅水高度,弱化流场结构变化。

基于人水和谐视角的国家水网优化布局构想与展望

国家水网建设有助于缓解当前日益复杂的人水矛盾,基于人水和谐论开展国家水网优化布局研究,能够为国家水网建设提供新的思路。本文从人水和谐的视角出发,通过梳理国家水网提出过程,分析现阶段国家水网建设中亟待解决的问题,在此基础上辨析人水和谐论对国家水网优化布局的指导作用;进一步以人水和谐理念为引领,从指导理论、基本原则、布局方法、关键内容与研究途径等方面提出了国家水网优化布局的构建框架;从促进人与自然和谐共生、实现综合效益最优发挥、支撑国家安全总体保障、助力先进文化繁荣发展、建设现代水网智慧服务5个方面描绘了国家水网优化布局的蓝图;从理论、方法、技术、支撑体系4个方面对国家水网优化布局未来研究进行展望。

我国城市幼儿园空间布局适配性的现状、成因与优化建议

供需适配的幼儿园空间布局是促进学前教育资源均衡配置的关键举措,能有效推动学前教育高质量发展。本研究以我国中部地区某省会城市主城区759所幼儿园为例,运用地理信息分析工具中的核密度估计、最近邻指数等方法,分别从“有园上”“入园近”“上好园”“园幼共生”四个维度对幼儿园空间布局是否存在错配的核心议题进行深入剖析。研究发现:该市部分幼儿园空间分布存在欠适配现象,且形成了空间与结构错配的问题,其主要原因是资源投入层面的财政失衡、需求提出层面的逐优心理和城市规划层面的建设滞后。本文建议通过构建数字化管理平台、优化供需对接机制、加强配套园治理、加大治薄扶弱补缺的力度等措施,推动幼儿园空间布局从错配到适配的转换,从而促进区域学前教育公共服务一体化和高质量发展。

如何优化乡村小规模学校布局

乡村小规模学校布局优化是城乡教育一体化发展的重点和难点,更是确保乡村儿童接受公平优质教育的重要基础。但现实中,小规模学校布局仍存在诸多问题:布局分散程度高;决策中缺少科学的程序和技术;布局规划中学校教育资源不足、弱势学生入学机会不均等;校际空间隔离,跨校资源共享难以稳定持续等。因此,应进一步明确小规模学校布局优化的逻辑理路:以教育规划理论引领布局优化的教育功能发挥;以“公平、效率、质量”框架重构布局规划目标;以“集中+分散”布局结构为乡村振兴奠定教育服务基础;以“入学可达性提升”促进乡村儿童入学机会均等;以资源均衡配置促进城乡学校一体化发展。同时,围绕“空间可达、服务范围、学校规模、校际发展优质均衡程度、能否促进学校传承乡土文化”等维度因地制宜设置学校布局标准,并提供支持性策略,主要包括:综合区位因素,构建小规模学校布局与乡村振兴规划融合系统;以参与式GIS技术为基础,优化学校布局规划程序;强化各级政府责任,妥善处理好布局规划中的伴生问题;共享教育资源,促进小规模学校与县镇学校一体化发展。

国家战略腹地建设的内涵特征、重大意义和推进策略

面对复杂的国际政治环境和全球产业竞争,立足我国新发展阶段的新特征,我国亟需尽快优化调整重大生产力布局,在广大中西部内陆腹地拓展战略纵深,夯实国家安全基石。2023年12月中央经济工作会议首次提出“建设国家战略腹地”的战略构想,为优化重大生产力布局,保障我国国家安全创造了条件。“国家战略腹地”是相对于前沿地区而言,能为前沿地区提供强大支撑的战略后方,主要是指围绕我国东部沿海起着稳定后方并服务国家战略安全的中西部内陆城市群或相应地区。“国家战略腹地”概念首次在国家层面提出,是党中央从国家发展大局去考量和谋划的,相比其他重大区域战略,国家战略腹地具备地理区位的枢纽性、经济发展的强大韧性、创新发展的引擎性、城市文化的凝聚性等基本特征。从我国区域经济发展史看,“三线建设”“区域协调发展战略”在不同历史时期承担了优化重大生产力布局、维护“大后方”的使命任务,国家战略后方作为新时代“大后方”概念,在新时代语境下自然也被寄予历史重任。进言之,加快推进国家战略腹地建设是当前应对百年大变局的应有之义、构建新发展格局的乘势之举和优化重大生产力布局的破题之策,唯有以自身的确定性应对外部的不确定性,才能把握战略主动。为把战略决策和预期目标变为现实结果,国家战略腹地建设必须系统推进。通过完善战略规划顶层设计、统筹战略物资储备建设、强化战略科技力量支撑、打造战略产业备份基地和建设战略运力空间网络,使之成为我国经济发展的新的增长极和动力源,不断推进新时代大后方高质量发展和高水平安全建设。

基于组合圆锥构型的水下声学定位系统布站优化

在水下目标定位中,布站构型的共面约束会导致目标单点定位的几何精度因子(GDOP)理论最小值不能达到。为改善水下布站的观测矩阵,将单圆锥扩展为组合圆锥构型布站模式。然而,传统的优化算法易陷入局部最优且存在收敛效率低等问题。针对以上问题,提出了一种改进的自适应粒子群算法。首先提出了一种有共面约束的水下声学定位系统组合圆锥构型,并证明了所提出的组合圆锥构型具有可叠加性、可旋转性以及GDOP旋转不变性;其次,针对布站优化指标单一的问题,构建了一种基于水下目标全局GDOP的布站优化准则;在此基础上提出了一种基于自适应粒子群的优化算法,并应用于组合圆锥构型下的水下声学定位系统布站优化。所提出的自适应粒子群算法能增强布站的全局最优性,提高算法收敛效率和水下目标的定位精度。