OPTIMASI CL/CD MAKSIMUM PADA PERANCANGAN AIRFOIL BERBASIS ARTIFICIAL NEURAL NETWORK DENGAN BASED GRADIENT METHOD DAN NONLINEAR LEAST SQUARES

SUBEKTI, Suhud and Haryanto, Ismoyo (2011) OPTIMASI CL/CD MAKSIMUM PADA PERANCANGAN AIRFOIL BERBASIS ARTIFICIAL NEURAL NETWORK DENGAN BASED GRADIENT METHOD DAN NONLINEAR LEAST SQUARES. Undergraduate thesis, Mechanical Engineering Departement of Engineering Faculty, Diponegoro University.

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Official URL: http://perpus.mesin.undip.ac.id/

Abstract

The Most of the power plants using fossil energy, while reserves of fossil resources are decreasing. This situation is imposing to develop the use of renewable energy sources which are environmental friendly. One of those sources is wind energy. In implementation of wind energy can be realized by using wind turbines. Hence the selection of the proper airfoil is most important aspects of a wind turbine blade design. For this purpose the airfoil which has maximum lift to drag ratio (CL/CD) has to be choosen. In this design method, first the training of ANN (artificial neural network) that will be used as the optimization process to get the airfoil geometry was conducted. Optimization methods which are used in this optimization process are based-gradient method and nonlinear least squares. Then the central point from the results of the optimization, is applied in the transformation of complex variables (transformation Joukowski) to generate the airfoil geometry. Followed by CFD (Computational Fluid Dynamics) to obtain coefficients of lift and drag maximum numerically, using a variation of several angles of attack. From the CFD process the value of CL/CD are obtained. Comparing of maximum CL/CD values from optimization and CFD analysis showed that the maximum CL/CD values at optimization process, using based-gradient method and nonlinear least squares are 31.10802 and 22.6107 respectively. Meanwhile from CFD analysis those values are 29.47179 and 30.03736 respectively. It can be concluded that maximum CL/CD from based-gradient method is better.

Item Type:Thesis (Undergraduate)
Subjects:T Technology > TJ Mechanical engineering and machinery
Divisions:Faculty of Engineering > Department of Mechanical Engineering
Faculty of Engineering > Department of Mechanical Engineering
ID Code:28941
Deposited By:Mrs yuni nurjanah
Deposited On:05 Aug 2011 10:39
Last Modified:05 Aug 2011 10:39

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