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multi-objective optimization of ultrasonic vibration-assisted drilling in carbon fiber reinforced polyetherketoneketone laminate

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POLYMER COMPOSITES 2025年第4期46卷 3595-3611页

作者： Wu, Nan Zhang, Liqiang Zhang, Meihua Zhao, Man Liu, Gang Shanghai Univ Engn Sci Sch Mech & Automot Engn Shanghai Peoples R China Chengdu Zhiyuan Adv Mfg Technol Inst Chengdu Peoples R China Key Lab Intelligent Mfg Technol Large Complex Thin Shanghai Peoples R China

Carbon fiber reinforced polyetherketoneketone (CF/PEKK) possess excellent mechanical properties and also meet low-carbon environmental standards, leading to their increasingly widespread application in recent years. To ensure the strength of mechanical connections, it is crucial to minimize drilling damage. This paper utilizes Ultrasonic Vibration-Assisted Drilling (UVAD) to enhance hole-making quality, comparing multiple drilling performance indicators with Conventional Drilling (CD), including thrust force, drilling temperature, delamination factor, and burr factor. The results demonstrate that UVAD significantly reduces thrust force and drilling temperature, decreases delamination and burring damage, and effectively improves hole quality. To further optimize processing parameters, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was used, targeting thrust force, Material removal rate (MRR), and delamination factor as optimization goals. The reliability of the model was verified through experimentations. This paper provides an effective technical approach for high-quality drilling in CF/PEKK materials, offering significant practical application value for material processing in aerospace and other *** The quality of CF/PEKK processed by CD and UVAD was compared. UVAD can effectively improve the quality of drilling. The drilling parameters were optimized by NSGA-II. The reliability of the prediction model was verified by experiments.

关键词： CF/PEKK hole-making multi-objective optimization thermoplastic resin UVAD

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multi-objective optimization of syngas production from bio-glycerol: A bi-reforming approach using NSGA-II

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JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING 2025年第3期13卷

作者： Zol, Santosh Chandodkar, Hrushikesh Ahmed, Mukhtar Haider, Mohd Belal Kumar, K. D. P. Lakshmee Naidu, B. Neelam Kumar, Rakesh Viswanadham, Nagabhatla Inst Chem Technol Dept Chem Engn Jalna Maharashtra India Indian Inst Technol Dept Chem Engn Mumbai 400076 India Indian Inst Petr Light Stock Proc Div Dehra Dun India Rajiv Gandhi Inst Petr Technol Chem Engn Dept Amethi India

This study investigates the multi-objective optimization (MOO) of syngas production from bio-glycerol using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) for constrained optimization in the bi-reforming process. Thermodynamic modelling of the system was conducted in Aspen Plus V12, applying the Gibbs free energy minimization method and accounting for all by-products. Key decision variables, including the water-to-glycerol ratio, CO2-to-glycerol ratio, and reforming temperature, were examined for their impact on process performance. Results demonstrated that bi-reforming glycerol is more advantageous for syngas production compared to dry reforming, as it operates at lower temperatures, reducing coke formation. A glycerol bi-reforming process was simulated and optimized using MOO to maximize syngas production and CO2 conversion while minimizing exergy loss and CO2 emissions. The optimization revealed trade-offs among the objectives, with the Pareto front showcasing optimal solutions. Specifically, a higher water-to-glycerol ratio favoured a higher H-2-to-CO ratio but led to decreased CO2 conversion and increased energy consumption and CO2 emissions. The net flow method (NFM) is used to select the best optimal solutions from the Pareto front solutions based on the weight of the objective functions. Despite these trade-offs, bi-reforming of bio-glycerol was shown to be a viable method for producing syngas with a high H-2/CO ratio and low CO2 emissions. The best optimal solutions obtained for the bi-reforming of glycerol show an SGR of similar to 0.44 and CGR of 1.89, with the reforming temperature of 1202 K giving a CO2 conversion of 42.55 % with exergy efficiency of 78.3 % and syngas production of 6.29 per mole of glycerol having H-2/CO ratio of 3.16.

关键词： Glycerol bi-reforming Glycerol value addition Thermodynamic analysis multi-objective optimization Gibbs free energy minimization Non-dominated sorting genetic algorithm-II

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multi-objective optimization of proton exchange membrane fuel cell performance based on a new leaf-shaped flow field

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2025年 120卷 254-275页

作者： Zhu, Haixi Ma, Yinjie Zhang, Lingxu Liu, Hanwen E, Jiaqiang Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Peoples R China

Challenges such as cost, lifespan, and durability hinder the commercialization of proton exchange membrane fuel cell. Enhancing the performance of fuel cell systems and improving water-thermal management are crucial for optimizing the overall performance of fuel cell. This study developed a novel leaf-shaped flow field structure and conducted a comprehensive performance evaluation. The results showed that the leaf-shaped flow field outperforms the serpentine and parallel flow fields in terms of overall performance. The simulation results of central composite design are analyzed based on the response surface methodology. A response regression model of power density, system efficiency, average water content of membrane and maximum temperature difference of cathode catalyst layer was established. The reliability of the regression model was verified through analysis of variance. The non-dominated sorting genetic algorithm II coupled with the regression model was used for multi- objective optimization. The optimal solution was obtained by combining the technique for order preference by similarity to ideal solution decision-making method. Compared to the basic model, the optimal solution showed improvements of 37.12 %, 38.18 %, 22.94 %, and 74.17 % in power density, system efficiency, average water content of the membrane, and the maximum temperature difference in the cathode catalyst layer, respectively.

关键词： Proton exchange membrane fuel cell Flow field design Response surface methodology Computational fluid dynamics multi-objective optimization Non-dominated sorting genetic algorithm II

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multi-objective optimization for Robotaxi Dispatch With Safety-Carpooling Mode in Pandemic Era

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第1期26卷 878-891页

作者： Qi, Liang Li, Mengqi Guo, Xiwang Luan, Wenjing Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Liaoning Petrochem Univ Coll Comp & Commun Engn Fushun 113001 Peoples R China

Autonomous driving has been successfully implemented in such particular areas as logistics distribution centers, container terminals, and university campuses. Robotaxi represents one of its important applications. This work studies a robotaxi dispatch problem during the pandemic era. It aims to design a robotaxi dispatch approach according to a defined severity degree of the pandemic, which can decrease a virus infection rate by reducing contact among passengers. It develops a multi-objective optimization model to minimize travel cost of robotaxis, waiting time of both robotaxis and passengers, and contact among passengers. A two-stage nondominated sorting genetic algorithm (NSGA-TS) is proposed to solve the problem. Three operations are designed to generate new solutions, which can ensure its solution diversity and speed up its convergence. Its effectiveness is verified via its comparison with two popular multi-objective optimization algorithms, i.e., multi-objective evolutionary algorithm based on decomposition (MOEA/D) and nondominated sorting genetic algorithm II (NSGA-II). Experimental results show that the proposed model can effectively reduce travel cost and waiting time. Besides, it can reduce the virus infection rate by decreasing contact among passengers at different severity degrees of the pandemic. This work is conducive for our society to building intelligent transportation systems in the post-pandemic era.

关键词： Robots Costs Public transportation Pandemics optimization optimization models Vehicles Shared transport Genetic algorithms COVID-19 Autonomous driving robotaxi dispatch problem multi-objective optimization nondominated sorting genetic algorithm virus infection rate

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multi-objective optimization for the operation of district heating systems considering flexibility and economy

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APPLIED THERMAL ENGINEERING 2025年 263卷

作者： Chen, Jiaying Lv, Hongkun Ding, Liwei Wu, Gang Zhang, Kang Hou, Chenglong Lai, Zhenya State Grid Zhejiang Elect Power Res Inst Hangzhou 310014 Zhejiang Peoples R China

Facing the transition of energy systems, a critical challenge for the operation and scheduling of the nextgeneration district heating system (DHS) is achieving flexible dynamic matching of thermal energy supply and demand whilst reducing operational costs. This study introduces the Gram-Charlier A expansion method to establish a source-load uncertainty quantification model and thus formulates a method to quantify the operation flexibility considering source-load fluctuations. Furthermore, this study establishes a multi-objective optimization model that considers operation cost and flexibility as optimization objectives, analyzing the optimal dispatch strategy for multi-energy coupled DHS. Utilizing a compromise programming approach, a trade-off curve was obtained, and the impact of total heat supply and heat source configuration on the optimization results was analyzed. A case study of an integrated "thermal-electrical-photovoltaic-storage" DHS in a northern Chinese city demonstrates the effectiveness of the proposed method. The case study indicates that operation cost and flexibility are inversely related;enhancing operation flexibility requires higher operational costs. The results also reveal a threshold effect between the rated power of the heating units and the operation flexibility, where an increase in rated power to a critical value no longer provides gains in flexibility. The study concludes that the proposed multi-objective optimization dispatch method achieves a balance between operational cost and flexibility, effectively reducing system operating costs and enhancing the DHS's adaptability to the dynamic characteristics of multi-energy systems. This research offers valuable insights into the rational configuration of heating units and the integration of renewable energy sources into comprehensive energy systems.

关键词： District heating system Uncertainty analysis Operation flexibility multi-objective optimization

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multi-objective optimization on thermal-hydraulic performance of symmetrical hierarchical microchannel heat sinks

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APPLIED THERMAL ENGINEERING 2025年 271卷

作者： Wang, Yuwei Qi, Cong China Univ Min & Technol Sch Low Carbon Energy & Power Engn Xuzhou 221116 Peoples R China Zhejiang Univ Inst Adv Equipment Hangzhou 310027 Peoples R China

The increasing integration of microelectronic devices will lead to hot spots and reduce service life. The micro-channel heat sink can effectively manage thermal loads, and its energy efficiency requires urgent enhancement amidst global energy constraints. This study investigated the effects of the secondary channel width and trapezoidal bottom angle on the thermohydraulic performance through numerical simulation and multi-objective optimization. Results showed that expanding the secondary channel width provides a larger flow space and increases the heat transfer area. The pressure drop is reduced, while maintaining effective cooling. The energy efficiency ratio of the cooling system is improved. Increasing the trapezoidal bottom angle can increase the velocity and takes away the heat quickly. The temperature uniformity is improved to avoid excessive local hot spot. Taking the minimization of pressure drop and thermal resistance as objectives, the Pareto optimal solution is obtained by Non-dominated Sorting Genetic Algorithm II. Using Technique for Order Preference by Similarity to Ideal Solution with entropy weight to get the optimal compromise solution. The secondary channel width is 0.216 mm, and the trapezoidal bottom angle is 56.93 degrees. Compared with the microchannel without fins, the pressure drop is reduced by 15.19 % and the thermal resistance is reduced by 23.79 %. A new method was provided to improve the efficiency of structural optimization and obtain microchannel structures with high temperature uniformity and energy efficiency ratio, which provides a very important reference value for the parameter optimization of microchannels.

关键词： Microchannels Heat sink multi-objective optimization Temperature uniformity

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multi-objective optimization evaluation of renewable and clean methanol synthesis

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INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2025年 20卷 443-452页

作者： Hai, Tao Rasheed, Rassol Hamed Sharma, Aman Dhawan, Aashim Sharma, Prabhat Rajab, Husam Becheikh, Nidhal Aich, Walid Kolsi, Lioua Singh, Narinderjit Singh Sawaran Ajman Univ Artificial Intelligence Res Ctr AIRC POB 346 Ajman U Arab Emirates INTI Int Univ Fac Data Sci & Informat Technol Nilai 71800 Malaysia Warith Al Anbiyaa Univ Fac Engn Air Conditioning Engn Dept Karbala Iraq GLA Univ Inst Engn & Technol Dept Mech Engn Mathura Uttar Pradesh India Chitkara Univ Ctr Res Impact & Outcome Rajpura 140417 Punjab India Chitkara Univ Chitkara Ctr Res & Dev Kalujhanda 174103 Himachal Prades India Najran Univ Coll Engn Dept Mech Engn POB 1988King Abdulaziz Rd Najran Saudi Arabia Northern Border Univ Min Res Ctr POB 1321 Ar Ar 91431 Saudi Arabia Univ Hail Coll Engn Dept Mech Engn Hail 81451 Saudi Arabia

This study examines the feasibility of establishing a power-to-fuel facility for synthesizing renewable methanol (e-methanol) through the integration of green hydrogen and captured carbon dioxide. Hydrogen is produced via water electrolysis using surplus renewable electricity, while carbon dioxide is captured from a conventional power plant's emissions through an amine-based absorption process. Despite the current economic constraints, with production costs exceeding current market prices for methanol, the study highlights potential future competitiveness, especially considering impending European regulatory frameworks. Furthermore, the study employs a hybrid Grey Wolf Optimizer-Particle Swarm optimization approach to balance the trade-offs between methanol production cost and energy efficiency.

关键词： Clean hydrogen CO2 capture Financial evaluation E-methanol multi-objective optimization

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multi-objective optimization in roll-to-roll UV embossing for manufacturing linear Fresnel lenses

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POLYMER ENGINEERING AND SCIENCE 2025年第5期65卷 2624-2641页

作者： Yu, Cheng-Long Wu, Cheng-Hsien Natl Kaohsiung Univ Sci & Technol Dept Mold & Die Engn Kaohsiung 807 Taiwan

Linear Fresnel lenses are widely used in solar energy concentration, image processing, and optical imaging applications. Concentrated solar power technology offers a more efficient and economical solution by focusing sunlight onto a smaller receiver, thereby increasing energy conversion efficiency and reducing facility costs. This research aims to design and manufacture a concentrating lens that can achieve the most uniform illumination and the lowest energy loss percentage. The optical design of the linear Fresnel lens aims for maximum uniformity and minimal energy loss. The lens parameters include a lens area, a focal length, and a structure spacing. Roll-to-roll UV embossing was applied to produce the linear Fresnel lenses. Four embossing parameters: resin pressure, film speed, film-press pressure, and film tension, were optimized. The design optimization uses the Taguchi method and multi-objective response surface methodology (RSM), which are applied to the optical design and process parameter analysis. The results show that optimized parameter combinations can significantly improve the replication percentage of the lens structure and reduce surface bubble defects. In addition, the linear Fresnel lens was tested on solar cells. The highest quality lenses are manufactured under multi-objective optimization *** multi-objective optimization design using response surface methodology. Applying linear Fresnel lenses to solar cells increases output power. Optimized Fresnel lens design achieves uniform illumination and low energy loss. Roll-to-roll UV embossing makes Fresnel lenses with excellent light focus.

关键词： Fresnel lenses multi-objective optimization roll-to-roll Taguchi methods UV embossing

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multi-objective optimization of fine pulverized coal separator based on computational fluid dynamics and genetic algorithm

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CHEMICAL ENGINEERING SCIENCE 2025年 311卷

作者： Li, Haixia Wang, Ruixiang Li, Zhihui Zhi, Mengzhi Zhang, Anchao Sun, Zhijun Zhang, Xinmin Si, Haibin Hu, Zhijian Hu, Ruiguang Henan Polytech Univ Sch Mech & Power Engn Jiaozuo 454000 Peoples R China Jiaozuo Wanfang Aluminum Mfg Co Ltd Jiaozuo 454172 Peoples R China

This study presents a multi-objective optimization of a helical-roof inlet fine coal separator using computational fluid dynamics (CFD) and a modified Non-dominated Sorting Genetic Algorithm II. Five critical geometric parameters were investigated: vortex finder diameter (De/D), cone tip diameter (Dt/D), cone height (Hc/D), exhaust pipe insertion length (Hin/D), and inlet velocity (v). Response surface methodology revealed that increasing De/ D reduces pressure drop but risks lower efficiency, while larger Hc/D enhances particle residence time and efficiency at the cost of higher pressure drop. Interactions between De/D and Hin/D significantly influenced flow stability. The optimal configuration (De/D = 0.54, Dt/D = 0.39, Hc/D = 4.00, Hin/D = 1.31, v = 12.37 m/s) yielded a 23.96% reduction in pressure drop and 13.61 % improvement in separation efficiency compared to the reference model. CFD validation confirmed the accuracy of the optimized solutions, providing insights for balancing energy consumption and separation performance in industrial separators.

关键词： Separation efficiency Pressure drop Optimal design multi-objective optimization Pareto front

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multi-objective optimization on plate fin layout design in vehicle domain control units using deep learning based non-dominating sorting genetic algorithm II

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INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2025年 210卷

作者： Liang, Lanzhi Lu, Longsheng Huang, Li Xie, Yingxi Yang, Shu Huang, Yongcong Huang, Zeqiang South China Univ Technol Sch Mech & Automot Engn Guangzhou Peoples R China Huizhou Desay SV Automot Co Ltd Huizhou Peoples R China

The transition from decentralized Electronic Control Units (ECUs) to centralized Domain Control Units (DCUs) in automotive electric/electronics architecture intensifies the need for efficient thermal management solutions. High power density chips within minimized DCUs generate significant heat flux over limited spaces, challenging conventional cooling methods amid constraints like limited installation space, weight reduction, and cost efficiency. Natural convection air cooling remains a cost-effective and reliable solution, but optimizing fin arrangements and shapes is critical to enhance heat dissipation. This study presents a multi-objective optimization framework that integrates a deep learning-based genetic algorithm to determine the optimal fin layout for a given heat source and Thermal Interface Materials (TIMs) configuration. A generative temperature field prediction model is developed using a U-Net backbone combined with a mask attention module, effectively predicting temperature fields under varying parameters, including heat source and TIM properties, and fin dimensions. The model demonstrates reliable performance with mean maximum absolute errors of 3.14 degrees C and 1.30 degrees C for the maximum and minimum temperatures of all heat sources, respectively. Such model effectively overcomes the limitations of traditional, computationally intensive optimization techniques that rely on iterative numerical simulations. By integrating a deep learning surrogate model with a genetic algorithm, the framework offers a practical and efficient solution for thermal optimization in complex systems like DCUs, facilitating rapid design iterations and contributing to improved thermal management.

关键词： Domain control units Natural convection multi-objective optimization deep learning Non-dominating sorting genetic algorithm

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