检索结果-内蒙古大学图书馆

multi-objective optimization of reinforced concrete columns considering long-term and second order effects: Constructability, cost, and environmental impact

引用

STRUCTURAL CONCRETE 2025年第0期

作者： Preuss, Arthur de C. Grotti, Ewerton de Almeida, Felipe Schaedler Gomes, Herbert M. Univ Fed Rio Grande do Sul Grad Program Civil Engn Ave Osvaldo Aranha 99 Porto Alegre Brazil Fed Univ Rio Grandedo Sul Grad Program Mech Engn Porto Alegre Brazil Fed Univ Fed Rio Grande do Sul Dept Civil Engn Porto Alegre Brazil

This work presents a methodology for the optimum design of reinforced concrete (RC) columns subjected to axial compression and bending end loads, considering long-term and second-order effects, designed according to the general method outlined in Eurocode 2 (EN 1992-1-1, 2004). The approach is applicable to columns of any slenderness, with arbitrary polygonal cross-section shape and subjected to any combination of axial compression and bending end loads. The multi-objective problem is formulated for the simultaneous improvement of cost, constructability, and environmental impact. The solution is obtained by the metaheuristic algorithms multi-objective quantum particle swarm optimization (MOQPSO), multi-objective particle swarm optimization (MOPSO) and the well-established non-dominated sorting genetic algorithm II (NSGA-II). Examples of the optimization of RC columns with rectangular cross-section and subjected to many load cases showed that cost and environmental impact can be minimized simultaneously at the expense of constructability, which is observed by the non-linear Pareto frontier obtained for each pair of these conflicting objectives.

关键词： constructability cost environmental impact multi-objective optimization reinforced concrete columns second-order effect

来源：评论

学校读者我要写书评

暂无评论

multi-objective optimization of gasoline blending scheduling via NSGA-II algorithm with composite operators considering oil movement path planning

引用

EXPERT SYSTEMS WITH APPLICATIONS 2025年 280卷

作者： He, Renchu Bian, Rui Hua, Junjie Zhao, Liang Xu, Feng Long, Jian China Univ Petr Coll Artificial Intelligence Dept Automat Beijing 102249 Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China East China Univ Sci & Technol State Key Lab Ind Control Technol Shanghai 200237 Peoples R China

Gasoline blending is a complex process of mixing multiple components into refined oil, directly affecting the economic benefits and product quality of refineries. However, traditional gasoline blending scheduling optimization methods often overlook the movement paths of oils, leading to misunderstanding between scheduling schemes and actual operations. This paper conducts in-depth research on the tank area topology structure composed of key equipment, such as storage tanks, blending heads, pumps, etc., and constructs a gasoline blending and scheduling model considering oil movement paths. The model aims to maximize refined oil production and delivery volume, minimize excess octane number attributes, and reduce oil movement costs. Because of the complexity of solving the multi-path search problem, this paper introduces NSGA-II for solution, using chromosomes of evolutionary algorithms to represent oil movement paths and combining with a priority search algorithm to ensure path feasibility. Experimental results show this model can provide feasible gasoline blending and scheduling schemes for refinery production.

关键词： Gasoline blending scheduling Oil movement multi-objective optimization Path planning

来源：评论

学校读者我要写书评

暂无评论

multi-objective optimization of the mixed-flow intelligent production line for automotive MEMS pressure sensors

引用

APPLIED INTELLIGENCE 2025年第1期55卷 1-14页

作者： Zhang, Quanyong Li, Hui Shen, Shengnan Cao, Wan Jiang, Jing Tang, Wen Hu, Yuanshun Wuhan Univ Inst Technol Sci Wuhan 430072 Peoples R China Wuhan Univ Sch Power & Mech Engn Wuhan 430072 Peoples R China Wuhan Univ Hubei Prov Engn Res Ctr Integrated Circuit Packagi Wuhan 430072 Peoples R China Wuhan Univ Hubei Key Lab Elect Mfg & Packaging Integrat Wuhan 430072 Peoples R China Wuhan Fine MEMS Inc Wuhan 430075 Peoples R China Wuhan Huagong Cyber Data Syst Co Ltd Wuhan 430074 Peoples R China

Intelligent manufacturing can provide powerful support for the digital transformation of manufacturing industry. Micro-electro-mechanical system (MEMS) sensors have been widely used in the automotive industry because of their small size, low cost, and high reliability. Aiming at the problems of low flexibility, poor adaptability, and high manufacturing cost in the mixed-flow intelligent production line of multi-variety automotive MEMS pressure sensors in this study, a multi-objective optimization model is established with takt time and balance rate as optimization objectives. The non-dominated sorting genetic algorithm-II (NSGA-II) is used to obtain the multi-objective optimization of the mixed-flow intelligent production line with the elite strategy, crowding degree, and crowded comparison operator. The accuracy of the NSGA-II is validated by comparing it with that of the ant colony optimization (ACO) algorithm, simulated annealing (SA) algorithm, and particle swarm optimization (PSO) algorithm. The NSGA-II achieves higher optimization accuracies for takt time and balance rate compared to ACO, SA, and PSO algorithms. Specifically, the NSGA-II achieves optimization accuracies of 2.73%, 2.44%, and 8.99% for the takt time, slightly surpassing those of the ACO, SA, and PSO algorithms respectively. Similarly, for the balance rate, the NSGA-II achieves optimization accuracies of 2.17%, 1.89%, and 2.48%, slightly higher than those of the ACO, SA, and PSO algorithms respectively. The takt time is optimized by NSGA-II to less than 10 s/piece, while the balance rate is optimized to over 90%. The multi-objective optimization of the mixed-flow intelligent production line for automotive MEMS pressure sensors is practical and instructive for improving production line efficiency.

关键词： multi-objective optimization Takt time Balance rate Non-dominated sorting genetic algorithm-II

来源：评论

学校读者我要写书评

暂无评论

multi-objective optimization on combined heat pump and mechanical vapor recompression system for waste heat recovery of offshore wind power converter

引用

DESALINATION 2025年 601卷

作者： Hu, Xiaowei Shi, Chenyang Dong, Shengming Fu, Xingyu Ma, Tianya Chen, Qijing Tianjin Univ Commerce Sch Mech Engn Tianjin 300134 Peoples R China

Offshore wind power converter station generates a significant amount of low-temperature waste heat, requiring tremendous freshwater for discharge. Conventional waste heat utilization solutions are no longer effective because their products are unable to be reused in situ. Therefore, two types of seawater desalination systems combining heat pump and mechanical vapor recompression were proposed and investigated by multi-objective optimization and sensitivity analysis. From a view of thermodynamics and economics, the total heat transfer area and total power consumption were adopted as objective functions. An improved non-dominated sorting genetic algorithm was adopted to determine the key parameters, including the condensation temperature of the heat pump, the temperature of seawater after the electronic expansion valve, and the condensation temperature of water vapor in the seawater and water vapor heat exchanger. The Pareto front and approximate ideal solution sorting methods determined the optimal operational states of the two systems as [70.5, 20, 28.57] and [61.42, 40, 73.48], respectively. From the sensitivity analysis, the condensation temperature of the condenser is the most critical parameter. Moreover, HP-MVR-1 and HP-MVR-2 need 139.75 kW and 158.17 kW electric power if freshwater is fully utilized in situ. It may be an effective solution for waste heat in offshore wind power converters.

关键词： Mechanical vapor recompression Heat pump Seawater desalination multi-objective optimization Non-dominated sorting genetic algorithm Offshore power wind converter

来源：评论

学校读者我要写书评

暂无评论

multi-objective optimization Design Framework and Flight Performance Prediction for Reentry Module Using a Rapid Analysis Program

引用

INTERNATIONAL JOURNAL OF AERONAUTICAL AND SPACE SCIENCES 2025年第3期26卷 1341-1353页

作者： Yeo, Hoonjung Lee, Minsul Kim, Dohoon Kim, Kyu Hong Seoul Natl Univ Dept Aerosp Engn Seoul 08826 South Korea Seoul Natl Univ Inst Adv Aerosp Technol Seoul 08826 South Korea

In this study, we developed a multi-objective design optimization framework that integrates thermal, aerodynamic, trajectory, and structural analyses through the use of low-fidelity and rapid analysis tools. Using a sphere-cone-flare shape as the baseline, the optimization was aimed at minimizing the total heating load, maximizing the volumetric efficiency, and minimizing the ballistic coefficient, employing a genetic algorithm for optimization. The Pareto-front solutions revealed correlations between the total heating load, volumetric efficiency, and ballistic coefficient. Increasing the flare area effectively reduced the total heating load and ballistic coefficient by enhancing the drag and reducing the aerodynamic heating at through significant deceleration. However, this adjustment resulted in a reduction in volumetric efficiency. Conversely, reducing the flare and adopting a blunt hemispherical shape increased the volumetric efficiency yet resulted in higher ballistic coefficients and significant aerodynamic heating. Performance analysis of the proposed designs indicated improved thermal performance and ballistic coefficients compared to the baseline. Future research should focus on optimizing the entry modules for planetary exploration by considering diverse planetary characteristics.

关键词： multi-objective optimization High-speed vehicle design Genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

multi-objective optimization of cold plates for suppressing thermal runaway propagation within a lithium-ion battery module

引用

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2025年 198卷

作者： Fang, Hongjun Gong, Junhui Li, Lun Nanjing Tech Univ Coll Safety Sci & Engn Nanjing 210009 Jiangsu Peoples R China Nanjing Tech Univ Coll Emergency Management Nanjing 210009 Jiangsu Peoples R China

A liquid cooling system composed of three cold plates with varying cold plate thickness (d) and coolant flow velocity (v) was proposed to suppress thermal runaway propagation (TRP) within a lithium-ion battery (LIB) module which consists of 3 rows of prismatic LIBs. A 3D numerical model verified by experimental data was developed to investigate TRP behaviors of 256 cooling scenarios. To balance cooling efficiency and system mass, an Entropy-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) optimization algorithm was employed to determine the optimal solution by considering five crucial indicators, including onset time of TR (ton), duration time of LIB module above critical temperature (Delta t), system mass (M), total volume flow rate of coolant (Vflow), and number of LIB rows suffering TR (n). Effects of d and von these indicators were systematically analyzed. The results show that without cooling all three rows of LIBs experience TR progressively, featuring high TRP rate and long Delta t. After applying the cooling system, row-to-row TRP is greatly decelerated or inhibited, depending on d and v. ton monotonously increases with larger d and v, while step-like dependency of Delta t on v is detected. M only linearly depends on d, and Vflow linearly correlates with d and v. Two boundary lines separating the entire simulation cases into three regions, featuring controlled, transition and uncontrolled TRP scenarios, are obtained by analyzing critical conditions. Ten optimal and ten worst cooling scenarios based on EntropyTOPSIS optimization are identified, which indicate increasing v is more efficient for improving cooling performance than increasing d. The proposed cooling system may be applied in industrial processes to inhibit TRP, where prismatic LIB modules are used, and the drawn conclusions would provide useful insight into risk management of LIBs.

关键词： Thermal runaway propagation multi-objective optimization Lithium-ion battery module Entropy-TOPSIS Liquid cooling

来源：评论

学校读者我要写书评

暂无评论

multi-objective optimization of a Wind/Photovoltaic/Battery hybrid system using a novel hybrid meta-heuristic algorithm

引用

ENERGY CONVERSION AND MANAGEMENT 2025年 327卷

作者： Kapen, Pascalin Tiam Fotso Victor Univ Dschang Univ Inst Technol Dept Renewable Energy POB 134 Bandjoun Cameroon

Hybrid renewable energy systems offer a sustainable and environmentally friendly alternative to traditional energy sources. However, challenges such as high energy costs and the intermittent nature of renewable resources hinder the widespread adoption of these systems. The main innovation of this work lies in the development of a hybrid meta-heuristic algorithm that combines the Grey Wolf and Whale Optimizers. This novel algorithm is applied to optimize the sizing of a wind/photovoltaic/battery hybrid system designed to meet the energy demands of administrative offices at the University Institute of Technology Fotso Victor in Bandjoun, Cameroon, where frequent power outages disrupt academic activities. To achieve optimal system performance, a mathematical framework was developed to minimize three key objective functions: levelized cost of energy, net present cost, and loss of power supply probability. multiple meta-heuristic algorithms, including Grey Wolf, Particle Swarm, African Vultures, Artificial Gorilla Troops, and Whale Optimizers, were evaluated in the study. The proposed hybrid algorithm, which updates the positions of hunters iteratively in a spiral-shaped trajectory, demonstrated superior performance. It achieved optimal values of 5.3077E + 04 US$ for net present cost, 0.17990 US$/kWh for levelized cost of energy, and 0.000541 for loss of power supply probability. In addition, the hybrid algorithm showed better convergence trends, statistical performance, and computational efficiency compared to existing methods. The findings of this work demonstrate the potential of the proposed algorithm as a powerful tool for the optimization of hybrid renewable energy systems, particularly in resource-constrained settings where computational efficiency and solution reliability are critical. Its ability to consistently outperform existing algorithms suggests its applicability to a wide range of energy system design challenges, from microgrids in rural areas to large-sc

关键词： multi-objective optimization Grey wolf optimizer Particle swarm optimizer African vultures optimizer Artificial gorilla troops optimizer Whale optimizer

来源：评论

学校读者我要写书评

暂无评论

multi-objective optimization of Natural Lighting Design in Reading Areas of Higher Education Libraries

引用

BUILDINGS 2025年第9期15卷 1560-1560页

作者： Cui, Xiao Ahn, Chi-Won Hanyang Univ ERICA Dept Architectural Design 55 Hanyangdaehak Ro Ansan 15588 South Korea

Effective natural lighting in university library reading areas significantly influences users' visual comfort, task performance, and energy efficiency. However, existing library lighting designs often exhibit problems such as uneven illumination, excessive glare, and underutilization of natural daylight. To address these challenges, this study proposes a multi-objective optimization framework for library lighting design based on the NSGA-II algorithm. The framework targets the following three key objectives: improving illuminance uniformity, enhancing visual comfort, and reducing lighting energy consumption. The optimization process incorporates four critical visual comfort parameters-desktop illuminance, correlated color temperature, background reflectance, and screen luminance-whose weights were determined using the analytic hierarchy process (AHP) with input from domain experts. A parametric building information model (BIM) was developed in Revit, and lighting simulations were conducted in DIALux Evo to evaluate different design alternatives. Experimental validation was carried out in an actual library setting, with illuminance data collected from five representative measurement points. The results showed that after optimization, lighting uniformity improved from less than 0.1 to 0.6-0.75, glare values (UGR) remained below 22, and daylight area coverage increased by 25%. Moreover, lighting energy consumption was reduced by approximately 20%. Statistical analysis confirmed the significance of the improvements (p < 0.001). This study provides a systematic and reproducible method for optimizing natural lighting in educational spaces and offers practical guidance for energy-efficient and user-centered library design.

关键词： university library multi-objective optimization natural lighting visual comfort energy consumption optimization

来源：评论

学校读者我要写书评

暂无评论

multi-objective optimization analysis for power performance of closed expansion cycle NTR engine with medium thrust

引用

PROGRESS IN NUCLEAR ENERGY 2025年 185卷

作者： Wang, Zhiying Gou, Junli Zhang, Bo Xi An Jiao Tong Univ Sch Nucl Sci & Technol Xian 710049 Peoples R China

Nuclear Thermal Propulsion (NTP) offers significant advances over conventional chemical propulsion systems, providing high thrust, high specific impulse, long endurance and reusability. These capabilities are well suited to the increasing requirements of future space missions. This study focused on the medium-thrust, solid-state, closed-cycle NTP engine, selecting nine critical design parameters for a comprehensive multi-objective optimization (MOO) analysis based on three steady-state performance metrics by implementing the Non-dominated Sorting Genetic Algorithm III (NSGA-III) algorithm and coupling the SCTRAN code to calculate the steadystate thermal-hydraulic parameters. The results presented a range of optimal engine configurations. For scenarios with two predefined mission objectives, the study recommends optimal values for a third performance objective in addition to the nine most important system design parameters. The optimal designs showed a reduced global sensitivity for certain parameters, thereby increasing the robustness of the system. The reliability of the optimization approach was confirmed by comparing one of the study's detailed steady-state results with existing literature. The paper concluded with key optimization recommendations that are instructive for future NTP engine design and refinement.

关键词： NTR multi-objective optimization Solid phase reactor Closed expansion cycle

来源：评论

学校读者我要写书评

暂无评论

multi-objective optimization Design of PCS Box Girder Bridges with Small and Medium Spans Using Genetic Algorithms

引用

BUILDINGS 2025年第3期15卷 361-361页

作者： Li, Zhijie Qi, Jianan Wang, Jingquan Southeast Univ Sch Civil Engn Key Lab Concrete & Prestressed Concrete Struct Minist Educ Nanjing 211189 Peoples R China Southeast Univ Natl Key Lab Safety Durabil & Hlth Operat Long Spa Nanjing 211189 Peoples R China Southeast Univ Bridge Engn Res Ctr Southeast Univ Nanjing 211189 Peoples R China Jiangsu Univ Fac Civil Engn & Mech Zhenjiang 212013 Peoples R China

With the development of algorithms for autonomous decision-making in the field of structural engineering, the design of precast concrete segment (PCS) box girder bridges faces new challenges. This paper proposes using a multi-objective optimization method based on genetic algorithms for the rapid design of PCS box girder bridges with small and medium spans. By considering 20 design parameters such as the physical dimensions of the box girder cross-section, material properties, and prestressing parameters, the paper formulates and quantifies three objective functions: cost, safety, and structural performance. The multi-objective optimization was conducted using four optimization algorithms (NSGA-II, NSGA-III, GDE3, and PSO). An optimization evaluation index (phi[F(x)]) was established and weights were assigned to different optimization objectives. A specific design case based on the general diagram of a 3 x 25 m-long continuous PCS box girder bridge was carried out. The results indicate that genetic algorithms performed exceptionally well on this problem, with the NSGA-III algorithm achieving the best phi[F(x)] value of 0.2789 among all algorithms. A performance analysis was conducted on various optimization models using box plots and sensitivity studies. Scatter plots and surface plots of the Pareto front of the optimized solutions were generated, and corresponding cross-sectional design drawings were created based on the two proposed solutions. Compared with the general graph, the design cases provided by the NSGA-III algorithm model have a change rate of 8.03%, -0.29%, and 75.49% in the three optimization objectives, respectively, indicating a significant improvement effect. The research content of this paper provides a reasonable direction for future studies on intelligent bridge design methodologies.

关键词： multi-objective optimization precast concrete segmental bridge box girder non-dominated sorting genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：