Minimum cost‐based design of isolated PV ‐wind hybrid system considering the PV tilt angle and wind turbine hub height as design parameters using genetic algorithm

Author(s):  
Khaled Hosny Ibrahim ◽  
Eslam Mohamed Ahmed ◽  
Saber Mohamed Saleh
2021 ◽  
Author(s):  
Diplina Paul ◽  
Abhisek Banerjee

Abstract In this article, authors have studied genetic algorithm-based optimization technique to optimize rotor profile for elliptic shaped Savonius-style wind turbine with an aim to maximize the coefficient of performance. Genetic algorithm has been used to optimize design variables having distinct values and discontinuous and nondifferentiable objective functions. Optimization procedure using genetic algorithm uses the following steps: initialization, assessment, assortment, crossover and lastly alteration. Once the genetic algorithm is initialized, then the evaluation process trails, where each parametric value is evaluated based on the fitness function stated as objective function. Then the GA operators i.e assortment, cross over and alteration are applied. At the end of GA operation procedure, a new set of values of design parameter is generated. This procedure is endlessly iterated until the convergence criteria is met. Then the optimized and non-optimized profiles are studied using numerical simulation. Initially a two-dimensional numerical model is developed and validated against experimental results. The two-dimensional analysis is conducted using k-ω shear stress transport model. Unsteady Reynold’s Averaged Navier Stoke’s equations have been solved to simulate the flow field of a Savonius-style rotor. This analysis has been executed using finite volume approach in Fluent 17.2 version. Grid independence study is performed to curtail the effect of grid size on the flow field portrayals. The optimization technique implemented on the Savonius-style wind turbine, generated design parameters that were able to yield a coefficient of performance value of 0.398. The coefficient of torque and coefficient of performance values are studied for both optimized and non-optimized profile as a function of tip speed ratio. Numerical simulation predicted a maximum gain of 41% for coefficient of performance at TSR = 1.0 over for optimized profile over the non-optimized profile.


2020 ◽  
Vol 14 ◽  
Author(s):  
Osama Bedair

Background: Modular steel buildings (MSB) are extensively used in petrochemical plants and refineries. Limited guidelines are available in the industry for analysis and design of (MSB) subject to accidental vapor cloud explosions (VCEs). Objectives: The paper presents simplified engineering model for modular steel buildings (MSB) subject to accidental vapor cloud explosions (VCEs) that are extensively used in petrochemical plants and refineries. Method: A Single degree of freedom (SDOF) dynamic model is utilized to simulate the dynamic response of primary building components. Analytical expressions are then provided to compute the dynamic load factors (DLF) for critical building elements. Recommended foundation systems are also proposed to install the modular building with minimum cost. Results: Numerical results are presented to illustrate the dynamic response of (MSB) subject to blast loading. It is shown that (DLF)=1.6 is attained at (td/t)=0.4 for front wall (W1) with (td/T)=1.25. For side walls (DLF)=1.41 and is attained at (td/t)=0.6. Conclusions: The paper presented simplified tools for analysis and design of (MSB) subject accidental vapor cloud blast explosions (VCEs). The analytical expressions can be utilized by practitioners to compute the (MSB) response and identify the design parameters. They are simple to use compared to Finite Element Analysis.


2018 ◽  
Vol 12 (3) ◽  
pp. 181-187
Author(s):  
M. Erkan Kütük ◽  
L. Canan Dülger

An optimization study with kinetostatic analysis is performed on hybrid seven-bar press mechanism. This study is based on previous studies performed on planar hybrid seven-bar linkage. Dimensional synthesis is performed, and optimum link lengths for the mechanism are found. Optimization study is performed by using genetic algorithm (GA). Genetic Algorithm Toolbox is used with Optimization Toolbox in MATLAB®. The design variables and the constraints are used during design optimization. The objective function is determined and eight precision points are used. A seven-bar linkage system with two degrees of freedom is chosen as an example. Metal stamping operation with a dwell is taken as the case study. Having completed optimization, the kinetostatic analysis is performed. All forces on the links and the crank torques are calculated on the hybrid system with the optimized link lengths


2021 ◽  
pp. 0309524X2110039
Author(s):  
Amgad Dessoky ◽  
Thorsten Lutz ◽  
Ewald Krämer

The present paper investigates the aerodynamic and aeroacoustic characteristics of the H-rotor Darrieus vertical axis wind turbine (VAWT) combined with very promising energy conversion and steering technology; a fixed guide-vanes. The main scope of the current work is to enhance the aerodynamic performance and assess the noise production accomplished with such enhancement. The studies are carried out in two phases; the first phase is a parametric 2D CFD simulation employing the unsteady Reynolds-averaged Navier-Stokes (URANS) approach to optimize the design parameters of the guide-vanes. The second phase is a 3D CFD simulation of the full turbine using a higher-order numerical scheme and a hybrid RANS/LES (DDES) method. The guide-vanes show a superior power augmentation, about 42% increase in the power coefficient at λ = 2.75, with a slightly noisy operation and completely change the signal directivity. A remarkable difference in power coefficient is observed between 2D and 3D models at the high-speed ratios stems from the 3D effect. As a result, a 3D simulation of the capped Darrieus turbine is carried out, and then a noise assessment of such configuration is assessed. The results show a 20% increase in power coefficient by using the cap, without significant change in the noise signal.


2000 ◽  
Vol 176 ◽  
pp. 135-136
Author(s):  
Toshiki Aikawa

AbstractSome pulsating post-AGB stars have been observed with an Automatic Photometry Telescope (APT) and a considerable amount of precise photometric data has been accumulated for these stars. The datasets, however, are still sparse, and this is a problem for applying nonlinear time series: for instance, modeling of attractors by the artificial neural networks (NN) to the datasets. We propose the optimization of data interpolations with the genetic algorithm (GA) and the hybrid system combined with NN. We apply this system to the Mackey–Glass equation, and attempt an analysis of the photometric data of post-AGB variables.


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