All Title Author
Keywords Abstract

Publish in OALib Journal
ISSN: 2333-9721
APC: Only $99


Relative Articles

АНАЛ З СТРУКТУР МАТЕМАТИЧНИХ МОДЕЛЕЙ НЕСТАЦ ОНАРНИХ АЕРОДИНАМ ЧНИХ КОЕФ Ц НТ В Analysis of the structure of mathematical models of unsteady aerodynamic coefficients Анализ структур математических моделей нестационарных аэродинамических коэффициентов

Unsteady numerical estimation of the aerodynamic loads of the Krueger flap

ЗМ НА АЕРОДИНАМ ЧНИХ ХАРАКТЕРИСТИК ПРИ НЕСТАЦ ОНАРНОМУ ОБТ КАНН ПРОФ ЛЮ НА ВЕЛИКИХ КУТАХ АТАКИ Change in the unsteady aerodynamic characteristics of flow profile at high angles of attack Изменение аэродинамических характеристик при нестационарном обтекании профиля на больших углах атаки

Discrete approximation of functionals with jumps and creases

Unsteady aerodynamic forces for aeroelastic analysis of two-dimensional lifting surfaces

Higher Order Compact Finite Difference Schemes for Unsteady Boundary Layer Flow Problems

State-space model identification and feedback control of unsteady aerodynamic forces

A Coordinate Transformation for Unsteady Boundary Layer Equations

Aerodynamic Models for Hurricanes II. Model of the upper hurricane layer


Non-Linear Unsteady Aerodynamic Response Approximation Using Multi-Layer Functionals

DOI: 10.1590/S0100-73862002000100005

Keywords: nsteady aerodynamics, aeroelasticity, multi-layer functionals, neural networks, genetic algorithms.

Full-Text   Cite this paper   Add to My Lib


non-linear functional representation of the aerodynamic response provides a convenient mathematical model for motion-induced unsteady transonic aerodynamic loads response, that accounts for both complex non-linearities and time-history effects. a recent development, based on functional approximation theory, has established a novel functional form; namely, the multi-layer functional. for a large class of non-linear dynamic systems, such multi-layer functional representations can be realised via finite impulse response (fir) neural networks. identification of an appropriate fir neural network model is facilitated by means of a supervised training process in which a limited sample of system input-output data sets is presented to the temporal neural network. the present work describes a procedure for the systematic identification of parameterised neural network models of motion-induced unsteady transonic aerodynamic loads response. the training process is based on a conventional genetic algorithm to optimise the network architecture, combined with a simplified random search algorithm to update weight and bias values. application of the scheme to representative transonic aerodynamic loads response data for a bidimensional airfoil executing finite-amplitude motion in transonic flow is used to demonstrate the feasibility of the approach. the approach is shown to furnish a satisfactory generalisation property to different motion histories over a range of mach numbers in the transonic regime.


comments powered by Disqus