The shape of bridge deck sections used for long-span suspension bridges has evolved through the years, from the compact box deck girders, to twin box and multi-box decks sections, which proved to have better aerodynamic behaviour, and to bring economic advantages on the construction material usage side. This thesis presents a study of a new type of multi-box bridge deck for the Megane Bridge, consisting of two side decks for traffic lanes, and two middle decks for railway traffic, connected using stabilizing beams. Aerodynamic static force coefficient measurements were performed on a section model with a scale of 1:80, for Reynolds numbers up to 5.1 × 105 under angles of attack from -10° to 10°. Also there-dimensional CFD simulations were performed by employing a Large Eddy Simulation (LES) algorithm with a standard Smagorinsky subgrid-scale model, for Re = 9.3 × 107 and angles of attack 𝛼= -4°, -2°, 0°, 2° and 4°. The experimental and numerical results were compared with respect to accuracy, sensitivity, and practical suitability. Furthermore, the aerodynamic character for each individual decks including static coefficients, wind flow pattern and pressure distribution were studied through CFD simulation. ILS (Iterative Least Squares) method was applied to extract the flutter derivatives of Megane section model based on the results obtained from free vibration tests for evaluating the flutter stability. A comparison of the flutter derivatives was carried out between bridges with different deck configurations and the results are included in this thesis.

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There has been lots of interest in designing more fuel efficient aircraft using concepts such as boundary layer ingestion (BLI) that cause large amounts of pressure and swirl distortion that enter the jet engines. To enable ground testing the performance of these engines in different distortion patterns, the StreamVane and ScreenVane systems have been developed. A StreamVane consists of a complex vane pack that is custom designed for each distortion profile and the ScreenVane combines the StreamVane with a pressure distortion screen for testing engines under both pressure and swirl distortions. The complexity and uniqueness of these devices make predicting their structural integrity and propensity to flutter a challenge, necessitating the need for studying flutter in these complex vane packs. In order to study flutter of these complex vane packs, a methodology was created to obtain trailing edge displacements and frequencies from high speed video of a StreamVane and was used on a quad swirl StreamVane and a Simplified model. Unsteady CFD with periodic mesh deformation based off of its modal analysis was used to validate if it can predict the flutter velocity as well as understanding what the unsteady aerodynamic response to flutter is. A parameter study was then conducted along with oilflow visualization to better understand the potential causes of flutter and the impact of different design parameters. A harmonic response analysis was conducted on each of these designs and a correlation between the amplitude from the harmonic response and the flutter Mach number was obtained that can be used to predict when a StreamVane will flutter. A new series of StreamVanes were designed and based off of computational analysis, two were selected for manufacture. They both successfully avoided fluttering in flutter tests and were found to accurately replicate the goal swirl profile when measured with a 5 hole probe. These results provide a basis for understanding and predicting flutter in StreamVanes. / Master of Science / There has been lots of interest in designing more fuel efficient aircraft using concepts such as boundary layer ingestion (BLI) that cause large amounts of pressure and swirl distortion that enter the jet engines. To enable ground testing the performance of these engines in different distortion patterns, the StreamVane and ScreenVane systems have been developed. A StreamVane consists of a complex vane pack that is custom designed for each distortion profile and the ScreenVane combines the StreamVane with a pressure distortion screen for testing engines under both pressure and swirl distortions. The complexity and uniqueness of these devices make predicting their structural integrity and propensity to flutter a challenge, necessitating the need for studying flutter in these complex vane packs. Flutter is when a structure experiences excess vibration when exposed to unsteady aerodynamic loads. In order to study flutter of these complex vane packs, a methodology was created to obtain trailing edge displacements and frequencies from high speed video of a StreamVane and was used on a quad swirl StreamVane and a Simplified model. Unsteady computation fluid dynamics (CFD) with periodic mesh deformation was used to validate if it can predict the flutter velocity as well as understanding what the unsteady aerodynamic response to flutter is. A parameter study was then conducted along with oilflow visualization to better understand the potential causes of flutter and the impact of different design parameters. A harmonic response analysis, which consists of a dynamic structural analysis with sinusoidal loading applied, was conducted on each of these designs. A correlation between the amplitude from the harmonic response and the flutter Mach number was obtained that can be used to predict when a StreamVane will flutter. A new series of StreamVanes were then designed and based off of computational analyses, two were selected for manufacture. They both successfully avoided fluttering in flutter tests and were found to accurately replicate the goal swirl profile when measured with a 5 hole probe downstream of the StreamVane. These results provide a basis for understanding and predicting flutter in StreamVanes and other complex vane packs.

A robust technique for performing numerical simulations of nonlinear unsteady aeroelastic behavior is developed. The technique is applied to long-span bridges and the wing of a modern business jet. The heart of the procedure is combining the aerodynamic and structural models. The aerodynamic model is a general unsteady vortex-lattice method. The structural model for the bridges is a rigid roadbed supported by linear and torsional springs. For the aircraft wing, the structural model is a cantilever beam with rigid masses attached at various positions along the span; it was generated with the NASTRAN program. The structure, flowing air, and control devices are considered to be the elements of a single dynamic system. All the governing equations are integrated simultaneously and interactively in the time domain; a predictor-corrector method was adapted to perform this integration. For long-span bridges, the simulation predicts the onset of flutter accurately, and the numerical results strongly suggest that an actively controlled wing attached below the roadbed can easily suppress the wind-excited oscillations. The governing equations for a proposed passive system were developed. The wing structure is modelled with finite elements. The deflections are expressed as an expansion in terms of the free-vibration modes. The time-dependent coefficients are the generalized coordinates of the entire dynamic system. The concept of virtual work was extended to develop a method to transfer the aerodynamic loads to the structural nodes. Depending on the speed of the aircraft, the numerical results show damped responses to initial disturbances (although there are no viscous terms in either the aerodynamic or structural model), merging of modal frequencies, the development of limit-cycle oscillations, and the occurrence of a supercritical Hopf bifurcation leading to motion on a torus. / Ph. D.

Flutter suppression is a problem of considerable interest in modern avionics. Flutter is a vibration caused by energy in the airstream being absorbed by a non-rigid wing. Active control is one possible method of suppressing flutter. However, due to unmeasurable aerodynamic-lag states, developing an active control using full-state feedback is not viable. The use of a state-estimator is a more practical way of developing active controllers. In this paper we investigate two control methods using state-estimators. We also use simple models of disturbances to test attenuation and robustness of each control method. Finally, a method of quantitative robust analysis is reviewed and then applied to each of the controlled systems. / Master of Science

Flutter is a dynamic instability which can result in catastrophic failures of an air vehicle. Preventing flutter can be an important factor in the aircraft design, affecting the structural design. Thus, the weight and performance of the aircraft is also being affected. Understanding the role of each design factor of a wing on the onset of flutter can help designers on the flutter clearance of the aircraft. Analysis to predict flutter, ground vibration tests and flight flutter tests, which are performed to verify that the dedicated flight envelope is clear from flutter, are the most important certification processes in modern aviation. Flight flutter testing is a very expensive process. In flight flutter tests the air vehicle is instrumentated with exciters, accelerometers and transmitters to send the test data simultaneously to the ground station to be analyzed. Since flutter is a very severe instability, which develops suddenly, the data should be followed carefully by the engineers at the ground station and feedback should be provided to the pilot urgently when needed. Low test step numbers per flight, increases the cost of flutter testing. Increasing efforts in pre-flight test processes in flutter prediction may narrow the flight flutter test steps and decrease the costs. In this study, flutter prediction methods are investigated to aid the flutter test process. For incompressible flight conditions, some sample problems are solved using typical section model. Flutter solutions of a simple 3D wing are also performed via a coupled finite element linear aerodynamics approach using the commercial tool Nastran. 3D flutter solutions of the wing are compared with the typical section solutions to see how close can the typical section method predict flutter compared to the flutter analyis using the three dimensional wing model. A simulated flutter test method is introduced utilizing the two dimensional typical section method. It is shown that with a simple two dimensional typical section method, flutter test simulation can be performed successfully as long as the typical section model approximates the dynamic properties of the wing closely.

Neste trabalho é desenvolvido um sistema de controle ativo para supressão de flutter de uma asa utilizando-se sensores de pressão em pontos estratégicos de sua superfície. O flutter é um fenômeno aeroelástico que caracteriza um acoplamento instável entre estrutura flexível e escoamento aerodinâmico não estacionário. Quando a modificação da estrutura ou da aerodinâmica da asa não é viável, o uso de sistemas de controle passa a ser uma boa opção. Para o desenvolvimento do sistema de controle proposto, é primeiramente desenvolvido um modelo numérico de asa flexível. Com esse modelo numérico e a pressão na superfície da asa medida em certos pontos e realimentada ao sistema controlador, são determinadas correções no ângulo de uma superfície de controle no bordo de fuga. A tentativa de se utilizar um sistema de controle bem simples, com o uso de um único sensor de pressão, mostra a viabilidade de se implementar um sistema deste tipo em aeronaves reais. Esse sistema pode tornar-se uma alternativa aos desenvolvidos até então com o uso de acelerômetros, além de ser útil em sistemas onde se procura prever o estol e observar o comportamento da distribuição de pressão sobre a asa em vôo. / In this work, a wing flutter suppression active control system using pressure sensors in strategic points is developed. Flutter is an aeroelastic phenomenon characterized by an unstable coupling of a flexible structure and a non-stationary aerodynamic flow. When changes of the wing structure or of the aerodynamics are not viable, the use of automatic control systems becomes a good option. For the developing of the suggested control system, a numeric model of a finite flexible wing is firstly done. With this model and the pressure over the wing surface read in certain points and fedback to the control system, changes of the control surface angle on the trailing edge are determined. The attempt to use a simple control system, with a unique pressure sensor shows the viability of implanting this kind of system in real aircrafts. This system may become an alternative to those developed until now, using accelerometers. Yet, it could be useful, in systems where it is necessary to predict stall and observe the pressure load behavior over the wing in flight.

As flight flutter testing on an airplane progresses to high dynamic pressures and high Mach number region, it becomes very difficult for engineers to predict the level of the remaining stability in a flutter-prone mode and flutter-prone mechanism when response data is infested with uncertainty. Uncertainty and ensuing scatter in modal data trends always leads to diminished confidence amidst the possibility of sudden decrease in modal damping of a flutter-prone mode. Since the safety of the instrumented prototype and the crew cannot be compromised, a large number of test-points are planned, which eventually results in increased development time and associated costs. There has been a constant demand from the flight test community to improve understanding of the con-ventional methods and develop new methods that could enable ground-station engineers to make better decision with regard to flutter susceptibility of structural components on the airframe. An extensive literature survey has been done for many years to take due cognizance of the ground realities, historical developments, and the state of the art. Besides, discussion on the results of a survey carried on occurrences of flutter among general aviation airplanes has been provided at the very outset. Data for research comprises results of Computational Aero elasticity Analysis (CAA) and limited Flight Flutter Tests (FFTs) on two slightly different structural designs of the airframe of a supersonic fixed-wing airplane. Detail discussion has been provided with regard to the nature of the data, the certification requirements for an airplane to be flutter-free in the flight-envelope, and the adopted process of flight flutter testing. Four flutter-prone modes - with two modes forming a symmetric bending-pitching flutter mechanism and the other two forming an anti-symmetric bending-pitching mechanism have been identified based on the analysis of computational data. CAA and FFT raw data of these low frequency flutter modes have been provided followed by discussion on its quality and flutter susceptibility of the critical mechanisms. Certain flight-conditions, at constant altitude line and constant Mach number lines, have been chosen on the basis of availability of FFT data near the same flight conditions. Modal damping is often a highly non-linear function of airspeed and scatter in such trends of modal damping can be very misleading. Flutter margin (FM) parameter, a measure of the remaining stability in a binary flutter mechanism, exhibits smooth and gradual variation with dynamic pressure. First, this thesis brings out the established knowledge of the flutter margin method and marks the continuing knowledge-gaps, especially about the applicable form of the flutter margin prediction equation in transonic region. Further theoretical developments revealed that the coefficients of this equation are flight condition depended to a large extent and the equation should be only used in small ‘windows’ of the flight-envelope by making the real-time flutter susceptibility assessment ‘progressive’ in nature. Firstly, it is brought out that lift curve slope should not be treated as a constant while using the prediction equation at constant altitudes on an airplane capable of transonic flight. Secondly, it was realized that the effect of shift in aerodynamic canter must be considered as it causes a ‘transonic-hump’. Since the quadratic form of flutter margin prediction equation developed 47 years ago, does not provide a valid explanation in that region, a general equation has been derived. Furthermore, flight test data from only supersonic region must be used for making acceptable predictions in supersonic region. The ‘ameliorated’ flutter margin prediction equation too provides bad predictions in transonic region. This has been attributed to the non-validity of quasi-steady approximation of aerodynamic loads and other additional non-linear effects. Although the equation with effect of changing lift curve slope provides inconsistent predictions inside and near the region of transonic-hump, the errors have been acceptable in most cases. No consistent congruency was discovered to some earlier reports that FM trend is mostly parabolic in subsonic region and linear in supersonic region. It was also found that the large scatter in modal frequencies of the constituent modes can lead to scatter in flutter margin values which can render flutter margin method as ineffective as the polynomial fitting of modal damping ratios. If the modal parameters at a repeated test-point exhibit Gaussian spread, the distribution in FM is non-Gaussian but close to gamma-type. Fifteen uncertainty factors that cause scatter in modal data during FFT and factor that cause modelling error in a computational model have been enumerated. Since scatter in modal data is ineluctable, it was realized that a new predictive tool is needed in which the probable uncertainty can be incorporated proactively. Given the recent shortcomings of NASA’s flutter meter, the neural network based approach was recognized as the most suitable one. MLP neural network have been used successfully in such scenarios for function approximation through input-output mapping provided the domains of the two are remain finite. A neural network requires ample data for good learning and some relevant testing data for the evaluation of its performance. It was established that additional data can be generated by perturbing modal mass matrix in the computational model within a symmetric bound. Since FFT is essentially an experimental process, it was realized that such bound should be obtained from experimental data only, as the full effects of uncertainty factors manifest only during flight tests. The ‘validation FFT program’, a flight test procedure for establishing such bound from repeated tests at five diverse test-points in safe region has been devised after careful evaluation of guide-lines and international practice. A simple statistical methodology has been devised to calculate the bound-of-uncertainty when modal parameters from repeated tests show Gaussian distribution. Since no repeated tests were conducted on the applicable airframe, a hypothetical example with compatible data was considered to explain the procedure. Some key assumptions have been made and discussion regarding their plausibility has been provided. Since no updated computational model was made available, the next best option of causing random variation in nominal values of CAA data was exercised to generate additional data for arriving at the final form of neural network architecture and making predictions of damping ratios and FM values. The problem of progressive flutter susceptibility assessment was formulated such that the CAA data from four previous test-points were considered as input vectors and CAA data from the next test-point was the corresponding output. General heuristics for an optimal learning performance has been developed. Although, obtaining an optimal set of network parameters has been relatively easy, there was no single set of network parameters that would lead to consistently good predictions. Therefore some fine-tuning, of network parameters about the optimal set was often needed to achieve good generalization. It was found that data from the four already flown test-points tend to dominate network prediction and the availability of flight-test data from these previous test-points within the bound about nominal is absolutely important for good predictions. The performance improves when all the five test-points are closer. If above requirements were met, the predictive performance of neural network has been much more consistent in flutter margin values than in modal damping ratios. A new algorithm for training MLP network, called Particle Swarm Optimization (PSO) has also been tested. It was found that the gradient descent based algorithm is much more suitable than PSO in terms of training time, predictive performance, and real-time applicability. In summary, the main intellectual contributions of this thesis are as follows: • Realization of that the fact that secondary causes lead incidences of flutter on airplanes than primary causes. • Completion of theoretical understanding of data-based flutter margin method and flutter margin prediction equation for all ranges of flight Mach number, including the transonic region. • Vindication of the fact that including lift-curve slope in the flutter margin pre-diction equation leads to improved predictions of flutter margins in subsonic and supersonic regions and progressive flutter susceptibility assessment is the best way of reaping benefits of data-based methods. • Explanation of a plausible recommended process for evaluation of uncertainty in modal damping and flutter margin parameter. • Realization of the fact that a MLP neural network, which treats a flutter mechanism as a stochastic non-linear system, is a indeed a promising approach for real-time flutter susceptibility assessment.

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