Flutter Behavior of Highly Flexible Two- and Three-bladed Wind Turbine Rotors

With the progression of novel design, material, and manufacturing technologies, the wind energy industry has successfully produced larger and larger wind turbine rotor blades while driving down the Levelized Cost of Energy (LCOE). Though the benefits of larger turbine blades are appealing, larger blades are prone to aero-elastic instabilities due to their long, slender, highly flexible nature, and this effect is accentuated as rotors further grow in size. In addition to the trend of larger rotors, new rotor concepts are emerging including two-bladed rotors and downwind configurations. In this work, we introduce a comprehensive evaluation of flutter behavior including classical flutter, edgewise vibration, and flutter mode characteristics for two-bladed, downwind rotors. Flutter speed trends and characteristics for a series of both two- and three-bladed rotors are analyzed and compared in order to illustrate the flutter behavior of two-bladed rotors relative to more well-known flutter characteristics of three-bladed rotors. In addition, we examine the important problem of blade design to mitigate flutter and present a solution to mitigate flutter in the structural design process. A study is carried out evaluating the effect of leading edge and trailing edge reinforcement on flutter speed and hence demonstrates the ability to increase the flutter speed and satisfy structural design requirements (such as fatigue) while maintaining or even reducing blade mass.

Flutter speed prediction by using deep learning

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.

Experimental Investigation on the Effect of Angles of Attack to the Flutter Speed of a Flat Plate in Axial Flow

The application of flat plates to the field of wind harvesting requires a lot of research toward the understanding of the flutter behavior of the plates. There are shortages of articles that discuss the effect of varying the angles of attack to the flutter speed of a flat plate. This research aims to conduct a basic experimental research on the effect of relative position of a thin-flat plates to the direction of the air flow to its flutter speed. In this study, a thin-flat plate was placed in a subsonic wind tunnel to test its flutter speed. The position of the plate was varied in various angles of attack. The effect of the angles of attack to the flutter speed was observed.

Flutter Suppression of an Airfoil Using Neuro-Fuzzy Control

Flutter, a self-excited vibration of wings and control surfaces, can lead to catastrophic failure of aircraft structures. Classical methods have been applied successfully for flutter suppression and for increasing the flutter critical speed. With the demand of higher speed and more flexible aircraft, more advanced active flutter control techniques are required. In this study, a neuro-fuzzy methodology for flutter suppression of a two dimensional airfoil is explored. A MATLAB simulation environment is used for the modeling and analysis. The airfoil model is simulated according to a set of aeroelastic equations of motion. A neuro-fuzzy controller, called NEFCON, is then embedded in the airfoil model for increasing the flutter speed. NEFCON learns from the motion of the airfoil and automatically produces fuzzy rules. The simulation results show that these fuzzy rules can successfully increase the critical flutter speed. The performance of the fuzzy rules is tested with differential airfoil parameters.

Flutter Suppression of an Airfoil Using Neuro-Fuzzy Control

Flutter, a self-excited vibration of wings and control surfaces, can lead to catastrophic failure of aircraft structures. Classical methods have been applied successfully for flutter suppression and for increasing the flutter critical speed. With the demand of higher speed and more flexible aircraft, more advanced active flutter control techniques are required. In this study, a neuro-fuzzy methodology for flutter suppression of a two dimensional airfoil is explored. A MATLAB simulation environment is used for the modeling and analysis. The airfoil model is simulated according to a set of aeroelastic equations of motion. A neuro-fuzzy controller, called NEFCON, is then embedded in the airfoil model for increasing the flutter speed. NEFCON learns from the motion of the airfoil and automatically produces fuzzy rules. The simulation results show that these fuzzy rules can successfully increase the critical flutter speed. The performance of the fuzzy rules is tested with differential airfoil parameters.

The effect of folding wingtips on the worst-case gust loads of a simplified aircraft model

In recent years, the development of lighter and more efficient transport aircraft has led to an increased focus on gust load alleviation. A recent strategy is based on the use of folding wingtip devices that increase the aspect ratio and therefore improve the aircraft performance. Moreover, numerical studies have suggested such a folding wingtip solution may incorporate spring devices in order to provide additional gust load alleviation ability in flight. It has been shown that wingtip mass, stiffness connection and hinge orientation are key parameters to avoid flutter and achieve load alleviation during gusts. The objective of this work is to show the effects of aeroelastic hinged wingtip on the problem of worst-case gust prediction and the parameterization and optimization of such a model for this particular problem, that is, worst-case gust load prediction. In this article, a simplified aeroelastic model of full symmetric aircraft with rigid movable wingtips is developed. The effects of hinge position, orientation and spring stiffness are considered in order to evaluate the performance of this technique for gust load alleviation. In addition, the longitudinal flight dynamics of a rigid aircraft with an elastic wing and folding wingtips is studied. Multi-objective optimizations are performed using a genetic algorithm to exploit the optimal combinations of the wingtip parameters that minimize the gust response for the whole flight envelope while keeping flutter speed within the safety margin. Two strategies to increase flutter speed based on the modification of the wingtip parameters are presented.

Aeroelastic scaling of flying demonstrator: flutter matching

The traditional approach for the design of aeroelastically scaled models assumes that either there exists flow similarity between the full-size aircraft and the model, or that flow non-similarities have a negligible effect. However, when trying to reproduce the behavior of an airliner that flies at transonic conditions using a scaled model that flies at nearly-incompressible flow conditions, this assumption is no longer valid and both flutter speed and static aerodynamic loading are subject to large discrepancies. To address this issue, we present an optimization-based approach for wing planform design that matches the scaled flutter speeds and modes of the reference aircraft when the Mach number cannot be matched. This is achieved by minimizing the squared error between the full-size and scaled aerodynamic models. This method is validated using the Common Research Model wing at the reference aircraft Mach number. The error in flutter speed is computed using the same wing at model conditions, which are in the nearly-incompressible regime. Starting from the baseline wing, its planform is optimized to match the reference response despite different conditions, achieving a reduction of the error in the predicted flutter speed from 7.79% to 2.13%.

Improving the Efficiency of Testing Aircraft Models for Flutter Using Measurement and Information Systems in a Subsonic Wind Tunnel

The technology of testing dynamically and structurally similar aircraft models for flutter in subsonic wind tunnels using information and measurement systems (IMS) is based on collection and processing of experimental data obtained in subcritical modes. The data received feature a significant scatter, in view of which the critical flutter speed is determined with acceptable accuracy only after its statistical post-processing. In view of the need to study a number of model versions during the flutter tests, the technology involved significant time spent for wind tunnel air flow startups and for processing of experimental data. To decrease the above-mentioned time expenditures, a more efficient technology was developed, using which it becomes possible, owing to a more sophisticated IMS structure, to quickly determine the critical flutter speed with acceptable accuracy directly in the course of tests. The essence of the new technology is that it eliminates interference that occurs in the existing system by introducing data transmission equipment into the IMS structure via a wireless Wi-Fi network. In view of this feature, it becomes possible to do the following in the course of testing the model for flutter in subcritical modes: to record the model time response to the impulse force, perform its spectral analysis, and plot the amplitude spectrum. The plotted amplitude spectrum is then used to measure the fundamental harmonic component, calculate and plot the functional dependence of the quantity inverse to the amplitude of the model oscillations fundamental tone on the flow velocity using approximation and extrapolation methods. The critical flutter speed is determined with acceptable accuracy when the functional dependence graph crosses zero. It is shown that the use of the proposed technology in flutter tests makes it possible to shorten the time taken to start the wind tunnel by a factor of 5 and the time taken to process the experimental data by a factor of 6, with the resulting error not exceeding 5%. It is recommended to use the technology in the Central Aerohydrodynamic Institute's subsonic wind tunnels in performing aircraft models flutter tests.