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multi-objective optimization of comprehensive performance in airfoil channel PCHEs using Bezier curves

APPLIED THERMAL ENGINEERING 2025年 261卷

作者： Liu, Rui Zhang, Chuanliang Zhao, Bin Chen, Jiaxiang Wang, Ziyi Changsha Univ Sci & Technol Coll Energy & Power Engn Changsha 410114 Peoples R China Changsha Univ Sci & Technol Sch Elect & Informat Engn Changsha 410114 Peoples R China

The airfoil fin (AFF) Printed circuit heat exchanger (PCHE) has attracted significant attention for its excellent comprehensive performance. This study proposes an optimized design for AFF PCHE to enhance the comprehensive performance by integrating Bezier curves, computational fluid dynamics (CFD), and multi-objective genetic algorithm (MOGA). A set of 12 Bezier curve-based variables is utilized to define and control the airfoil geometry, with optimization targets set on two comprehensive evaluation criteria: the first enhanced ratio (n1) and the third enhanced ratio (n3). The MOGA-generated Pareto front reveals the evolution of AFF structures in relation to n1 and n3. Results show that as the leading and trailing edges of the AFFs become sharper and the thickness decreases, the n1 of the PCHE channel gradually increases, while n3 decreases. Conversely, as the thickness of the AFFs increases and the trailing edge shape transitions from blunt to elliptical and finally to round, n3 significantly increases while n1 decreases. Furthermore, when changes focus mainly on the leading edge of the AFFs, n3 improves without markedly affecting n1. Compared to the traditional airfoil channel, the n1 of the Fin-b channel increases by 3.1%-10.8%, demonstrating its greater suitability under identical flow rate conditions. Similarly, the n3 of the Fin-g channel is 1.4%-11.6% higher than that of the traditional airfoil channel, highlighting its superior performance under identical pumping power conditions. The present work provides a valuable reference for optimizing the design of AFF PCHEs under identical flow rate and pumping power conditions.

关键词： Airfoil fin PCHE Bezier curves Pareto front multi-objective genetic algorithm Comprehensive performance

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Hybrid modeling and optimization of fiber laser hole cutting of austenitic stainless-steel sheets using response surface

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LASER PHYSICS 2024年第4期34卷 045602-045602页

作者： Niu, Zhenhua Dehkordi, Mohammad Hossein Razavi Ghazi, Mohammadreza Akbari, Mohammad Coll Electromech Engn Anyang Vocat & Tech Coll Anyang 455000 Peoples R China Islamic Azad Univ Dept Mech Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Aerosp & Energy Convers Res Ctr Najafabad Branch Najafabad Iran

In this study, an efficient approach was proposed to systematically model and optimize the laser small hole cutting process parameters using a hybrid approach for the design of experiment and multi-objective genetic algorithm optimization. The central composite design and response surface methodology were used to effectively model the impact of four main factors: cutting speed, laser power, gas pressure and focal distance on the responses. The responses considered were hole diameter circularity tolerance, spattering and cut kerf width, which were used to evaluate the quality of the laser hole cutting. The regression equations were used to model the effect of process parameters and their interactions on the responses. These regression models were then used as objective functions for optimization. The results show that the focal distance and laser power have had a significant influence on the hole diameter circularity tolerance and the variation in size of the cut kerf. In particular, the melted material spattering rate increased threefold when the focal distance increased from 0.4 to 0.8 mm. The optimization results highlighted that the best outcomes in terms of minimum deviation, spatter, and the cut-kerf width were achieved at low power (between 605 and 685 W) and low speeds (in the range of 11.1-12.7 m min-1). The optimal focal distance for all solutions was found to be 0 mm for the gas pressure (between 6.5 and 8 bars) to minimize the objective functions.

关键词： laser hole cutting response surface methodology multi-objective genetic algorithm dimensional tolerances optimization

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Experimental investigation on the optimal control of vortex shedding of a circular cylinder with rotating rods at moderate Reynolds numbers

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JOURNAL OF FLUIDS AND STRUCTURES 2024年 124卷

作者： Silva, Paulo H. N. Assi, Gustavo R. S. Univ Sao Paulo Dept Naval Architecture & Ocean Engn Escola Politecn Sao Paulo Brazil

This paper presents an experimental study of fluid-structure interaction conducted at Reynolds numbers around 104 with scale models in a water channel. A circular cylinder was equipped with eight control rods positioned around its perimeter to interact with the external flow. Each rod could be driven independently to rotate about its axis. The controlled rotation of the rods interfered with the vortex generation mechanism mitigating the formation of a coherent wake. The results showed that it is possible to simultaneously reduce the mean drag and fluctuating lift forces when the rods rotate at a uniform speed or with each rod set at a different rate. A multi-objective genetic algorithm was employed to find the optimum rotation speeds. The optimum rotation produced a more significant reduction in both objectives and also consumed less energy. The contribution of each rod depends significantly on its angular position around the body and the flow conditions resulting from the upstream control rods. The present work clarifies the physical principles of the phenomenon and paves the way for the development of technological applications.

关键词： Bluff body Flow control Vortex shedding Control rods multi-objective genetic algorithm

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Development and simulation of a gridded thermionic cathode electron gun for a high-energy photon source

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Nuclear Science and Techniques 2023年第3期34卷 73-81页

作者： Sheng-Chang Wang Da-Yong He Cai Meng Jing-Yi Li Zu-Sheng Zhou Jin-Dong Liu Institute of High Energy Physics Chinese Academy of SciencesBeijing 100049China

A gridded thermionic cathode electron gun was developed for the linear accelerator of the High Energy Photon Source(HEPS).An electron gun should provide a large maximum bunch charge with a wide adjustable *** satisfy these requirements,the shape of the electrode was optimized using a multi-objective genetic algorithm.A large bunch charge with an adjustable range was achieved using the grid-limited gun,the flow of which was analyzed using 3-D *** electron gun has been manufactured and tested,and the measured data of the grid-limited current and simulation results are compared and discussed in this study.

关键词： Electron gun multi-objective genetic algorithm Grid CST

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Optimization of the Structural Parameters of a Cyclone by Using a genetic algorithm

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CHEMICAL ENGINEERING & TECHNOLOGY 2022年第9期45卷 1691-1700页

作者： Song, Chen Wang, Jiarui Wang, Simin Wen, Jian Xi An Jiao Tong Univ Sch Chem Engn & Technol 28 Xianning VVest Rd Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Energy & Power Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China

A mathematical model is proposed to optimize a two-stage separation cyclone. The model combines the discrete phase model, multi-objective genetic algorithm, and response surface model. The model was validated by experimental data with an average deviation of 9.87 %. The effects of three structural parameters on the comprehensive performance of this cyclone are discussed. By applying a central composite design method, the cyclone was optimized and three optimal designs were obtained, with an average pressure drop reduction of 6.05 %, and 2.34-fold improvement in separation efficiency over the original design. The proposed model and results are of great significance for the further design of this type of cyclone.

关键词： multi-objective genetic algorithm Optimal design Structural parameters Two-stage separation Ultrafine particles

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Enhancing Steel Wheel Ventilation Efficiency Through multi-objective Optimization

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第8期49卷 11661-11682页

作者： Bogrekci, I. Demircioglu, P. Sasmaz, M. E. Unal, C. Aydin Adnan Menderes Univ Fac Engn Dept Mech Engn Aydin Turkiye Tech Univ Munich Inst Mat Sci TUM Sch Engn & Design Garching Germany Jantsa Wheel Ind Design Ctr Aydin Turkiye

This study focuses on the optimization of ventilation hole design in steel wheels used for heavy commercial vehicles. The primary objective is to reduce the weight of the wheel while ensuring compliance with radial fatigue and cornering fatigue test requirements. Four distinct ventilation types were parametrized using ANSYS Mechanical, with the von Mises stress on the disk, number of ventilations, and wheel weight serving as design parameters. Stress analysis and weight comparisons were performed between wheels featuring different ventilation types and an ellipse ventilation wheel. Incorporating the design of experiment (DoE) and response surface optimization (RSO) module in ANSYS Workbench 2022 R1 was employed to compare and evaluate the obtained values. Subsequently, the multi-objective genetic algorithm (MOGA-II) method was employed for optimization, aiming to identify the optimal design. The optimization process, utilizing a maximum of 20 iterations, a convergence stability percentage of 2%, and a maximum allowable Pareto percentage of 70%, yielded 1, 3, 3, and 3 candidate design points for round, slot, trapezoid, and halfmoon-type ventilation holes, respectively. Among the various ventilation types considered, the halfmoon-type ventilation hole exhibited the most promising results. Compared to the current design, the optimized wheel achieved a weight reduction of 0.9 kg (2.05%). This outcome demonstrates the effectiveness of the proposed methodology. Although lighter designs were not attainable while maintaining the same stress values for the other three ventilation types, the halfmoon-type ventilation hole was ultimately selected as the preferred design.

关键词： Design of experiment multi-objective genetic algorithm Finite element method Radial fatigue test Response surface optimization

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A data-driven multi-objective optimization approach for enhanced methanol yield and exergy loss minimization in direct hydrogenation of CO2

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APPLIED THERMAL ENGINEERING 2024年 251卷

作者： Samad, Abdul Saghir, Husnain Musawwir, Abdul Ahmad, Iftikhar Caliskan, Hakan Natl Univ Sci & Technol Sch Chem & Mat Engn Islamabad 44000 Pakistan Friedrich Alexander Univ Erlangen Nurnberg Dept Chem & Biol Engn Erlangen Germany Usak Univ Fac Engn & Nat Sci Dept Mech Engn TR-64200 Usak Turkiye

The stability and control of process industries have historically faced obstacles due to inherent uncertainties in their operations. This work focuses on leveraging machine learning models as a surrogate to facilitate real-time optimization of operating conditions of the direct hydrogenation of CO 2 to enhance methanol production while minimizing exergy losses. Numerous studies have explored steady-state exergy analysis and the impact of various process conditions on methanol production. However, there is a notable absence of research investigating the influence of exergy destruction on methanol production under dynamic conditions. Initially, a commercial software Aspen HYSYS was utilized to design, model, and simulate the methanol synthesis plant. Exergy analysis was conducted to quantify the process exergy losses, exergy efficiency, and potential areas for improvement. The process model transitioned to a dynamic mode by incorporating +/- 5% uncertainty into critical operating conditions, i.e. , temperature, pressure, and molar flow rate, simulating realworld variability and resulting in a dataset of 370 samples. Two machine learning models;the Gaussian process regression (GPR) and artificial neural network (ANN) model were developed using the data samples to predict the process exergy losses and molar flow rate of methanol produced. To enhance the predictive capabilities of these deployed models, Bayesian optimization was employed for hyperparameter tuning. The developed models were employed as surrogates in multi -objective genetic algorithm (MOGA) environments with the aim of maximizing the production of methanol with minimum exergy losses under uncertainty. The optimized process conditions, derived from MOGA-based methods, underwent cross -validation using the Aspen model. This analysis revealed that the methanol synthesis plant exhibited an exergy loss of 2425 kW, an exergy efficiency of 97.78%, and an improvement potential of 53.74 kW. The GPR and ANN models

关键词： multi-objective genetic algorithm Exergy losses Carbon capture and conversion Energy recovery Machine learning Renewable methanol

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Modelling and application of joint maintenance grouping and workload smoothing for an automotive plant

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第8期62卷 2832-2852页

作者： Truong, Minh-Tuan Vu, Hai-Canh Do, Phuc Iung, Benoit Voisin, Alexandre Univ Lorraine CRAN UMR 7039 CNRS Vandoeuvre Les Nancy France Roberval Univ Technol Compiegne Compiegne France

In the maintenance optimisation framework, grouping maintenance is a promising solution for maintenance planning of multi-component systems, in which maintenance activities are performed together to reduce maintenance costs. One of the most widely identified challenges in real applications of grouping maintenance is that it may disturb the maintenance workload balance (smoothness), causing many difficulties in production and/or labour scheduling and inventory management. In this study, we propose a joint optimisation approach for maintenance grouping and workload balancing to address the above challenge. First, a mathematical model of the joint optimisation problem was derived. A multi-objective grouping optimisation approach based on the Weighted Sum model and genetic algorithm was implemented to determine the Pareto-optimal grouping solution. The proposed approach was applied to a real case study of an automotive plant comprising 40 production lines with 1090 components. The results highlighted the advantages, effectiveness, and flexibility of the proposed maintenance approach in real-world applications.

关键词： Maintenance grouping workload smoothing automotive maintenance multi-objective genetic algorithm multi-component system

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Optimization design of trapezoidal flow field proton exchange membrane fuel cell combined with computational fluid dynamics, surrogate model, and multi-objective optimization algorithm

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IONICS 2024年第6期30卷 3375-3389页

作者： Wang, Changjiang Yu, Zeting Wu, Haonan Wang, Daohan Shandong Univ Sch Energy & Power Engn Jinan 250061 Peoples R China Shandong Univ Sch Elect Engn Jinan 250061 Peoples R China

The flow field structure plays the key roles in the operating reliability and power output of proton exchange membrane fuel cell (PEMFC). This study investigates and compares two typical structural parameters of PEMFC with the trapezoidal flow channel (TFC) and the trapezoidal flow channel with block (TFCB). A three-dimensional (3-D) multiphase TFC model is first developed, and then the multi-objective optimization is performed by using the trained artificial neural network (ANN) surrogate model and non-dominated sorting genetic algorithm (NSGA-II). Finally, the technique for order preference by similarity to an ideal solution (TOPSIS) is used to investigate the optimized structural parameters of TFC. The results show the net power output and the oxygen uniformity index of the optimized TFC are increased by 19.77% and 21.92% compared with the straight flow channel (SFC). Furthermore, it is also found using block in the trapezoidal flow channel (TFCB) can increase the performance of PEMFC, and it exhibits a 25.10% improvement for the net power output and 27.88% for oxygen uniformity index, respectively.

关键词： Proton exchange membrane fuel cell Trapezoidal flow field optimization Block Computational fluid dynamics Artificial neural network multi-objective genetic algorithm

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A multi-objective optimization approach for green supply chain network design for the sea cucumber ( Apostichopus japonicus ) industry

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SCIENCE OF THE TOTAL ENVIRONMENT 2024年 927卷 172050页

作者： Ren, Anqi Yu, Lixingbo Zhao, Xintao Jia, Fei Han, Fengfan Hou, Haochen Liu, Ying Dalian Ocean Univ Minist Educ Key Lab Environm Controlled Aquaculture 52 Heishijiao St Dalian 116023 Peoples R China Dalian Ocean Univ Coll Marine Technol & Environm 52 Heishijiao St Dalian 116023 Peoples R China Dalian Bangchui Isl Sea Cucumber Dev Co Ltd 987 Wuyi Rd Dalian 116100 Peoples R China Zhejiang Univ Coll Biosyst Engn & Food Sci 866 Yuhangtang Rd Hangzhou 310058 Peoples R China

In China, aquatic supply chain network design does not include the green concept or the coordination of environmental and economic performance. Sea cucumber ( Apostichopus japonicus ) is an aquatic product of high economic value;however, studies on sea cucumber supply chain network optimization are lacking. This study is the first to design the sea cucumber supply chain and construct an optimization model. Considering the characteristics of the sea cucumber industry, LCA for Experts software and the CML-IA-Aug. 2016 -world method were used to assess each aquaculture model ' s global warming potential (GWP), as the environmental performance indicator. In addition, multi -objective genetic algorithm (MOGA) coupled with Modified Technique for Order of Preference by Similarity to Ideal Solution (M-TOPSIS) integrates yield production, economic benefits, and environmental performance. The results demonstrated that cage seed rearing (CSR) combined bottom sowing aquaculture (BSA) represents the best production strategy upstream of the sea cucumber supply chain. In the downstream, the best proportion of sales channels in supermarkets, boutique stores and online shops accounted for 14.79 %, 58.02 % and 27.19 % of the production, respectively. The proposed optimization scenario 4 (S4) can increase product profit by 27.88 % and reduce GWP by 56.89 %. The following improvement measures are proposed: using sea cucumber aquaculture industry standards (cleaner production and green supplier selection) to regulate the behavior of enterprises, adopting an ecological and green production strategy, eliminating highenergy consumption and high emission production practices, and promoting widespread adoption of green consumption concepts. Finally, these measures may improve the sea cucumber supply chain, achieve coordinated environmental and economic performance development in the sea cucumber industry, and provide guidance for green optimization of other aquatic product supply chains in C

关键词： Life cycle assessment Green supply chain Network design multi-objective genetic algorithm M-TOPSIS

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