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Design of metaporous supercells by genetic algorithm for absorption optimization on a wide frequency band

APPLIED ACOUSTICS 2016年 102卷 49-54页

作者： Lagarrigue, C. Groby, J. -P. Daze, O. Tournat, V. Univ Maine LUNAM Univ CNRS LAUM UMR 6613 F-72085 Le Mans France

The optimization of acoustic absorption by metaporous materials made of complex unit cells with 2D resonant inclusions is realized using genetic algorithm. A nearly total absorption over a wide frequency band can be obtained for thin structures, even for frequencies below the quarter wavelength resonances i.e., in a sub-wavelength regime. The high absorption performances of this material are due to the interplay of usual visco-thermal losses, local resonances and trapped modes. The density of resonant and trapped modes in this dissipative porous layer, is a key parameter for broadband absorption. The best configurations and critical coupling conditions are found by genetic algorithm optimization. Several types of resonators are included gradually in the studied configurations (split-rings, Helmholtz resonators, back cavities) with increasing complexity. The optimization leads to a metaporous structure with a 2-cm sub-wavelength layer thickness, exhibiting a nearly total absorption between 1800 Hz and 7000 Hz. The influence of the incidence angle on the absorption properties is also shown. (C) 2015 Published by Elsevier Ltd.

关键词： Metamaterials Porous materials Metaporous Finite element method genetic algorithm optimization

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genetic algorithm-Based optimization Used in Rolling Schedule

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Journal of Iron and Steel Research International 2008年第2期15卷 18-22页

作者： YANG Jing-ming CHE Hai-jun DOU Fu-ping ZHOU Tao Institute of Electrical Engineering Yanshan University Qinhuangdao 006004 Hebei China

A genetic algorithm-based optimization was used for 1 370 mm tandem cold rolling schedule,in which the press rates were coded and *** superiority individual is reserved in every *** and comparison of optimized schedule with the existing schedule were *** is seen that the performance of the optimal rolling schedule is satisfactory and promising.

关键词： tandem cold rolling rolling schedule energy consumption genetic algorithm optimization

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Improved CNFET Performance Based on genetic algorithm Parameters optimization 8

Improved CNFET Performance Based on Genetic Algorithm Parame...

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8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)

作者： Sayed, Shimaa. I. Abutaleb, M. M. Nossair, Zaki B. Helwan Univ Elect Commun & Comp Engn Cairo Egypt

ISBN: (纸本)9781538633717

The performance of Carbon Nanotube Field Effect Transistors (CNFETs) depends critically on device parameters such as CNT diameter, number of nanotubes, and inter-nanotube spacing. To achieve a minimum Power-Delay Product (PDP) of the CNFET based digital design, the genetic algorithm (GA) is used in this paper to optimize CNFET device parameters. The results of GA optimization are found to be 10 carbon nanotubes with 1.33nm CNT diameter and 4nm spacing between them. The performance of CNFET digital circuits (such as XOR2, 2x1 MUX and D-latch) based on these optimum values is compared with that designed based on typical parameters. The simulation results demonstrate better performance of proposed CNFET based circuits with the significant reduction of PDP values in comparison with previous designs.

关键词： CNFET device parameters genetic algorithm optimization power-delay product minimization nanoelectronic circuit design

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Task-Offloading optimization Using a genetic algorithm in Hybrid Fog Computing for the Internet of Drones

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SENSORS 2025年第5期25卷 1383-1383页

作者： Attalah, Mohamed Amine Zaidi, Sofiane Mellal, Nacima Calafate, Carlos T. Univ Ctr Tipaza Dept Elect Tipasa 42000 Algeria Univ Oum Bouaghi Dept Math & Comp Sci Res Lab Comp Sci Complex Syst RELA CS 2 Oum El Bouaghi 04000 Algeria Univ Oum El Bouaghi Dept Networks & Telecommun Res Lab Artificial Intelligence & Autonomous Objec Oum El Bouaghi 04000 Algeria Univ Politecn Valencia Dept Comp Engn DISCA Valencia 46022 Spain

Research and development on task offloading over the Internet of Drones (IoD) has expanded rapidly in the last few years. Task offloading in a fog IoD environment is very challenging due to the high dynamics of the IoD topology, which cause intermittent connections, as well as the stringent requirements of task offloading, such as reduced delay. To overcome these challenges, in this paper, we propose a task-offloading optimization strategy using a heuristic genetic algorithm (GA) with hybrid fog computing technology for the Internet of Drones, named GA Hybrid-Fog. The proposed solution employs a GA for task offloading from edge Unmanned Aerial Vehicles (UAVs) to both fog base stations (FBSs) and fog UAVs (FUAVs) in order to optimize offloading delays (transmission and fog computing delays) and guarantee higher storage and processing capacity. Experimental results show that GA Hybrid-Fog achieves greater improvements in task-offloading delays compared to other IoD technologies (GA BS-Fog, GA UAV-Fog, and GA UAV-Edge).

关键词： Internet of Drones fog computing networks genetic algorithm optimization task offloading in IoD unmanned aerial vehicles

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Blade shape optimization of the Savonius wind turbine using a genetic algorithm

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APPLIED ENERGY 2018年 213卷 148-157页

作者： Chan, C. M. Bai, H. L. He, D. Q. Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Kowloon Hong Kong Peoples R China

The Savonius wind turbine is one of the best candidates for harvesting wind energy in an urban environment, due to unique features such as compactness, simple assembly, low noise level, self-starting ability at low wind speed, and low cost. However, the conventional Savonius wind turbine with semicircular blades has a relatively low power coefficient. This work focuses on optimizing the shape of the blade of the Savonius wind turbine to further improve its power coefficient. An evolutionary-based genetic algorithm (GA) is incorporated into computational fluid dynamics (CFD) simulations, thereby coupling blade geometry definition with mesh generation and fitness function evaluation in an iterative process. Three variable points along the blade cross-section are used to define the geometry of the blade arc, and the objective function of GA is set to maximize the power coefficient. Two-dimensional flow around the wind turbine is modeled by the shear-stress transport (SST) k-omega turbulence model and solved through the finite-volume method in ANSYS Fluent. Three GA optimization runs with different population and genetic operations have been carried out to provide the optimal shape of the blade of the Savonius turbine. Compared to the wind turbine with semicircular blades, the wind turbine with optimal blades and a tip speed ratio (TSR) of 0.8 achieved significant improvement (up to 33%) on the time-averaged power coefficient. In addition, the Savonius turbine with optimal blades outperformed the one with semicircular blades at a wide range of TSR (= 0.6-1.2), suggesting that the Savonius wind turbine with optimal blades has great potential to be applied in the real urban environment. The aerodynamic forces and flow structures pertaining to both wind turbines with optimal and semicircular blades are compared and discussed, to improve our understanding on their underlying mechanisms and to further improve their performance.

关键词： Wind energy Savonius wind turbine/rotor genetic algorithm optimization

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STUDY ON GEANT4 SIMULATION OF GRH SYSTEM AND MULTIPLE-OBJECTIVE optimization OF OPTICAL REFLECTOR SYSTEM WITH genetic algorithm

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FUSION SCIENCE AND TECHNOLOGY 2014年第3期66卷 405-413页

作者： Liu, Bin Hu, Huasi Zhang, Tiankui Guan, Xingyin Xi An Jiao Tong Univ Sch Energy & Power Engn Xian 710049 Peoples R China China Acad Engn Phys Laser Fus Res Ctr Mianyang 621900 Peoples R China Northwest Inst Nucl Technol Xian 710049 Peoples R China

Parameters of fusion reaction history play an important role in inertial confinement fusion diagnosis. Two types of detectors, named gas Cherenkov detector (GCD) and gamma reaction history (GRH), have been well applied for measurement of fusion reaction history due to their fast responses and capacities for setting the threshold. This study was carried out in two stages. First, simulation of some components of the GRH system was carried out with Geant4. Second, an optimization method by combining a genetic algorithm with the Geant4 code was established and applied to the optical reflectors of the GRH system. The optimization process was focused on 16.7-MeV gamma rays with a threshold of 12 MeV. An optimal time response of 5 ps and an efficiency at the receiving surface of 2.2661 X 10(-2) Cherenkov photons! incident 16.7-MeV gamma ray were obtained at 1.9158 atm of CO2 pressure and a temperature of 20 degrees C.

关键词： GRH system genetic algorithm optimization Geant4 simulation

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Aerodynamic optimization of the tangential stacking line of a transonic axial flow compressor rotor using genetic algorithm

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2019年第1期41卷 1-12页

作者： Asghari, M. Mirzabozorg, M. Agha Seyed Adami, M. Malek Ashtar Univ Technol Dept Mech & Aerosp Engn Esfahan Iran

In this paper, aerodynamic optimization of the tangential stacking line of the NASA Rotor 37 as a transonic axial flow compressor rotor is carried out using computational fluid dynamics and genetic algorithm. To cover a wide range of curves with a minimum number of design parameters, a B-spline curve with three control points at 33, 66 and 100% of the blade span is used to define the blade stacking lines. Firstly, by rotating the tangential position of the control points, different rotors have been created and are simulated using the Navier-Stokes governing equations. Then, using genetic algorithm operators, based on the adiabatic efficiency as an objective function, new blades are created and numerically simulated. This process is repeated to achieve maximum adiabatic efficiency. The comparison of the optimum blade and the original blade indicates that optimal tangential stacking line causes the shock wave to move downstream and reduce the secondary flow which has led to an improvement of about 1.7% of the adiabatic efficiency.

关键词： Axial flow compressor Transonic rotor genetic algorithm optimization Tangential stacking line CFD

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Characteristics optimization of the Maglev Train Hybrid Suspension System Using genetic algorithm

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2015年第3期30卷 1163-1170页

作者： Safaei, Farhad Suratgar, Amir Abolfazl Afshar, Ahmad Mirsalim, Mojtaba Amirkabir Univ Technol Dept Elect Engn Tehran 15914 Iran St Marys Univ Dept Engn San Antonio TX 78228 USA

This paper focuses on the optimal structural design of a hybrid permanent-magnet-electro-magnetic suspension system (PEMS) for a magnetic levitation (Maglev) transportation system in order to decrease the suspension power loss. First, the nonlinear magnetic force expression of a PEMS system is obtained by developing the magnetic equivalent circuit of the hybrid structure. The proposed analytical framework accounts for leakage fluxes and material properties such as iron reluctances. A number of design considerations are also presented to attain more practical results. genetic algorithm is then employed to optimize the lifting force while reducing the system power loss. Moreover, 3-D finite element method (FEM) is utilized in the analyses and it is shown that the results calculated from the proposed model match well with those obtained from FEM. In addition, superiorities of the implemented model over the existing approaches are demonstrated. The outcomes show that the proposed method has increased the magnetic force, while significantly reducing the suspension power loss compared with those in the conventional pure electromagnet structure and in a previously proposed hybrid structure.

关键词： Finite-element-method genetic algorithm optimization hybrid magnetic levitation Maglev train modeling permanent-magnet (PM) permanent-magnet-electro-magnetic

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Techno-economic optimization and social costs assessment of microgrid-conventional grid integration using genetic algorithm and Artificial Neural Networks: A case study for two US cities

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JOURNAL OF CLEANER PRODUCTION 2019年第Aug.20期229卷 552-569页

作者： Nagapurkar, Prashant Smith, Joseph D. Missouri Univ Sci & Technol Chem & Biochem Engn Dept Rolla MO 65401 USA

Through two case studies, a methodology is presented that assessed the techno-economic and environmental performance of microgrid-conventional grid integration scenarios for fifty homes located in US cities of Fargo and Phoenix. The microgrid was composed of seven components - solar photovoltaics, wind-turbines, lead acid batteries, biodiesel generators, fuel cells, electrolyzers and H-2 tanks. Firstly, mathematical models that predicted the hourly power generation were developed for every microgrid component. Secondly, Artificial Neural Networks were utilized to predict hourly electricity demand and its results were validated with actual available data. Thirdly, through an electricity dispatch strategy and a genetic algorithm optimization technique, microgrid configurations were determined that had lowest levelized cost of energy, $/kWh. From peak power standpoint, four microgrid-conventional grid integration scenarios were examined, namely, microgrid possessing penetration level of 25%, 50%, 75%, 100%. Based on the environmental life cycle assessment of power generation, three carbon taxes were imposed -$12, $48, $72/tonne carbon dioxide emitted. Microgrid's electricity cost was found to be $0.43-0.86/kWh. Imposing carbon taxes barely showed any effect on microgrid's electricity cost nor its optimum configuration, but conventional grid's electricity cost was found to increase by 7-33% as its carbon emissions were five times as that of microgrid. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Microgrid Techno-economic assessment Environmental assessment (LCA) genetic algorithm optimization Artificial neural network Carbon tax

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Performance assessment and optimization of water spray strategy for indirect evaporative cooler based on artificial neural network modeling and genetic algorithm

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APPLIED ENERGY 2024年 368卷

作者： Ma, Xiaochen Shi, Wenchao Lu, Lin Yang, Hongxing Hong Kong Polytech Univ Dept Bldg Environm & Energy Engn Renewable Energy Res Grp RERG Hong Kong Peoples R China

Indirect evaporative cooling has garnered considerable attention and research owing to its notable advantages, especially high cooling efficiency and low energy consumption. However, the complexity and diversity of parameters affecting indirect evaporative cooler (IEC) performance necessitate the development of a fast and efficient performance prediction model, which remains a critical research objective. To address the particular challenge, this paper introduces a novel approach that utilizes the principles of neural network theory to construct a predictive model based on backpropagation artificial neural networks (BP-ANN). The developed model is employed to investigate the impact of six main operating parameters of the water spray system on the IEC performance. Wet-bulb efficiency and pressure drop of secondary air channel are utilized as the performance evaluation indexes. The model is rigorously validated by relevant experimental data, enabling a comprehensive analysis of the influence of each parameter on the operational performance of the whole system. The significance of the influential parameters is evaluated through grey relational analysis. Furthermore, a multi-objective optimization approach employing a genetic algorithm (GA) is introduced to effectively determine the optimal operating parameters of the IEC system. The results demonstrate that the whole system can efficiently operate with low energy consumption when the wet-bulb efficiency and pressure drop weights are balanced. The optimal operating parameters include a nozzle pressure of 0.15 MPa, spacing of 85 mm, aperture of 6 mm, count of 11, spray density of 0.85 kg/(s center dot m2) center dot m 2 ) and air velocity of 2 m/s, at which time the wet-bulb efficiency was 0.85, the pressure drop was 210 Pa, and the coefficient of performance (COP) was 12.4, which demonstrated an efficiency increase of 23.2% in comparison with the base system. The findings of this research significantly contribute to a c

关键词： Indirect evaporative cooler Artificial neural network Performance prediction Grey relational analysis genetic algorithm optimization

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