Title :
Modeling of longitudinal unsteady aerodynamics at high angle-of-attack based on support vector machines
Author_Institution :
Coll. of Aerosp. Eng., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China
Abstract :
Accurately modeling nonlinear and unsteady aerodynamics at high attitude flight plays an important role in design of future high performance fighters. In the meanwhile, it also can improve the prediction of high angle of attack dynamics of normal aircraft configurations. Support vector machines (SVMs), known as a novel type of learning machines based on statistical learning theory and structural risk minimization (SRM) principle, can be used for handle regression problems. By denoting a set of nonlinear transformations from the complex input space to a high-dimensional feature space, SVMs can approximate the regression function by a linear regression in the feature space. Such implementation is so simple that it can be analyzed mathematically. By employing SVMs, the present work models the unsteady pitching oscillation aerodynamic data of a 1/10 scaled aircraft model. Here, the input data are established from the wind tunnel experiments at different frequencies and amplitudes. To make comparison, the artificial neural networks (ANNs) technique is also used. It turned out that SVMs can overcome the ANNs´s inherent drawback of slow training convergence speed. Consequently, SVMs demonstrate high potentials for dealing with the chosen modeling of unsteady aerodynamics.
Keywords :
aerodynamics; aircraft; approximation theory; learning (artificial intelligence); mechanical engineering computing; minimisation; oscillations; regression analysis; risk analysis; support vector machines; wind tunnels; 1/10 scaled aircraft model; SRM principle; SVM; complex input space; high angle-of-attack dynamics prediction; high attitude flight; high performance fighter design; high-dimensional feature space; learning machines; linear regression; longitudinal unsteady aerodynamics modeling; mathematical analysis; nonlinear aerodynamics modeling; nonlinear transformations; normal aircraft configurations; regression function approximation; slow training convergence speed; statistical learning theory; structural risk minimization principle; support vector machines; unsteady pitching oscillation aerodynamic data; wind tunnel experiments; Aerodynamics; Analytical models; Atmospheric modeling; Computational modeling; Data models; Support vector machines; Testing; Artificial Neural Networks (ANNs); High Angle-of-Attack; Modeling of Unsteady Aerodynamics; Support Vector Machines(SVMs);
Conference_Titel :
Natural Computation (ICNC), 2012 Eighth International Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4577-2130-4
DOI :
10.1109/ICNC.2012.6234640