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Multi-objective optimization on bionic fractal structure for heat exchanging of single fluids by genetic algorithm

INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW 2024年 110卷

作者： Yao, Zikang Pan, Lisheng Mu, Bai Wei, Xiaolin Chinese Acad Sci State Key Lab High Temp Gas Dynam Inst Mech Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Engn Sci Beijing 100049 Peoples R China Beijing Univ Civil Engn & Architecture Sch Environm & Energy Engn Beijing 100044 Peoples R China

The heat transfer process using a single-fluid cooling wall is widely used in various fields, including highperformance computers, solar panels, automotive power supplies, and aerospace engine protection. In high heat flux environments, thermal management has become a significant challenge, and enhancing heat transfer is crucial for effective thermal management. Although parallel microchannel heat transfer structures are a current research focus due to their compact structure and easy manufacturing, their uneven flow distribution is a key constraint hindering further development. Bionic fractal heat transfer structures have gained widespread attention due to their favorable flow uniformity, high efficiency, and low energy consumption. Currently, the structural design of biomimetic fractal heat transfer structures mostly employs Murray's law and its derived parameters. A thermal design program suitable for variable flow rate and physical properties is proposed and a model for a single fluid biomimetic fractal heat transfer structure is established. The heat transfer characteristics of fractal heat transfer structures were explained using supercritical CO2 as a heat transfer medium. The study comprehensively analyzed the influence of different design parameters of single-fluid biomimetic fractal heat transfer structures on heat transfer performance and provided a method for optimizing their design. The results showed that there exists an optimal value for heat transfer performance indicators under different structural design parameters. genetic algorithms are applied to multi-objective optimize the structural parameters of biomimetic fractal heat transfer structures and the performance indicator is improved by 21.6%.

关键词： Bionic fractal structure Thermo-hydraulic performance Heat transfer enhancement genetic algorithm optimization Single-fluid cooling

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optimization of Magnetoplasmonic ε-Near-Zero Nanostructures Using a genetic algorithm

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SENSORS 2022年第15期22卷 5789页

作者： de Figueiredo, Felipe A. P. Moncada-Villa, Edwin Mejia-Salazar, Jorge Ricardo Inst Nacl Telecomunicacoes Inatel BR-37540000 Santa Rita Do Sapucai Brazil Univ Pedag & Tecnol Colombia Escuela Fis Ave Cent Norte 39-115 Tunja 150003 Colombia

Magnetoplasmonic permittivity-near-zero (epsilon-near-zero) nanostructures hold promise for novel highly integrated (bio)sensing devices. These platforms merge the high-resolution sensing from the magnetoplasmonic approach with the epsilon-near-zero-based light-to-plasmon coupling (instead of conventional gratings or bulky prism couplers), providing a way for sensing devices with higher miniaturization levels. However, the applications are mostly hindered by tedious and time-consuming numerical analyses, due to the lack of an analytical relation for the phase-matching condition. There is, therefore, a need to develop mechanisms that enable the exploitation of magnetoplasmonic epsilon-near-zero nanostructures' capabilities. In this work, we developed a genetic algorithm (GA) for the rapid design (in a few minutes) of magnetoplasmonic nanostructures with optimized TMOKE (transverse magneto-optical Kerr effect) signals and magnetoplasmonic sensing. Importantly, to illustrate the power and simplicity of our approach, we designed a magnetoplasmonic epsilon-near-zero sensing platform with a sensitivity higher than 56 degrees/RIU and a figure of merit in the order of 10(2). These last results, higher than any previous magnetoplasmonic s-near-zero sensing approach, were obtained by the GA intelligent program in times ranging from 2 to 5 min (using a simple inexpensive dual-core CPU computer).

关键词： genetic algorithm optimization magnetoplasmonics magneto-optics sensing TMOKE

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Multi-objective optimization on bionic fractal structure for heat exchanging of two fluids by genetic algorithm

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2023年第1期212卷

作者： Pan, Lisheng Yao, Zikang Yao, Wei Wei, Xiaolin Chinese Acad Sci Inst Mech State Key Lab High Temp Gas Dynam Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Engn Sci Beijing 100049 Peoples R China

Numerous industrial processes involve heat exchange, and the efficiency of energy usage depends heavily on the effectiveness of this exchange. Thermal structure is a critical factor that influences the process of heat transfer. The bionic fractal structure has been heavily researched due to its ability to provide a more uniform flow distribution and improved heat transfer performance. An iterative approach to determining the central wall temperature is suggested for heat transfer between two fluids with varying mass flow rates and physical features. A bionic fractal heat transfer model for two fluids was developed, and the features of the bionic fractal heat transfer structure with supercritical carbon dioxide (supercritical CO2) and H2O were expounded. Furthermore, the variation of heat transfer performance with parameters was analyzed comprehensively. A method for the optimization and design of a bionic fractal heat transfer structure using two fluids was also provided, and the index of heat transfer performance was found to have the best value for various structural factors. The multi-objective optimizations of the bionic fractal heat transfer structure were carried out by a genetic algorithm, and the results showed a 15% reduction in power consumption for the optimized heat transfer structure, a 2.4% increase in heat transfer, and an improvement in the index of heat transfer performance by 17%.(c) 2023 Elsevier Ltd. All rights reserved.

关键词： Bionic fractal structure Thermo-hydraulic performance Heat transfer enhancement genetic algorithm optimization

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Application of Digital Twin technology in high-precision assembly: structural accuracy analysis and rework optimization for large-scale equipment

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2025年第3期138卷 1229-1250页

作者： Yan, Jihong Ding, Huaqiu Liang, Hanglong Harbin Inst Technol Sch Mechatron Engn Harbin Peoples R China

With the increasing precision requirements of large and complex equipment, traditional assembly methods can no longer satisfy modern industrial production demands in terms of cost and efficiency. Balancing comprehensive assembly quality with the optimization of rework costs for structural components has become a critical challenge. To overcome this, we propose an assembly error analysis and optimization approach based on Digital Twin (DT) technology integrated with a genetic algorithm. Initially, we develop an error transfer and accumulation model (ETAM) using small displacement torsor (SDT) theory, which accounts for manufacturing errors and positional deviations. This model is further enhanced into a DT-based ETAM (DT-ETAM) by incorporating structural deformation data from finite element analysis (FEA) simulations along with actual measurement data. Building upon this framework, we introduce a comprehensive structural assembly accuracy analysis method that integrates multiple error sources. Additionally, to address assembly issues caused by significant deviations in structural features, we present an innovative optimization model that simultaneously considers assembly quality and rework costs. We employ a multifaceted integrated genetic algorithm (MIGA) to develop an optimization solution method for revising assembly feature schemes. The effectiveness of the proposed methods is demonstrated through a case study involving an actual adjustable sidewall frame (ASF). The results confirm that our approach achieves cost-effective, efficient, and high-precision structural reworking, thereby addressing the bottleneck in high-precision assembly processes.

关键词： Small displacement torsor Tolerance analysis Digital Twin technology Large-size equipment structure genetic algorithm optimization

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optimization of GFRP-concrete-steel composite column based on genetic algorithm- artificial neural network

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APPLIED OCEAN RESEARCH 2024年 143卷

作者： Zhao, Zhongwei Bao, Yuyang Gao, Tian An, Qi Liaoning Tech Univ Sch Civil Engn Fuxin 123000 Peoples R China Chongqing Jiaotong Univ State Key Lab Mt Bridge & Tunnel Engn Chongqing 400074 Peoples R China Qingdao Univ Technol Sch Civil Engn Qingdao 266520 Peoples R China

genetic algorithms (GA) and artificial neural networks (ANN) as intelligent algorithms have been widely applied in engineering fields such as structural design. The GFRP-concrete-steel composite column (GCS), known for its corrosion resistance, has garnered significant attention in its design. However, the application of GA and ANN methods in the context of GCS has primarily been confined to enhancing the predictive accuracy of ANN. This paper introduces a novel approach for the structural optimization of GCS using GA and ANN. A total of 1050 finite element models were generated and analyzed using ANSYS for the purpose of training ANN. The validity of these finite element models was confirmed through comparative analysis with experimental data. The trained ANN was then utilized to predict the loading capacity of GCS for each generation in the GA. By collecting pricing data for GFRP tubes, steel tubes, and concrete under various parameters, pricing fitting formulas for the three materials were derived. Subsequently, the cost of GCS under different parameter sets was calculated. The maximization of the ratio of GCS loading capacity to its cost (LP) was established as the optimization objective. Through the iterative optimization process of the GA-ANN algorithm, the structural optimization was achieved to maximize the economic efficiency of GCS. Comparing the optimization results with numerical simulation results showed that errors were mostly contained within 10 % or even as low as 5 %, thereby validating the accuracy of the GA-ANN algorithm. The applicability of the GA-ANN algorithm to GCS structural optimization was demonstrated by altering a single concrete variable. The method proposed in this paper can be effectively applied to the structural design and optimization of composite columns with a sandwich structure, such as GCS.

关键词： Glass fiber-reinforced plastic Compressive strength Artificial neural network genetic algorithm optimization

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optimization design of space radiation cooler based on response surface method and genetic algorithm

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CASE STUDIES IN THERMAL ENGINEERING 2023年 50卷

作者： Liu, Shuai Meng, Xu Yuan, Zhipeng Ren, Limin Chen, Liheng Chinese Acad Sci Changchun Inst Opt & Fine Mech & Phys Changchun 130033 Jilin Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

The cooling capacity of a radiator under complex environmental conditions is improved by controlling the detector temperature within a range of the thermal control index. An optimal design method for the radiation cooler based on the response surface method (RSM) and genetic algorithm is proposed, and the area and location of the radiation cooler of the space ultraviolet imager detector are optimized. First, the physical model and thermal simulation model of the space ultraviolet imager are established. Then, the detector temperature is used as the objective function, and the length, height, and inclination angle of the radiator in space are used as the design variables. The mathematical model of the objective function is established by the RSM, and the rationality of the mathematical model is verified by analysis of variance (ANOVA). Finally, the Pareto optimal frontier is obtained using the genetic algorithm. The optimization results confirm that the optimal design parameters of the radiator are XW = 165.58 mm, XH = 160.29 mm, theta 1 = 7.28 degrees, theta 2 = 5.87 degrees. Compared with before optimization, the area of the radiator is reduced by 18.18%, and the weight is reduced by 48.78 g. Based on verifying the optimization results, the optimized radiation cooler can control the detector within the target temperature of-20 to-10 degrees C. Therefore, the optimal design of the radiation cooler meets the requirement of lightweight while improving the cooling capacity of the radiator.

关键词： Space radiator cooler Space ultraviolet imager Response surface method genetic algorithm optimization

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optimization of Savonius turbine clusters using an evolutionary based genetic algorithm

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Energy Procedia 2019年 158卷 637-642页

作者： Y. Zheng H.L. Bai C.M. Chan Department of Civil and Environmental Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong KongSAR China

VAWTs benefit from constructive coupling effect that significantly improves efficiency. This work aims at optimizing the configuration of turbine clusters with three Savonius turbines. To maximize the aggregate power output of a turbine cluster, two-dimensional (2D) computational fluid dynamics (CFD) is utilized, along with an evolutionary based genetic algorithm (GA). Four scenarios are investigated considering different combinations of rotational directions. For the optimal cluster obtained, a highest 37% increase in averaged power coefficient ( C P ¯ )over an isolated Savonius turbine is achieved at Tip Speed Ratio ( TSR ) = 0.8. The substantial increase in efficiency for turbines in the optimal cluster is essentially due to constructive coupling in the flow field. Two major effective coupling mechanisms are proposed based on flow structures to explain the observed performance increase. Furthermore, a method to expand the optimal cluster into compact wind farms is proposed. It is verified that for a compact Savonis-type VAWT wind farm that consists of 12 turbines, a 30% increase in C P ¯ is obtained, showing the practicality of constructing efficient VAWT wind farms in areas with limited land.

关键词： Wind energy Vertical Axis Wind Turbines Savonius turbine genetic algorithm optimization

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Parametric optimization for Improved Tool Life and Surface Finish in Micro Turning Using genetic algorithm

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Procedia Engineering 2013年 64卷 878-887页

作者： M. Durairaj S. Gowri Sr. Asst. Professor Department of Mechanical Engineering Tagore Engineering College Chennai - 127 Director EMMRC Anna University Chennai - 25

Micro turning is a process by which micro components can be *** paper deals with CNC Micro turning of Inconel 600 alloy with titanium carbide coated tool. Machining is done in DT-110 integrated multiprocessor micro machine *** turning is carried out with full factorial experiments with various combinations of cutting parameters such as speed (25,31, and 37 m/min), feed (5,10, and 15 μm/rev) and depth of cut (30,50 and 70 μm). For every set of experiments, the output parameters such as the tool wear and the surface roughness are measured. Non-linear regression model is used to represent relationship between input and output variables and a multi-objective optimization method based genetic algorithm is used to optimize the cutting parameters in turning process such as cutting speed, feed and depth of cut. Two conflicting objectives such as tool wear and surface roughness are simultaneously optimized.

关键词： Micro Turning Surface Roughness Tool Wear Mathematical Modelling genetic algorithm optimization

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Aerodynamic Shape optimization of Wind Turbine Blades Using a Parallel genetic algorithm

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Procedia Engineering 2013年 61卷 28-31页

作者： Ozge Polat Ismail H. Tuncer Middle East Technical University 06800 Ankara TURKEY

An aerodynamic shape optimization methodology based on genetic algorithm and Blade Element Momentum theory is developed for rotor blades of horizontal axis wind turbines optimization studies are performed for the maximization of power production at a specific wind speed, rotor speed and rotor diameter. The potential flow solver with a boundary layer model, XFOIL, provides sectional aerodynamic loads. The sectional chord length, the sectional twist and the blade profiles at root, mid and tip regions of the blade are taken as design variables. The blade sections may be defined by the NACA four digit airfoil series or by arbitrary airfoil profiles defined by a Bezier curve. The sectional flow computations required by genetic algorithm, which is inherently parallel, are performed in a parallel computing environment with 512 cores. Message Passage Interface (openMPI), is employed in parallel computations. Validation studies are first performed.

关键词： Horizontal Axis Wind Turbine Aerodynamic Design genetic algorithm optimization Blade Element Momentum Theory Panel Method

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Optimum sizing and configuration of electrical system for telecommunication base stations with grid power, Li-ion battery bank, diesel generator and solar PV

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Journal of Energy Storage 2025年 123卷

作者： Silva, C.M.M.R.S. Premadasa, P.N.D. Chandima, D.P. Karunadasa, J.P. Wheeler, Patrick Department of Electrical Engineering University of Moratuwa Moratuwa10400 Sri Lanka Department of Electrical Electronic & Systems Engineering NSBM Green University Homagama Sri Lanka PEMC Research Group Faculty of Engineering University of Nottingham Nottingham United Kingdom

The rising demand for cost effective, sustainable and reliable energy solutions for telecommunication base stations indicates the importance of integration and exploring the feasibility of hybrid energy systems. This study develops a mathematical model and investigates an optimization approach for optimal sizing and deployment of solar photovoltaic (PV), battery bank storage and a diesel generator for grid connected telecommunication base station. The proposed optimum hybrid electrical system is designed to minimize total capital and operational costs while achieving 100% power availability for telecommunication equipment under varying grid power availabilities. A detailed analysis was conducted under different grid power availabilities and base station load profiles heterogeneous to different geographical locations where telecommunication base stations are located. A comprehensive analysis is conducted by genetic algorithm for different system configurations under specified constraints and specifications of electrical systems. The analysis results demonstrate the dynamic and adaptive behaviour in the levelized cost of energy against grid power availability, combinations in energy resources and cost intensity of selected resources while maintaining the levelized cost of energy of the proposed electrical system between 0.047 $/kWh to 0.060 $/kWh which is well below the considered tariff of utility grid of 0.087 $/kWh in the optimization. © 2025

关键词： Adaptive operation genetic algorithm optimization Hybrid energy system Network availability Optimal sizing Telecommunication base station

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