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

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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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 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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Feasibility of optimum energy use and cost analyses by applying artificial intelligence and genetic optimization methods in geothermal and solar energy-assisted multigeneration systems

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RENEWABLE ENERGY 2024年 237卷

作者： Yilmaz, Ceyhun Sen, Ozan Sakarya Univ Mech Engn Sakarya Turkiye Afyon Kocatepe Univ Mech Engn Afyonkarahisar Turkiye

This study evaluates the potential for optimizing energy utilization and cost analysis in geothermal and solar energy-supported multigeneration systems using artificial intelligence (AI) and genetic algorithm (GA) optimization methods. Integrating renewable energy sources aims to promote sustainable energy solutions focusing on efficiency and cost-effectiveness. A cogeneration system that leverages geothermal and solar energy for electricity generation and space cooling is analyzed. The model incorporates an organic Rankine cycle (ORC) for power generation and a geothermal-solar absorption cooling cycle for space cooling, developed using Artificial Neural Networks (ANN) and optimized through thermoeconomic methods. Thermodynamic and thermoeconomic analyses guide the optimization process using an ANN-based GA method. The system, with parameters such as a 130 degrees C geothermal source temperature, an 85 kg/s mass flow rate, and a 600 W/m2 2 monthly average solar radiation intensity in Afyonkarahisar, achieves overall energy and exergy efficiencies of 19.5% and 43.5%, respectively. optimization via the GA method significantly improves performance, increasing net power output from 2240 kW to 2760 kW and cooling capacity from 2720 kW to 3061 kW. Additionally, the cost of electricity decreases by 12.1 %, from 0.0165 $/kWh to 0.0145 $/kWh, while the cooling cost is reduced by 41.9 %, from 0.0745 $/kWh to 0.0525 $/kWh.

关键词： Artificial neural network genetic algorithm optimization Geothermal energy Power and cooling Solar energy Thermoeconomic analysis

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Shifted and Weighted LFCC Features for Hand Movements Recognition Using EEG Signals

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2025年第1期20卷 785-796页

作者： Reffad, Aicha Mebarkia, Kamel Univ Ferhat ABBAS Set 1 Fac Technol Electrotech Dept LAS Lab Setif 19000 Algeria Univ Ferhat ABBAS Set 1 Fac Technol Elect Dept LIS Lab Setif 19000 Algeria

The Brain computer interface (BCI) technology attracts many researchers due to its vital applications in medicine and biomedical domains. Decoding the motor imagery electroencephalography signals (MI-EEG) to find the corresponding brain activities constitutes the core of the BCI system. Researchers use both classical machine learning and deep learning to identify such activities. In this work, left/right hand movement is recognized using MI-EEG signals features. The EEG signal features vector is derived from the wavelet transformation (WT) and the linear frequency cepstral coefficient (LFCC) to feed an SVM classifier. Two publicly available MI-EEG datasets are tested, BCI competition III b and BCI competition IV 2b. Firstly, the classification is performed using all features without any modification for the LFCC features. Secondly, the LFCC features are shifted and weighted (SWLFCC) by genetic algorithm (GA) to improve the classification accuracy. The average classification accuracy was improved and achieved 92.19 and 87.23% for BCI competition III b and BCI competition IV 2b datasets respectively. Compared to the works using the same datasets, the performance to identify left and right hand is improved by 2.4% for the BCI competition III b. There was very small improvement for the second dataset though the deep learning was used for the last best accuracy. The novel SWLFCC features showed their efficiency in BCI system to identify MI left/right hand movement. The SWLFCC features are tuned by GA optimization to adapt any nonlinearity existing between tasks and MI-EEG signals.

关键词： Brain computer interface Motor imagery Features extraction Machine learning genetic algorithm optimization

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optimization and analysis of reliquefaction system utilizing hydrogen as refrigerant for liquid hydrogen carriers

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APPLIED ENERGY 2025年 384卷

作者： Choi, Minsoo Jung, Wongwan Ji, Sangmin Lee, Jinkwang Korea Adv Inst Sci & Technol Dept Mech Engn Daehak Ro 291 Daejeon 34141 South Korea Dong A Univ Dept Naval Architecture & Ocean Engn 37 Nakdong Daero 550 Beon Gil Busan 49315 South Korea Gyeongsang Natl Univ Dept Mech Convergence Engn 54 Charyong Ro 48beon Gil Chang Won 51931 Gyeongsangnam D South Korea

In this study, a reliquefaction system utilizing a reverse Brayton cycle with hydrogen as a refrigerant to handle boil-off hydrogen (BOH) generated within liquid hydrogen carriers operating in oceanic routes from a thermodynamic perspective is proposed. Furthermore, optimization targeting specific energy consumption (SEC) as the fitness function was performed using a genetic algorithm. Subsequently, an economic analysis was conducted at the equipment unit level for the reliquefaction system. Thermodynamic analysis of the optimized system revealed a SEC of 5.028 kWh/kg and an exergy efficiency of 44.48 %. During optimization, the circulation rates of BOH and refrigerant decreased, leading to a significant reduction in the exergy destruction within the heat exchangers and an improvement in the system efficiency. Notably, the optimization results indicated the significant influence of the heat exchange conditions in the heat exchangers on the overall system efficiency. It is also worth noting that the optimal BOH compression pressure was approximately 25 bara, which is similar to the pressure of the raw material hydrogen used as a feed gas in onshore hydrogen liquefaction systems. This suggests that the design experience gained from manufacturing equipment for onshore hydrogen liquefaction systems can provide significant benefits when producing equipment for onboard BOH reliquefaction systems. Economic analysis revealed that in the optimized system, it costs USD 1.09 to reliquefy 1 kg of BOH.

关键词： Boil-off hydrogen Reliquefaction system Process simulation genetic algorithm optimization Thermodynamic analysis

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Optimizing design of openings in vibrating plates for enhanced vibro-acoustic performance using a genetic algorithm approach

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JOURNAL OF SOUND AND VIBRATION 2025年 598卷

作者： Maamoun, Khaled Said Ahmed Wrona, Stanislaw Pawelczyk, Marek Karimi, Hamid Reza Silesian Tech Univ Dept Measurements & Control Syst Gliwice Poland Politecn Milan Dept Mech Engn Milan Italy

Optimizing structural openings in vibro-acoustic systems is essential for balancing functional needs with acoustic performance in various engineering applications. This paper presents an efficient optimization approach for designing openings in plate structures within vibro-acoustic systems, aimed at enhancing acoustic performance while maintaining structural integrity. A genetic algorithm framework is developed to determine the optimal shapes and locations of openings simultaneously, utilizing an analytical model based on hyperbolic and trigonometric admissible functions within a master-slave software architecture. The proposed method accommodates a variety of opening requirements for different systems and employs a cost function that evaluates both the average and peaks of sound power responses to identify the designs with the highest fitness. This results in significant reductions in sound power levels compared to median and worst-case designs. Additionally, the method analyzes how variations in opening locations, numbers, and shapes impact acoustic performance, providing guidance for opening designs that avoid undesired acoustic outcomes and improve noise barrier performance in vibro-acoustic systems.

关键词： Vibrating plates with openings genetic algorithm optimization Vibro-acoustic system enhancement Structural-acoustic coupling Sound power reduction

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optimization of working fluid in a novel Multi-Stream heat exchange combined cycle for liquid natural gas cold energy utilization

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APPLIED THERMAL ENGINEERING 2025年 265卷

作者： Zhao, Yiming Wang, Yingguang Han, Yu Liang, Yong Hu, Dapeng Dalian Univ Technol Dept Chem Machinery Dalian 116012 Peoples R China PipeChina Engn Technol Innovat Co Ltd Tianjin 300450 Peoples R China Dalian Ocean Univ Dept Machinery & Power Engn Dalian 116023 Peoples R China

This study investigates the impact of working fluids on the performance of Liquid Natural Gas (LNG) Cold Energy power generation cycles and identifies the optimal fluid for a multi-stream cascade combined cycle (MS-CCC). A genetic algorithm with mixed integer nonlinear programming is employed to optimize the MS-CCC, comparing it with traditional combined cycles and cascade combined cycles using both single-component and binary mixed working fluids. The candidate working fluids are innovatively selected based on normal boiling point temperatures, with a uniform distribution across the range of -128 degrees C to -2 degrees C, covering most of the LNG vaporization process. Analysis results indicate that using binary mixed working fluids effectively enhances heat transfer matching within condensers in MS-CCC and significantly improves the exergy output of the bottom cycle working fluid, thereby greatly reinforcing the advantages of MS-CCC in utilizing working fluid cold energy. With the optimal mixed working fluid, MS-CCC achieves a net power output (Wnet) of 4262.2 kW and an exergy efficiency (eta ex) of 24.3 %, marking a 32.5 % increase in eta ex compared to employing optimal single-component working fluid. The selection of the base component with a lower saturated temperature in bottom cycle significantly affects MS-CCC performance by simultaneously impacting the cold energy extraction in both cycles. Variations in mole fractions of the working fluid components also impact MS-CCC efficiency. Deviations of 30 % from the optimal mole fraction can reduce Wnet by over 23 %. These findings provide valuable insights for optimizing cold energy power generation cycles.

关键词： Cold energy power generation Multi-stream heat exchange Liquefied natural gas Working fluid genetic algorithm optimization

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Design and optimization of water drop curve baffles in high temperature PEMFC channel

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IONICS 2024年第12期30卷 8221-8232页

作者： Yang, Hao Cheng, Taihong Mao, Yi Wenzhou Univ Coll Mech & Elect Engn Wenzhou 325035 Peoples R China

The operating temperature of high-temperature proton exchange membrane fuel cells (HT-PEMFC) exceeds 120 degrees C. The strategic addition of baffles in the HT-PEMFC flow channel can facilitate the diffusion of reactant gases towards the gas diffusion layer (GDL), increase the reaction area of the catalytic layer, improve the reaction rate, and enhance overall performance. The water drops curve baffle proposed in this study exhibits a more optimal curvature profile for gas flow compared to previous baffle designs. In this study, the optimal quantity, inlet and outlet orientations, and size parameters of the water drop curve baffles were examined by combining three-dimensional simulation modeling with genetic algorithm optimization. A comparison of net power density for configurations ranging from zero to four baffles in the HT-PEMFC demonstrated that the configuration with four baffles achieved superior performance, resulting in a net power density of 0.368 W/cm2, which represents a 5.74% increase compared to the basic channel without baffles. Additionally, it was observed that reverse flow incurs an additional pressure drop of 1.12% compared to forward flow. Following optimization, with the variables set to a = 0.730123 and b = 0.317671, the net power density increased to 0.3759 W/cm2, reflecting a 2.17% improvement over the non-optimized state, thereby achieving the optimal performance level for HT-PEMFC.

关键词： High-temperature PEMFC Water drop curve baffles genetic algorithm optimization Net power density

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Optimizing thermal energy storage in rectangular unit: Exploring the impact of moving fin with phase change material

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JOURNAL OF ENERGY STORAGE 2024年 99卷

作者： Izgi, Burak Demirag, Hueseyin Zahit Yozgat Bozok Univ Dept Mech Engn TR-66200 Yozgat Turkiye

This research presents an innovative moving fin design for a latent heat storage unit, with the objective of improving thermal performance. The methodology integrates numerical simulation, a machine learning approach, and a multi-objective genetic optimization algorithm (NSGA-II). The study optimizes fin parameters, such as fin velocity (Vf), V f ), fin thickness (tf), t f ), fin length (Lf) L f ) and initial fin position (yf). y f ). The optimization process aims to maximize both power (P) P ) and stored energy per mass (Em), E m ), assigning equal importance to both criteria. The outcomes reveal a considerable rise in power with minor compromises in stored energy per mass. The incorporation of moving fins in rectangular enclosure with PCM leads to a significant power boost while preserving stored latent energy with minimal compromise. In comparison to the no-fin condition, fixed 1-fin and fixed 3-fin configurations lead to stored energy losses of 4.16 % and 11.73 %, respectively, accompanied by power increases of 20.48 % and 51.17 %, respectively. Conversely, the optimized condition for maximum power with moving fins incurs a mere 2.73 % stored energy loss but achieves a remarkable 173.92 % increase in power. The optimized design parameters for maximizing power (P) P ) include Vf f = 22.5 x 10-3 mm/s, yf f = 60 mm, tf= f = 2 mm, and Lf f = 40 mm.

关键词： Thermal energy storage genetic algorithm optimization Dynamic thermal systems

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