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Layout optimization for offshore wind farms considering both fatigue damage and power generation

RENEWABLE ENERGY 2025年 246卷

作者： Peng, Wangxuan Li, Baoliang Ge, Mingwei Li, Xintao Ding, Wei Li, Bo North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China China Longyuan Power Grp Co Ltd Beijing 100034 Peoples R China

Fatigue damage is a crucial factor affecting the lifespan and reliability of wind turbines. However, this factor is seldom considered in the phase of wind farm layout optimization (WFLO). This oversight can lead to increased operating and maintenance costs, especially for offshore wind farms. To fill this technical gap, a multi-objective WFLO framework considering both fatigue damage and power generation is proposed. Via analytical wake models of wind speed and turbulence, the fatigue damage of each turbine can be rapidly evaluated accounting for both power production and wake turbulence. Typically, the Horns Rev wind farm is taken as a benchmark, and two indicators of fatigue damage, the maximum (Fmax) and mean (Fmean) fatigue damage of all turbines, are considered in WFLO, respectively. The optimized layouts can achieve an increase in total power generation of over 2 % compared to the original layout, while reducing fatigue damage by about 1 %. Moreover, compared to single-objective WFLO focused solely on power generation, the multi-objective approach achieves a reduction of about 3.2 % in Fmax, while maintaining a comparable power output level. Further investigation shows that the indicator of Fmax in WFLO performs better to effectively reduce and balance the fatigue damage of all turbines.

关键词： Wind farm layout optimization multi-objective genetic algorithm Power generation Turbine fatigue damage

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The robotic assembly line balancing problem under task time uncertainty

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2025年第5-6期137卷 2991-3011页

作者： Zacharia, Paraskevi Nearchou, Andreas C. Univ West Attica Dept Ind Design & Prod Engn Athens 12244 Greece Univ Patras Dept Business Adm Patras Achaia Greece

Consideration is given to the robotic assembly line balancing problem (RALBP) under uncertain task (operation) times, a critical challenge encountered in automated manufacturing systems.. RALBP is a decision problem which seeks the optimal assignment of the assembly work as well as the most suitable robots to the workstations of the assembly line with respect to objectives related to the capacity of the line or/and its cost of operation. When multiple types of robots with different capabilities are being used, task times may vary depending on robot type and the nature of the task. Task variation is expected to be small for simple tasks but may be quite large for complex and failure sensitive operations. To deal with uncertainty in task variation we used fuzzy logic theory. First, we introduce formally the fuzzy RALBP and then we describe deeply the fuzzy representation of the task times. We address RALBP with respect to two optimization objectives namely, the production rate and workload smoothing. Since the problem is known to be NP-hard, we explore its heuristic solution through a new robust multi-objective genetic algorithm (MOGA) aiming to determine the Pareto optimal set. Simulation experiments assess MOGA's efficiency in comparison to the famous NSGA-II and MOPSO algorithms, while also exploring the trade-off between the two conflicting objectives.

关键词： Robots Flow lines Fuzzy task times multi-objective genetic algorithm Pareto optimization Metaheuristics

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A hybrid sonic black hole for enhancing low-frequency sound absorption

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PHYSICA SCRIPTA 2025年第5期100卷 055003-055003页

作者： Xian, Yuxuan Cheng, Baozhu Liu, Yang Zhang, Yanni Zhang, Zhiqiang Rugwizangoga, Blaise Kevin Jiangsu Univ Sci & Technol Sch Energy & Power Zhenjiang 212003 Peoples R China Northwestern Polytech Univ Sch Marine Sci & Technol Lab Ocean Acoust & Sensing Xian 710072 Peoples R China Soochow Univ Sch Rail Transportat Suzhou 215131 Peoples R China Suzhou Dongling Vibrat Test Instrument Co Ltd Suzhou 215163 Peoples R China Nanjing Univ Sci & Technol Inst Launch Dynam Nanjing Peoples R China Natl Key Lab Complex Multibody Syst Dynam Beijing Peoples R China

Sonic black hole (SBH) is a kind of broadband sound absorber, which features lightweight and high-efficiency. However, achieving low frequency sound absorption at a deep-subwavelength thickness remains a challenge for conventional SBHs. Herein, a hybrid sonic black hole (HSBH) with gradually varying neck-embedded Helmholtz resonators (HRs) and micro-perforated panels (MPPs) is proposed. The working bands are broaden at low frequencies with sub-wavelength and broadband sound absorption in the mid-and-high frequency range. Such HSBH exhibits excellent potential in broadband sound absorption with sub-wavelength. The transfer matrix method (TMM) is used to analyze the low-frequency sound absorption of HSBH, which is validated by the finite element method (FEM). It is shown that the proposed HSBH exhibits high sound absorption with sub-wavelength (the frequency of first peak is 102 Hz, about lambda/30 ). The enhanced low-frequency sound absorption of the proposed HSBH is also validated by the experiment results. Meanwhile, the influences of various structural parameters on the low-frequency properties and broadband characteristics of HSBHs are also revealed. A fast and elitist nondominated sorting genetic algorithm (NSGA-II) is introduced to optimize the HSBH. Finally, a coplanar HSBHs are also designed to achieve a broadband sound absorption from 100 to 3000 Hz.

关键词： sonic black holes sub-wavelength sound absorber acoustic metamaterials multi-objective genetic algorithm coplanar HSBHs

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An innovative optimal integrated solar-lignocellulosic biomass polygeneration system with biorefinery and solid oxide electrolyzer cell

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ENERGY CONVERSION AND MANAGEMENT 2025年 327卷

作者： Morid, Mehdi Manesh, Mohammad Hasan Khoshgoftar Univ Qom Energy Environm & Biol Syst Res Lab EEBRlab Div Thermal Sci & Energy Syst Dept Mech EngnFac Technol & Engn Qom Iran

Polygeneration systems combine various energy sources and processes to produce multiple products for residential and industrial needs in a unified system, offering enhanced sustainability and resilience compared to single-process systems. Optimizing these systems maximizes energy resource utilization. This study introduces a comprehensive polygeneration system generating eight products to satisfy power, cooling, heating, and secondary product requirements like fresh water, hydrogen, oxygen, carbon dioxide, and ethanol. The system is based on two renewable energy sources, solar and biomass, as well as traditional methane energy. Through a comprehensive evaluation encompassing energy, exergy, exergo-economic, exergo-environmental, emergoeconomic, and emergo-environmental considerations, the system is thoroughly evaluated. Mathematical modeling in MATLAB assesses thermal, economic, and ecological factors for optimization. Validation is performed using Thermoflex and Aspen Plus for an ethanol and power co-production biorefinery from corn stover. multi-objective optimization employs a machine learning and genetic algorithm approach in two phases. The initial phase targets three objectives for the hybrid multi-effect distillation adsorption desalination unit to optimize fresh water production, performance ratio, and energy consumption. Our results demonstrate that in the optimal scenario, there is an 8.2% increase in fresh water production rate, a 10.2% increase in performance ratio, and a 9.2% reduction in specific energy consumption. In the second phase, the overall system is optimized with the objective of maximizing thermodynamic performance while minimizing economic and environmental indicators. The six-objective optimization results in a 2.54% increase in overall energy efficiency, a 3.38% increase in overall exergy efficiency, and a 59.66% improvement in the coefficient of performance of the ejector refrigeration cycle. Additionally, it leads to a 1.88% reduction i

关键词： multi-objective genetic algorithm Solid Oxide Electrolysis Cell Recurrent Neural Network Optimization Bioethanol Biorefinery Renewable energy

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Toward green container liner shipping: joint optimization of heterogeneous fleet deployment, speed optimization, and fuel bunkering

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2024年第0期

作者： Zhao, Yuzhe Peng, Zhongxiu Zhou, Jingmiao Notteboom, Theo Ma, Yiji Dalian Maritime Univ Collaborat Innovat Ctr Transport Studies Dalian 116026 Peoples R China Dalian Univ Foreign Languages Business Sch Dalian 116044 Peoples R China Univ Ghent Maritime Inst B-9000 Ghent Belgium Univ Antwerp Fac Business & Econ B-2000 Antwerp Belgium Antwerp Maritime Acad Fac Sci B-2030 Antwerp Belgium

Container liner shipping companies, under the international shipping carbon reduction indicators proposed by the International Maritime Organization, must transform two key aspects: technology and operations. This paper defines a green liner shipping problem (GLSP) that integrates the deployment of a heterogeneous fleet, speed determination, and fuel bunkering. The objective is to achieve low-carbon operations in liner shipping, taking into consideration the diversification of power systems, the use of alternative fuels in ships, and the continuous improvement of alternative fuel bunkering systems. For this purpose, we present a bi-objective mixed integer nonlinear programming model and develop two methodologies: an epsilon-constraint approach and a heuristic-based multi-objective genetic algorithm. We validate the effectiveness of our model and methods through a case study involving container ships of various sizes deployed on intra-Asian short sea routes by SITC International Holdings Co., Ltd. The experimental results highlight the crucial role of dual-fuel (DF) ships in the pursuit of low-carbon strategies by liner companies, with liquefied natural gas and ammonia DF ships being the most widely used. Additionally, fuel cell (FC) ships, particularly those powered by ammonia and hydrogen, demonstrate significant carbon reduction potential. Furthermore, ships with larger container capacities have a greater cost advantage. For the GLSP, speed determination is an auxiliary decision, and the lowest speed is not necessarily the optimal choice. Decision-makers must carefully balance competing economic and carbon emission reduction objectives, as deploying more alternative fuel ships may increase fuel bunkering and fuel consumption, resulting in a higher total operating cost.

关键词： liner shipping heterogeneous fleet deployment speed determination fuel bunkering carbon emission bi-objective mixed integer nonlinear programming multi-objective genetic algorithm

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Investigation of high-compactness and high-efficiency TPMS precoolers for precooled aero-engine

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ENERGY 2025年 319卷

作者： Huang, W. S. Ning, H. Y. Tang, G. H. Xi An Jiao Tong Univ Sch Future Technol Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Energy & Power Engn Key Lab Thermo Fluid Sci & Engn MOE Xian 710049 Peoples R China

The precooler is the core functional component of the precooled air-breathing engines, which requires high power-to-weight ratio and robustness. However, the conventional compact tubular precooler suffers from low reliability, poor manufacturability and uneven flow distribution. The present study proposes a novel precooler based on triply periodic minimal surface (TPMS) in polar coordinate form. Firstly, the linear approximation method is introduced and regression models are established by response surface methodology (RSM) to determine the geometry parameters of polar TPMS. Then, the accuracy of the numerical method for TPMS is validated by experimental tests and new correlations for thermal and hydraulic performances of TPMS precooler are fitted. Subsequently, the segmental thermal design method and multi-objective genetic algorithm are combined for the multi-objective optimization of the precooler. Finally, the superior optimization objective combination selected by the decision-making method is compared with the conventional compact tubular precooler. The results indicate that the optimized TPMS precooler volume is reduced by 27.8%, with 23.8% increase in power-to- weight ratio. Additionally, the TPMS precooler fabricated by additive manufacturing can exhibit high reliability. The present methods for the novel TPMS precooler are also straightforward for high efficient and high compact heat exchangers in other fields under harsh thermal environment.

关键词： Triply periodic minimal surface Linear approximation method Segmental design method Parameter analysis Response surface method multi-objective genetic algorithm

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A High-Reliability Small-Area Task Offloading Mechanism With Trust Evaluation and Fuzzy Logic in Power IoTs

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第4期24卷 2935-2948页

作者： Wang, Suhong Qin, Tuanfa Chen, Tingting Guo, Wenhao Hu, Yongle Sun, Hongmin Guangxi Univ Sch Comp Elect & Informat Guangxi Key Lab Multimedia Commun & Network Techno Nanning 530004 Peoples R China Guangxi Vocat Tech Inst Ind Nanning 530001 Peoples R China Guangxi Univ Sch Comp Elect & Informat Guangxi Key Lab Multimedia Commun & Network Techno Nanning 530004 Peoples R China Guangxi Univ Sch Elect Engn Guangxi Key Lab Multimedia Commun & Network Techno Nanning 530004 Peoples R China Runjian Co Ltd Nanning 530007 Peoples R China Guangxi Vocat Tech Inst Ind Nanning 530001 Peoples R China

In order to solve the problem that high-priority tasks can not be processed timely and reliably due to the disorder of multi-task and dynamicity in Power Internet of Things(PIoTs), a high-reliability small-area task offloading mechanism with trust evaluation and fuzzy logic(HRSATF) is proposed. First, considering task priority, preemptive priority queue is introduced to ensure high-priority tasks processed preferentially, and minimum resource allocation coefficients(MRACs) of tasks are solved to ensure the effectiveness of offloading. Second, the trust model between smart device(SD) and edge server(ES) is established, and ESs are divided into three priorities based on trust value and computing power by fast non-dominated sorting. Thirdly, fuzzy logic is applied to select target ES when the priorities of task and ES do not match or the ES is offline, and MRAC is used to schedule tasks between SD and ES. Finally, NSGA2 is modified (MNSGA2) to verify the effectiveness of HRSATF in terms of success rate, time, power consumption and load balancing, where success rate is increased by 102.3%, and time and power consumption are decreased by 90.7%, 89.3% at most, respectively.

关键词： Fuzzy logic Costs Reliability Blockchains Resource management Mobile computing Computational modeling Cloud computing Smart grids Delays multi-objective genetic algorithm preemptive priority resource allocation task offloading trust evaluation

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Probabilistic seismic demand analysis for bridges based on earthquake scenarios

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ENGINEERING STRUCTURES 2025年 323卷

作者： Wang, Tianjia Xie, Xu Cheng, Cheng Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310058 Peoples R China

The current probabilistic seismic demand analysis of structures predominantly considers the correlation between the ground motion intensity measure (IM) and structural seismic demand, but fails to effectively capture regional source characteristics. It is essential to integrate earthquake engineering with seismic response of structures using source parameters to quantify regional ground motion uncertainties and determine appropriate IMs. This study presented a probabilistic seismic demand analysis method for structures based on earthquake scenarios, characterized by 1) utilizing the stochastic finite-fault method to simulate ground motions at a specific site instead of employing multiple seismic waves from different source areas, and 2) incorporating the correlation between IMs and the key indicator of source parameters, stress drop, into the probabilistic seismic demand analysis. Taking a continuous girder bridge as an example, the performance of the stress drop and 14 individual IMs in predicting the longitudinal seismic demand and reflecting source characteristics was evaluated. The results revealed that it was difficult to establish a reliable relationship between the stress drop and structural seismic demand. Owing to the complexities of ground motion and structural parameters, the individual IMs were challenging to effectively capture their characteristics simultaneously. Therefore, composite IMs have been proposed to cover the characteristics of both the source and seismic demand. The optimal solutions were derived using a multi-objective genetic algorithm, which exhibited desirable capabilities in both aspects, providing a comprehensive guide for the seismic performance assessment of regional transportation networks.

关键词： Probabilistic seismic demand analysis Stochastic finite-fault method Intensity measure Uncertainties in ground motions multi-objective genetic algorithm

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multi-objective optimization of double-layer microchannel heat sink with trapezoidal cross-sections based on computational fluid dynamics

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INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2025年 214卷

作者： Mohammadi, Rafat Dadras, Vahid Arak Univ Dept Mech Engn Arak Iran

This study presents a multi-objective optimization of a trapezoidal double-layer microchannel heat sink (TDLMCHS) to minimize both thermal resistance and pumping power. A three-dimensional fluid-solid conjugate heat transfer model, coupled with a multi-objective genetic algorithm, was employed. The optimization considered five design variables, including the flow rate ratio between the upper and lower channels and four geometric parameters related to the channel cross-sections. A parametric study explored the design space, and response surface approximation was applied to improve computational efficiency. The results showed that the optimized TDL-MCHS achieved up to a 42 % reduction in thermal resistance, though at the cost of a significant increase in pumping power. Conversely, minimizing pumping power by 42 % led to a 4 % reduction in thermal resistance. The Pareto-optimal front highlights the trade-offs between thermal performance and energy consumption, providing valuable insights for the efficient design of TDL-MCHSs in electronic cooling applications.

关键词： Double-layer microchannel heat sink Trapezoidal cross-section multi-objective genetic algorithm Thermal resistance Pumping power Electronic cooling Thermal management

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A novel multi-objective optimization model for designing a reverse supply chain network

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JOURNAL OF CLEANER PRODUCTION 2025年 499卷

作者： Momeni, Mojtaba Arab Mostofi, Amirhossein Jain, Vipul Chan, Felix T. S. Persian Gulf Univ Jam Fac Engn Bushehr Iran Auckland Univ Technol Dept Management Org & Technol Auckland New Zealand RMIT Univ Melbourne Coll Business & Law Sch Accounting Informat & Supply Chain Dept Logist & Supply Chain Management Melbourne Australia Macau Univ Sci & Technol Sch Business Dept Decis Sci Taipa Macao Peoples R China

Reverse supply chain management is one of the most effective ways to protect the environment and conserve natural resources while generating significant economic benefits for manufacturing firms. This paper examines the design of a reverse supply chain consisting of various facilities, including collection, inspection, and recycling centers, internal and external remanufacturing plants, suppliers, and secondary markets. The supply chain aims to minimize costs and carbon emissions. Additionally, the proposed model is extended to a multi-objective robust stochastic-based model to consider the uncertainty in the amount and quality of return products. Furthermore, to derive the Pareto solutions, two methods, epsilon- constraint, and the Metaheuristic algorithm of the multi-objective genetic algorithm are employed. In the multi-objective genetic algorithm, a new representation of solutions is provided, considering all dependencies between the variables and embodying the concept of robustness in the solutions of the Metaheuristic algorithms. According to the research findings, the Metaheuristic algorithm could compete with the exact method of epsilon- constraint in small-size problems, while the multi-objective genetic algorithm outperforms it in large-size problems. On the other hand, a sensitivity analysis reveals that revenue and emission-related parameters influence the level of expansion in the reverse supply chain, while the standard deviation of uncertain parameters mostly affects the robustness of the solutions. A balanced capacity throughout the supply chain is also recognized as essential for improving profitability and flexibility. Additionally, the model results showed that reducing emissions related to facilities can create opportunities for all the identified objectives within the supply chain.

关键词： Reverse supply chain Robust optimization epsilon- constraint method multi-objective genetic algorithm

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