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

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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genetic and hybrid algorithms for optimization of non-singular 3PRR planar parallel kinematics mechanism for machining application

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ROBOTICA 2018年第6期36卷 839-864页

作者： Rosyid, Abdur El-Khasawneh, Bashar Alazzam, Anas Khalifa Univ Sci & Technol Mech Engn Dept POB 127788 Abu Dhabi U Arab Emirates

This paper proposes a special non-symmetric topology of a 3PRR planar parallel kinematics mechanism, which naturally avoids singularity within the workspace and can be utilized for hybrid kinematics machine tools. Subsequently, single-objective and multi-objective optimizations are conducted to improve the performance. The workspace area and minimum eigenvalue, as well as the condition number of the homogenized Cartesian stiffness matrix across the workspace, have been chosen as the objectives in the optimization based on their relevance to the machining application. The single-objective optimization is conducted by using a single-objective genetic algorithm and a hybrid algorithm, whereas the multi-objective optimization is conducted by using a multi-objective genetic algorithm, a weighted sum single-objective genetic algorithm, and a weighted sum hybrid algorithm. It is shown that the single-objective optimization gives superior value in the optimized objective, while sacrificing the other objectives, whereas the multi-objective optimization compromises the improvement of all objectives by providing non-dominated values. In terms of the algorithms, it is shown that a hybrid algorithm can either verify or refine the optimal value obtained by a genetic algorithm.

关键词： genetic algorithm optimization Hybrid optimization Multi-objective optimization Parallel manipulators Manufacturing

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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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Wind Tower Vibration Controlled by a Pendulum TMD using genetic optimization: Beam Modelling 10

Wind Tower Vibration Controlled by a Pendulum TMD using Gene...

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10th International Conference on Structural Dynamics (EURODYN)

作者： Colherinhas, Gino B. Shzu, Maura A. M. Avila, Suzana M. de Morais, Marcus V. G. Univ Brasilia Campus Univ Darcy Ribeiro Brasilia DF Brazil Univ Brasilia Campus UnB Gama Gama DF Brazil

This paper models a wind turbine tower and passive pendulum TMD with finite element using the resources and capabilities of commercial software ANSYS. Although structural control of high slender towers using pendulum TMDs is widely studied in literature, it was not found yet studies modelling the pendulum TMD with ANSYS. Using a genetic algorithms (GA), this work optimize the parameters of pendulum TMD (stiffness, damping, length and mass ratio) installed on the top of a wind turbine tower. The aim of this paper is to evaluate the differences between the responses obtained through finite element and analytical 2 degree of freedom (2DOF) models. Also it is investigated the influence of the model adopted on TMD parameters optimization results. It was verified a little difference between the frequency response functions obtained through beam and analytical 2DOF models, using the same TMD parameters in both cases. (c) 2017 The Authors. Published by Elsevier Ltd.

关键词： Wind Turbine Tuned Mass Damper genetic algorithm optimization Structural Control

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Wind Tower Vibration Controlled by a Pendulum TMD using genetic optimization: Beam Modelling

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Procedia Engineering 2017年 199卷 1623-1628页

作者： Gino B. Colherinhas Maura A.M. Shzu Suzana M. Avila Marcus. V.G. de Morais Universidade de Brasília Campus Universitário Darcy Ribeiro Brasília Distrito Federal Brazil Universidade de Brasília Campus UnB Gama Gama Distrito Federal Brazil

关键词： Wind Turbine Tuned Mass Damper genetic algorithm optimization Structural Control

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Dielectric Characterization and optimization of Wide-band, Cavity-Backed Spiral Antennas

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APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL 2011年第2期26卷 123-130页

作者： Rahman, Nahid Sharma, Anjali Afsar, Mohammed Palreddy, Sandeep Cheung, Rudolf Tufts Univ High Frequency Mat Measurement & Informat Ctr Medford MA 02155 USA Microwave Engn Corp N Andover MA 01845 USA

This paper presents a novel approach to facilitate the design of wideband, cavity-backed spiral antennas. Using this approach, first, a 2-18 GHz, two-arm cavity backed Archimedean spiral antenna has been designed in FEKO. A multilayer dielectric absorber has been introduced in the cavity to facilitate unidirectional operation of the antenna. In order to incorporate the frequency-dependent complex permittivity data of the absorbing materials inserted in the cavity, precise microwave instrumentation has been used to determine these parameters experimentally. Based on this data, a genetic algorithm optimization procedure has been applied to derive the most favorable geometry of the absorbing cavity. Our results show that a design thus optimized significantly improves key performance parameters, maximizes the co-polarized gain, and minimizes the cross-polarized gain of the antenna across its operational bandwidth. We have then extended the approach to the design of a zigzagged 2-arm Archimedean spiral antenna and also a 1.5:1 ratio elliptical spiral antenna and presented the radiation characteristics here.

关键词： Axial ratio broadband absorbing materials cavity-backed spiral antenna complex permittivity measurements genetic algorithm optimization radiation patterns

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optimization: Drone-Operated Metal Detection Based on Machine Learning and PID Controller

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2022年第5期23卷 503-515页

作者： Joo, Minho Yoon, Jaehyun Junejo, Allah Rakhio Doh, Jaehyeok Def Psychol Operat Grp Seoul 04383 South Korea AID Co Gimpo Si 10077 Gyeonggi Do South Korea Gyeongsang Natl Univ Sch Mech & Mat Convergence Engn Jinju Si 52725 Gyeongsangnam D South Korea

This paper proposes a methodology to detect metals using a drone equipped with a metal detector and programmed by machine learning (ML) models. Our proposed research process could be considered a safe and efficient unmanned mine detection technology for the eventual removal of landmines. Users of this methodology can remotely control the drones without entering the minefield to detect the metal buried and to distinguish whether the metal is mine or not. To realize this idea, we have first stabilized and improved the attitude control of a drone with an attached metal detector by using the micro genetic algorithm-based optimization of proportional-integral-differential control gains. Next, for metal detection, ML models such as a support vector machine and a back-propagation neural network were trained using the annotated dataset. Finally, we have built a controlled drone equipped with a metal detector and trained ML models and experimentally validated our methodology. According to the experimental results, the present study secured the flight stability of the unmanned metal detection drones and the high detection success rate.

关键词： Back-propagation neural network genetic algorithm optimization Metal-detected drone PID control

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Effects of 10% biofuel substitution on ground level ozone formation in Bangkok, Thailand

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ATMOSPHERIC ENVIRONMENT 2009年第37期43卷 5962-5970页

作者： Milt, Austin Milano, Aaron Garivait, Savitri Kamens, Richard Univ N Carolina Inst Environm Chapel Hill NC 27599 USA King Mongkuts Univ Technol Joint Grad Sch Energy & Environm Bangkok 10140 Thailand

The Thai Government's search for alternatives to imported petroleum led to the consideration of mandating 10% biofuel blends (biodiesel and gasohol) by 2012. Concerns over the effects of biofuel combustion on ground level ozone formation in relation to their conventional counterparts need addressing. Ozone formation in Bangkok is explored using a trajectory box model. The model is compared against O-3, NO, and NO2 time concentration data from air monitoring stations operated by the Thai Pollution Control Department. Four high ozone days in 2006 were selected for modeling. Both the traditional trajectory approach and a citywide average approach were used. The model performs well with both approaches but slightly better with the citywide average. Highly uncertain and missing data are derived within realistic bounds using a genetic algorithm optimization. It was found that 10% biofuel substitution will lead to as much as a 16 ppb peak O-3 increase on these four days compared to a 48 ppb increase due to the predicted vehicle fleet size increase between 2006 and 2012. The approach also suggests that when detailed meteorological data is not available to run three dimensional airshed models, and if the air is stagnant or predominately remains over an urban area during the day, that a simple low cost trajectory analysis of O-3 formation may be applicable. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Bangkok Modeling ground level ozone Biodiesel Ethanol substitution genetic algorithm optimization

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On Neural Networks Based Electrothermal Modeling of GaN Devices

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IEEE ACCESS 2019年 7卷 94205-94214页

作者： Jarndal, Anwar Univ Sharjah Elect & Comp Engn Dept Sharjah 27272 U Arab Emirates

This paper presents an effcient artificial neural network (ANN) electrothermal modeling approach applied to GaN devices. The proposed method is based on decomposing the device nonlinearity into intrinsic trapping-induced and thermal-induced nonlinearities that can be simulated by low-order ANN models. The ANN models are then interconnected in the physics-relevant equivalent circuit to accurately simulate the transistor. genetic algorithm (GA)-based training procedure has been implemented to find optimal values for the weights of the ANN models. The modeling approach is used to develop a large-signal model for a 1-mm gate-width GaN high-electron mobility transistor (HMET). The model has been implemented in the advanced design system (ADS) and it has been validated by pulsed and continues small- and large-signal measurements. The model simulations showed a very good agreement with the measurements and verify the validity of the developed technique for dynamic electrothermal modeling of active devices.

关键词： GaN HEMT electrothermal modeling neural networks genetic algorithm optimization

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