Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Flutter

Flutter Estimation for Low Speed Aircraft Wing Using Fully Coupled Fluid – Structure Interaction

Mauwafak Ali Tewfik; Mohammed Idris Abu-Tabikh; Hayder Sabah Abd Al-Amir

Engineering and Technology Journal, 2013, Volume 31, Issue 16, Pages 3203-3215

The aero elastic responses and the flutter condition of 3-D flexible aircraft wing
were estimated by developed fully coupled fluid-structure interaction approach. The
actual wing in this approach was represented by an equivalent plate .Equivalent
plate model (structure model) based on assumed mode method was then combined
with unsteady panel-discrete vortex method (aerodynamic model) to build relatively
simple aeroelastic model. This model could be used for estimation of flutter
condition of moderate to high aspect ratio and low sweep wings of aircraft flight at
low subsonic speeds. The obtained results from the present model are able to
prediction the flutter condition of the actual wing at different angles of attack. The
increasing in the angle of attack leads to reduce flutter speed and flutter frequency.

Control on 3-D Fixable Wing Flutter Using an Adaptive Neural Controller

Mauwafak Ali Tawfik; Mohammed Idris Abu-Tabikh; Hayder Sabah Abd Al-Amir

Engineering and Technology Journal, 2012, Volume 30, Issue 16, Pages 2858-2874

An adaptive neural controller to control on flutter in 3-D flexible wing is
proposed. The aeroelastic model was based on the coupling between structure-of the
equivalent plate (wing) and the aerodynamic model that is based on a hybrid unsteady
panel methodTime domain simulations were used to examine the dynamic aeroelastic
instabilities of the system (e.g. the onset of flutter and limit cycle oscillation). The
structure of the controller consists of two models namely modified Elman neural
network (MENN) and feedforward multi-layer Perceptron (MLP). The MENN model
is trained with off-line and on-line stages to guarantee that the outputs of the model
accurately represent the plunge motion of the wing and this neural model acts as the
identifier. The feedforward neural controller is trained off-line and adaptive weights
are implemented on-line to find the generalized control action (function of addition
lift force), which controls the plunge motion of the wing. The general back
propagation algorithm is used to learn the feedforward neural controller and the
neural identifier. The simulation results show the effectiveness of the proposed
control algorithm; this is demonstrated by the minimized tracking error to zero
approximation with very acceptable settling time.