Aeroelastic Flutter in the Presence of an Active Flap

2018 ◽  
Author(s):  
Tso-Kang Wang ◽  
Kourosh Shoele
Keyword(s):  
Aeroelastic Flutter ◽  
Active Flap

scholarly journals Computational analysis of two-dimensional wing aeroelastic flutter using Navier-Stokes model

2018 ◽  
Vol 9 (4) ◽  
pp. 3459-3472 ◽  
Author(s):  
Magdy Saeed Hussin ◽  
Ashraf Ghorab ◽  
Mohamed A. El-Samanoudy
Keyword(s):  
Computational Analysis ◽  
Navier Stokes ◽  
Two Dimensional ◽  
Aeroelastic Flutter

Physical mechanism of intermittency route to aeroelastic flutter

2017 ◽  
Vol 75 ◽  
pp. 9-26 ◽  
Author(s):  
J. Venkatramani ◽  
S. Krishna Kumar ◽  
Sunetra Sarkar ◽  
Sayan Gupta
Keyword(s):  
Physical Mechanism ◽  
Aeroelastic Flutter

scholarly journals Flutter speed prediction by using deep learning

2021 ◽  
Vol 13 (11) ◽  
pp. 168781402110622
Author(s):  
Yi-Ren Wang ◽  
Yi-Jyun Wang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Short Term Memory ◽  
Learning Technology ◽  
Memory Space ◽  
Flutter Speed ◽  
Aeroelastic Flutter ◽  
Speed Prediction ◽  
Computational Fluid Dynamics Cfd ◽  
Cfd Method

Deep learning technology has been widely used in various field in recent years. This study intends to use deep learning algorithms to analyze the aeroelastic phenomenon and compare the differences between Deep Neural Network (DNN) and Long Short-term Memory (LSTM) applied on the flutter speed prediction. In this present work, DNN and LSTM are used to address complex aeroelastic systems by superimposing multi-layer Artificial Neural Network. Under such an architecture, the neurons in neural network can extract features from various flight data. Instead of time-consuming high-fidelity computational fluid dynamics (CFD) method, this study uses the K method to build the aeroelastic flutter speed big data for different flight conditions. The flutter speeds for various flight conditions are predicted by the deep learning methods and verified by the K method. The detailed physical meaning of aerodynamics and aeroelasticity of the prediction results are studied. The LSTM model has a cyclic architecture, which enables it to store information and update it with the latest information at the same time. Although the training of the model is more time-consuming than DNN, this method can increase the memory space. The results of this work show that the LSTM model established in this study can provide more accurate flutter speed prediction than the DNN algorithm.

scholarly journals Hummingbird feather sounds are produced by aeroelastic flutter, not vortex-induced vibration

2013 ◽  
Vol 216 (18) ◽  
pp. 3395-3403 ◽  
Author(s):  
C. J. Clark ◽  
D. O. Elias ◽  
R. O. Prum
Keyword(s):  
Vortex Induced Vibration ◽  
Aeroelastic Flutter

Aeroelastic flutter enhancement by exploiting the combined use of shape memory alloys and nonlinear piezoelectric circuits

2017 ◽  
Vol 407 ◽  
pp. 46-62 ◽  
Author(s):  
Vagner Candido de Sousa ◽  
Tarcísio Marinelli Pereira Silva ◽  
Carlos De Marqui Junior
Keyword(s):  
Shape Memory Alloys ◽  
Shape Memory ◽  
Aeroelastic Flutter ◽  
Combined Use

Investigations on precursor measures for aeroelastic flutter

2018 ◽  
Vol 419 ◽  
pp. 318-336 ◽  
Author(s):  
J. Venkatramani ◽  
Sunetra Sarkar ◽  
Sayan Gupta
Keyword(s):  
Aeroelastic Flutter

Aeroelastic Flutter Investigation and Stability Enhancement of a Transonic Axial Compressor Rotor Using Casing Treatment

2017 ◽  
Author(s):  
Kirubakaran Purushothaman ◽  
Sankar Kumar Jeyaraman ◽  
Ajay Pratap ◽  
Kishore Prasad Deshkulkarni
Keyword(s):  
Flow Separation ◽  
Aerodynamic Force ◽  
Damping Ratio ◽  
Axial Compressor ◽  
Casing Treatment ◽  
Aerodynamic Damping ◽  
Compressor Rotor ◽  
Aeroelastic Flutter ◽  
Blade Tip ◽  
Transonic Axial Compressor

This study discusses in detail the aeroelastic flutter investigation of a transonic axial compressor rotor using computational methods. Fluid structure interaction approach is used in this method to evaluate the unsteady aerodynamic force and work done of a vibrating blade in CFD domain. Energy method and work per cycle approach is adapted for this flutter prediction. A framework has been developed to estimate the work per cycle and aerodynamic damping ratio. Based on the aerodynamic damping ratio, occurrence of flutter is estimated for different inter blade phase angles. Initially, the baseline rotor blade design was having negative aerodynamic damping at part speed conditions. The main cause for this flutter occurrence was identified as large flow separation near blade tip region due to high incidence angles. The unsteadiness in the flow was leading to aerodynamic force fluctuation matching with natural frequency of blade, resulting in excitation of the blades. Hence axially skewed slot casing treatment was implemented to reduce the flow separation at blade tip region to alleviate the onset of flutter. By this method, the stall margin and aerodynamic damping of the test compressor was improved and flutter was avoided.

scholarly journals Aeroelastic Flutter and Sliding Mode Control of Wind Turbine Blade

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Lin Chang ◽  
Yingjie Yu ◽  
Tingrui Liu
Keyword(s):  
Sliding Mode Control ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Sliding Mode ◽  
Wind Turbine Blade ◽  
Sliding Mode Controller ◽  
Control Effect ◽  
Aeroelastic System ◽  
Mode Control ◽  
Aeroelastic Flutter

Flutter is an important form of wind turbine blade failure. Based on damping analysis, synthetically considering aeroelastic vibration instability of the blade and using the parameter fitting method, the aeroelastic flutter model of the pretwisted blade is built, with the simulation and emulation of flap and lead-lag directions flutter of the 2D dangerous cross section realized. Through the construction of two controllers, modular combinatorial sliding mode controller and sliding mode controller based on LMI for parameterized design suppress blade aeroelastic flutter. The results show that a better control effect can be achieved on the premise of the design of the precise parameters of the controller: the proposed sliding mode control algorithm based on LMI can effectively act on the aeroelastic system of the blade, significantly reduce the vibration frequency, and make the aeroelastic system converge to an acceptable static difference in a short time, which proves the effectiveness of sliding mode control in suppressing high-frequency vibration under high wind speed.

Aeroelastic flutter control of a bridge using rotating mass dampers and winglets

2020 ◽  
Vol 26 (23-24) ◽  
pp. 2185-2192
Author(s):  
Kamal K Bera ◽  
Naresh K Chandiramani
Keyword(s):  
Angular Speed ◽  
Fixed Number ◽  
Control Input ◽  
Vertical Force ◽  
Flat Plates ◽  
Aeroelastic Flutter ◽  
Continuous Rotation ◽  
Rational Function Approximation ◽  
Number Of Cycles ◽  
The Time Domain

Flutter control of a bridge deck section using a combination of aerodynamic and mechanical measures, that is controllable winglets and rotating mass dampers, is considered. Deck and winglets are considered as flat plates for their aerodynamics. Self-excited wind forces are represented in the time domain using the Scanlan–Tomko model with Roger’s rational function approximation for flutter derivatives. Winglet rotation relative to the deck is the control input generated by the variable-gain output feedback controller that uses vertical and torsional displacements of the deck as measured outputs. Control using winglets enhances the critical speed to twice the uncontrolled flutter speed. Further attenuation of vertical response is obtained by using rotating mass dampers configured to provide only a resultant vertical force due to counter-rotating unbalanced masses. The rotors are driven at a constant angular speed, and start–stop criteria are applied. This generates additional vertical force on the deck that is mostly out of phase with its vertical velocity. It yields better control than the damper operated in a continuous rotation mode for a fixed number of cycles. A maximum reduction of 15% in root mean square vertical response is obtained when compared with control using winglets only.

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