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Performance enhancement of segmented annular thermoelectric generator based on multi-parameter and multi-objective optimization

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THERMAL SCIENCE AND ENGINEERING PROGRESS 2024年 47卷

作者： Sun, Yajing Zhai, Pengcheng Wang, Shuhao Duan, Bo Li, Guodong Chen, Gang Wuhan Univ Technol Hubei Key Lab Theory & Applicat Adv Mat Mech Wuhan 430070 Peoples R China Wuhan Univ Technol State Key Lab Adv Technol Mat Synth & Proc Wuhan 430070 Peoples R China Natl Univ Singapore Dept Mech Engn Singapore 119077 Singapore

The excellent performance of annular thermoelectric generator (ATEG) is crucial for its commercial application, and it has been validated that the segment configuration can effectively enhance the ATEG performance. Nevertheless, owing to the limitations in conventional optimization methods which fail to simultaneously consider the co-influence of multiple parameters, further exploration is required for achieving optimal design of segmented annular thermoelectric generator (SATEG). In this study, we present a novel approach combining multi-objective genetic algorithm (MOGA) with finite element method to implement the multi-parameter and multi-objective optimization of SATEG. This approach not only reduces the computational cost but also ensures precise control on SATEG temperature below its maximum acceptable operation temperature. Furthermore, the optimization results highlight the importance of emphasizing diversity between p- and n- type TE legs as well as recognizing the different optimal geometrical structures for two types of TE legs, which have been always overlooked in previous researches. By adopting the optimal geometrical structure at a hot end temperature of 600 K, significant improvements in output power of 32.839 % and efficiency of 61.915 % are achieved for SATEG.

关键词： Segmented annular thermoelectric generator multi-parameter and multi-objective optimiza-tion multi-objective genetic algorithm Segment ratio Performance enhancement

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Optimization of energy ratio, benefit to cost and greenhouses gasses using metaheuristic techniques (genetic and particular swarm algorithms) and data envelopment analysis: Recommendations for mitigation of inputs consumption (a case crop: edible onion)

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ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY 2022年第6期41卷

作者： Elhami, Behzad Raeini, Mahmoud Ghasemi Nejad Taki, Morteza Marzban, Afshin Heidarisoltanabadi, Mohsen Agr Sci & Nat Resources Univ Khuzestan Dept Agr Machinery & Mechanizat Engn Mollasani Iran AREEO Agr Engn Res Dept Isfahan Agr & Nat Resources Res & Educ Ctr Esfahan Iran

The present study aimed to optimize the energy, economic, and greenhouse effects indices for edible onion production in Fereydan (85 growers), and Buin (15 growers) regions in Isfahan. Required data were collected through direct measurement, interview, and questionnaire. Total energy input was obtained before and after optimization using data envelopment analysis (DEA), multi-objective genetic algorithm (MOGA), and multi-objective particle swarm algorithm (MOPSA), and was determined as 236,335, 213,068 (8.75% saving), 125,663 (48.63% saving), and 320,657 MJ ha(-1) (35.67%), respectively. In addition, energy ratio (ER) was improved from 0.79 to 0.84, 1.81, and 0.8 using DEA, MOGA, and MOPSA, respectively. By reducing the production costs by 10.2%, 63.12%, and 29.4% using DEA, MOGA, and MOPSA, respectively, benefit-to-cost ratio (BCR) index was improved from 1.44 to 1.88, 5.21, and 1.94, respectively. Also results showed that GHG emissions were mitigated by 18.39%, 47.56%, and 27.94%, respectively. Based on the findings, MOGA showed better performance compared to DEA, and MOPSA in terms of ER, BCR, and GHG factors. Despite applying metaheuristic, and artificial intelligence methods by MOPSA, this algorithm was not able to optimize the target factors, and applied the energy, and production costs in negative status (in reverse).

关键词： data envelopment analysis multi-objective genetic algorithm multi-objective particle swarm algorithm transplanting onion

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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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multi-objective Optimization of Underwater Towed System during CourseKeeping Motion

Multi-Objective Optimization of Underwater Towed System duri...

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International Conference on Mechanical Design and Simulation (MDS)

作者： Cheng, Yanyan Li, Yunzhou Yang, Chong Kunming Ship Borne Equipment Res & Test Ctr Res Off 1 Kunming Peoples R China

ISBN: (纸本)9781510655263;9781510655256

In this paper, the underwater towed system during the course-keeping motion was taken as the research object, in addition, the governing equation of motion was given as well. By the means of the experimental data obtained by the design of experiment, the second-order polynomial response surface models reflecting the steady-state motion characteristics of the towrope were established. Apart from mentioned above, the tail drag depth and the head tension were taken as objective functions, and the optimal solution set of Pareto was obtained by using the multi-objective optimization algorithm. The results shown that the approximate models were used to optimize the towrope when the parameters of the towrope were within a certain range, which can quickly and accurately analyzed the influences of each parameter on the steady-state motion characteristics of the towrope.

关键词： underwater towed system course-keeping motion optimal Latin hypercube design response surface method multi-objective genetic algorithm

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Optimal design of micro-dimples on crankpin bearing surface for improving engine's lubrication and friction

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INDUSTRIAL LUBRICATION AND TRIBOLOGY 2021年第1期73卷 52-59页

作者： Jiao, Renqiang Nguyen, Vanliem Le, Vanquynh Bui, Vancuong Hubei Polytech Univ Sch Mech & Elect Engn Huangshi Hubei Peoples R China Thai Nguyen Univ Technol Fac Automot & Power Machinery Engn Thai Nguyen Vietnam

Purpose The purpose of this paper is to investigate the optimal design of micro-dimples on the bearing surface of the crankpin bearing (CB) to ameliorate the engine's lubrication and friction (ELF). Design/methodology/approach A hydrodynamic model of the CB considering the influence of the asperity contact is built under the impact of the dynamic loading of the slider-crank-mechanism. The micro-dimples on non-slip surface of the bearing are designed and optimized based on the lubrication model and multi-objective genetic algorithm. The performance of optimal micro-dimples on ameliorating the ELF is analyzed and compared with that of optimal CB dimensions via the reduction of the solid contact force, friction force and friction coefficient between the crankpin and bearing surfaces;and the increase of the oil film pressure. Findings The optimal design of micro-dimples on the bearing surface may not only greatly ameliorate the ELF but also make the rotation of the crankpin inside the bearing more stable in comparison with the optimization of CB dimensions. Originality/value This study results not only clearly ameliorates the ELF but also can be applied to the slip/non-slip surface pairs of other journal bearings to enhance their lubrication performance.

关键词： Crankpin bearing Micro-dimples Slip non-slip surfaces Lubrication and friction multi-objective genetic algorithm

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Tribological performance evaluation on brake friction material by using multi-objective optimization methods

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INDUSTRIAL LUBRICATION AND TRIBOLOGY 2021年第4期73卷 599-605页

作者： Oktem, Hasan Shinde, Dinesh Kocaeli Univ Hereke Asim Vocat Sch Div Machine & Met Technol Kocaeli Turkey Gujarat Technol Univ Ahmadabad Gujarat India

Purpose The purpose of this study is to present a novel approach for the evaluation of tribological properties of brake friction materials (BFM). Design/methodology/approach In this study, a BFM was newly formulated with 16 different ingredients and produced using an industrial hot compression molding process. Experimentation was carried out on the brake tester, which was developed for this purpose according to SAE J661 standards. The braking applications, sliding speed and braking pressure were considered as performance parameters, whereas coefficient of friction (CoF) and wear rate as output parameters. The influence of the performance parameters on the output was studied using response surface plots. Analysis of variance and regression analysis was accomplished for post-experimental evaluation of results. multi-criteria decision-making (MCDM) and multi-objective genetic algorithm (MOGA) were applied for estimating the most critical performance parameter combination to evaluate the BFM. Findings The present experimental model was significant and effectively used to predict the performance. MCDM generates the optimal values for the parameters braking applications, braking pressure (Bar) and sliding speed (rpm) as 1000, 30 and 915, whereas MOGA as 1008, 10.503 and 462.8202, respectively. Originality/value An efficient model for performance evaluation of the BFM considering maximum CoF and minimum wear rate was experimentally presented and statistically verified. Also, the two multi-objective optimization methodologies were implemented and compared. A comparison between the results of MCDM and MOGA reveals that MOGA yields 30% better results than MCDM.

关键词： Optimization multi-objective genetic algorithm Coefficient of friction multi-criteria decision-making Brake friction materials Wear rate

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Simultaneous multi-objective Optimization of Semi-active Intermediate Isolation System and Building Structure

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INTERNATIONAL JOURNAL OF STEEL STRUCTURES 2021年第2期21卷 604-612页

作者： Kim, Hyun-Su Kang, Joo-Won Sunmoon Univ Div Architecture Asan South Korea Yeungnam Univ Sch Architecture Gyongsan South Korea

Recently, to reduce seismic responses, dozens of high-rise buildings in Japan and Korea have adopted the intermediate isolation system (IIS). These applications have shown successful response reduction performance for tall buildings. The important dynamic responses of building structures with the IIS are the peak intermediate isolator drift (IID), and the peak inter-story drift (ISD). The semi-active intermediate isolation system (SAIIS) was developed to more effectively reduce these seismic responses. In previous study, the authors showed that the SAIIS using magnetorheological dampers successfully reduced both the IID and the ISD. However, the optimal design of the SAIIS only was conducted, without considering the building structure. If both the SAIIS and the building structure properties are considered in the optimal design procedure, more effective optimal design for both the SAIIS and the building structure can be achieved. In this research, a simultaneous multi-objective optimization (MOO) method of the SAIIS and the building structure is proposed to achieve this. genetic algorithm was selected for simultaneous MOO of the SAIIS and the building structure. The fuzzy inference controller was selected as a control algorithm. The authors show that in comparison to the sequential optimization procedure that practical applications generally use, the simultaneous MOO method can provide much better control capacity and structural design process.

关键词： Semi-active intermediate isolation system multi-objective genetic algorithm Simultaneous structural optimization Seismic response reduction

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Smart system management and techno-environmental optimal sizing of a desalination plant powered by renewables with energy storage

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2021年第5期45卷 7501-7520页

作者： Affi, Safa Cherif, Habib Belhadj, Jamel Univ Tunis ENSIT Tunis Tunisia Univ Tunis El Manar Lab Syst Elect ENIT Tunis Tunisia

The present study assessed the performances of a brackish water reverse osmosis desalination unit powered by (PV/Wind/Battery) system with hydraulic storage. The hybrid renewable energy system and the reverse osmosis desalination plant were modeled for a specific site in southern Tunisia using real meteorological and water consumption data for 1 year. A total of 8760 simulations were treated under a dynamic simulator coupled to a smart energy management strategy to reach the hourly water load demand. Although the recognized reliability of the hybrid renewable sources compared to one- power source, their design may still critical. Therefore, in this paper, the authors suggest applying the methodology of optimizing the presented unit using an integrated systemic meta-modeling which is built through the design of experiments tool and defined by an equation involving the decision variables of the optimization as its parameters. This equation outlines the objects of the optimum sizing. The selected optimal design of the proposed unit is predicted by the genetic algorithm NSGA-II based on minimizing two objectives in conflict: Embodied energy and hydraulic loss of power supply. The CPU time of the NSGA-II optimization via the meta-model reached only 15 minutes of treatment (with one day to build the design of experiments) compared to the classic optimization via dynamic simulation. The results of the optimum sizing show that it is possible to consider a low water shortage rate (0% <= LPSP-H < 5%) for a marginal variation of embodied energy rate which does not exceed (7%).

关键词： design of experiments hybrid renewable energy meta-model multi-objective genetic algorithm optimization water desalination water-energy nexus

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Sustainable Water Optimization Tool (SUWO): An Optimization Framework for the Water-Energy-Food-Ecosystem Nexus

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WATER 2025年第9期17卷 1280-1280页

作者： Yaykiran, Salim Ekdal, Alpaslan Istanbul Tech Univ Environm Engn Dept TR-34469 Maslak Turkiye

Sustainable water management requires integrated approaches balancing competing demands and environmental sustainability. This study introduces the Sustainable Water Optimization Tool (SUWO), an open-source, Python-based simulation-optimization framework for basin-scale surface-water-resources management. SUWO employs the water-energy-food-ecosystem (WEF-E) nexus approach, utilizing a multi-objective genetic algorithm (MOGA) to generate Pareto-optimal solutions and facilitate a trade-off analysis among water uses through simulations of reservoir operations, hydro-energy production, irrigation, and flow regulation. SUWO integrates scenario analysis with multi-criteria decision making (MCDM), enabling the evaluation of various management, climate, and environmental scenarios. The framework was applied to the Sakarya River Basin (SRB) in T & uuml;rkiye, a rapidly developing region pressured by water infrastructure development, hydroelectric power plants (HEPPs), and irrigation expansion. The SUWO-SRB model showed that while Non-dominated Sorting genetic algorithm II (NSGA-II) generally exhibited superior performance, NSGA-III presented a competitive alternative. The optimization results were analyzed across four management scenarios under varying hydrological conditions and environmental management classes (EMCs) for the near future. The model results highlight WEF-E nexus trade-offs. Maximizing energy production often impacts irrigation and the ecosystem, while prioritizing sustainable irrigation can reduce energy output. Dry conditions reduce hydropower and irrigation capacity, emphasizing water scarcity vulnerabilities. Ecological deviation negatively correlates with anthropogenic factors.

关键词： water resources management multi-objective optimization multi-objective genetic algorithm WEF-E nexus trade-off analysis environmental flows reservoir operation irrigation Sakarya Basin Python

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Optimization of inlet and outlet parameters of circular recirculating aquaculture tank based on CFD-ML-MOGA

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JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING 2025年第3期13卷

作者： Zhang, Guangpeng Yu, Guanghui Li, Daoliang Wang, Yang China Agr Univ Natl Innovat Ctr Digital Fishery Beijing 100083 Peoples R China Minist Agr & Rural Affairs Key Lab Smart Farming Technol Aquat Anim & Livesto Beijing 100083 Peoples R China China Agr Univ Beijing Engn & Technol Res Ctr Internet Things Agr Beijing 100083 Peoples R China China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China State Key Lab Efficient Utilizat Agr Water Resourc Beijing 100083 Peoples R China

Recirculating aquaculture system (RAS) is a highly efficient freshwater aquaculture model that can improve aquaculture efficiency, conserve resources, and reduce wastewater discharge. The flow rate in the aquaculture water body is the main factor affecting the aquaculture environment, and maintaining its stability can significantly improve the growth efficiency of fish fry in recirculating aquaculture tanks. Combining computational fluid dynamics (CFD) with machine learning (ML) and multi-objective genetic algorithm (MOGA) is an effective, low-cost, and implementable optimization method for aquaculture tank parameters, but most of the studies have been studied for octagonal tank. Therefore, we took circular recirculating aquaculture tank as the study object, constructed CFD simulation models, used pearson correlation analysis to assess the degree of parameter influence, built ANN models for mean flow rate and standard deviation (STD) of the flow rate, and optimized the parameter combinations using the non-dominated sorting genetic algorithm-II (NSGA-II) algorithm. The results show that the three parameters that have a greater influence on the mean velocity and STD of velocity are the inlet velocity, inlet diameter and inlet angle. The optimal parameter combinations are an inlet velocity of 0.91 m/s, an inlet diameter of 9.4 mm, and an inlet angle of 30.7 degrees, which increases the mean flow rate by 75 % and reduces the STD of the flow rate by 2 % relative to the baseline model. Finally, combining CFD with ML and MOGA is important in water homogeneity improvement and energy saving in recirculating aquaculture system.

关键词： Computational fluid dynamics Circular recirculating aquaculture tank multi-objective genetic algorithm Machine learning Inlet and outlet parameters

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