Flutter Analysis of an F-16A/B in Heavy Store Configuration

<div class="htmlview paragraph">A method is presented which permits determination of of propulsive efficiency and airplane drag terms from steady level flight speed-power data. The method is based on a computer formulation of Lock's equivalent propeller model which makes it suitable for use in parameter identification. The results are in the form of coefficients of polynomials defining the airplane and the propeller. These coefficients, determined from steady flight data, then permit easy calculation of propeller characteristics and airplane performance for other flight conditions. The method is particularly applicable to performance prediction and to simulation because the airplane and propeller are represented by a simple analytical model rather than by tables or graphs.</div> <div class="htmlview paragraph">The method is described, computer formulation of the propeller model is verified with wind tunnel data, airplane and propeller characteristics are determined from actual flight test data, and predicted power is compared with measured power for a given flight test condition.</div>

The paper provides results from a joint study of various transfer/alignment (T/A) techniques including velocity matching, integrated velocity matching, and doubly integrated velocity matching approaches. The goal is to determine how the various algorithms perform when driven with actual flight test data. The study addresses key T/A Kalman filter issues such as the effect of wing flexure vibration on instrument error estimation and rapid alignment concepts. A description of how a T/A Kalman filter is implemented and used in inertially guided weapons is included. The data used in the study was acquired from the Air Force Operational Concept Demonstration (OCD) Program. The OCD effort was performed by the Air-To-Surface Weapons System Program Office (SPO) at Eglin AFB in late 1992 and early 1993. The Eglin SPO initiated the flight test program to demonstrate the effectiveness of GPS/INS integration in a tactical weapon. The test was conducted using a Honeywell Integrated Flight Management Unit (IFMU) and an Interstate Electronics 5-channel P-code GPS receiver in a GBU-15 airframe on board a block 40 F-16 aircraft. Telemetry data from this OCD weapon was transmitted continuously from the beginning of weapon alignment to final impact on the ground. The OCD telemetry stream contained INS and GPS data from both the aircraft and weapon during captive carriage T/A maneuvers. After launch, the weapon data alone was transmitted during the free flight to the target. These messages provide the data set used in the T/A study. >

During a flight test program, certain instrumented parameters are subject to degradation due to wear and tear. Over the course of a flight test program, data from several flights will be lost due to undetected instrumentation faults. The application of artificial neural networks as flight test data estimators has been proposed with the intention of reducing the aforementioned cost and wasted test flights. Several network topologies have been studied. A simulation program has been used to identify the appropriate network topology for this task. It is shown that a single network is not capable of predicting all of the correct values of the suspect parameters based solely on the information received from the reliable instruments. However, it is shown that a collection of smaller networks can succeed in this task, with each network predicting one suspect parameter. Limited training and test results based on simulation-generated data are presented. Actual flight test data from a typical business jet have been used to verify this concept and these results are presented as well. It is demonstrated that in most cases these networks are capable of predicting the measured parameter outputs with sufficient accuracy to enable identification of instrumentation system degradation.

This paper proposes a new Linear Fractional Transformation (LFT) modeling approach for uncertain Linear Parameter Varying (LPV) multibody systems with parameter-dependent equilibrium. Traditional multibody approaches, which consist in building the nonlinear model of the whole structure and linearizing it around equilibrium after a numerical trimming, do not allow to isolate parametric variations with the LFT form. Although additional techniques, such as polynomial fitting or symbolic linearization, can provide an LFT model, they may be time-consuming or miss worst-case configurations. The proposed approach relies on the trimming and linearization of the equations at the substructure level, before assembly of the multibody structure, which allows to only perform operations that preserve the LFT form throughout the linearization process. Since the physical origin of the parameters is retained, the linearized LFT-LPV model of the structure exactly covers all plants, in a single parametric model, without introducing conservatism or fitting errors. An application to the LFT-LPV modeling of a robotic arm is proposed; in its nominal configuration, the model obtained with the proposed approach matches the model provided by the software Simscape Multibody, but it is enhanced with parametric variations with the LFT form; a robust LPV synthesis is performed using Matlab robust control toolbox to illustrate the capacity of the proposed approach for control design.

The flight envelope states the conditions where an aircraft is safe to fly and those conditions must be carefully determined for every configuration. That is why, even today, the numerical tools have become more and more precise, flight test is the only reliable method to define the flight envelope, especially in the conditions where the numerical model reaches a great degree of complexity (non-linearities). However, flight test is expensive and different procedures must arise in order to match reliability (safety) and cost (flight hours). This dilemma is even worse when free-flutter conditions must be provided for a new aircraft or a new external configuration, because the envelope expansion is done by increasing the flight test hours. The present article shows a procedure to predict the flutter speed based on real-time tuning of a quasi non-linear aeroelastic model. A two-dimensional non-linear (freeplay) aeroeslastic model is implemented in MatLab/Simulink with incompressible aerodynamic conditions. A comparison with real compressible conditions is provided. Once the numerical validation is accomplished, a parametric aeroelastic model is built in order to describe the proposed procedure and contribute to reduce the number of flight hours needed to expand the flutter envelope.

This paper describes a flutter boundary prediction of a two-dimensional smart wing being in process of structural adaptation, based on non-steady-state wing response data. As for smart wings, aeroelastic safety might be endangered because of unexpected increase or decrease in flexibility or a change in structural stiffness during on-going adaptation in a wind-tunnel or flight test. We perform, therefore, a simulation analysis on estimation of aeroelastic characteristics and flutter boundary of the wing during the on-going structural change while the smart wing flies at a fixed speed. The structural adaptation is represented by continuous systematic change in the natural frequency of the heaving (or torsional) motion of the wing. The aeroelastic characteristics of the wing and its flutter margin are evaluated by a recursive maximum likelihood estimation method for an iterative identification of parameters of a nonsteady system. Numerical results show that the flutter boundary prediction based on the flutter margin of discrete-time systems which the authors proposed originally for fixed wings is very effective in predicting the flutter boundary of the morphing wing in the process of adaptation. of airplane’s structure during its flight. Safety against flutter for a newly developed airplane must finally be guaranteed by carrying out a series of flight tests. In a conventional airplane the flight speed is the most visible variable to the instability. The higher the flight speed is, the more critical the stability becomes. This fact will be true for a morphing airplane with smart wings if it flies keeping the mechanical and geometrical properties of the wings fixed like conventional airplanes. A morphing airplane is essentially more flexible than a conventional airplane. Particularly, during the process of structural morphing or adaptation of the wing, its main structure and supporting systems for morphing would become movable and less rigid, so that the structural morphing might lead to coalescence of the frequencies of aeroelastic modes of the wings. One may foresee some possibility that the airplane flying even at a constant speed will encounter aeroelastic instability, for instance, caused by unexpected effects of a mechanical system for adaptation newly installed. It is very important, therefore, to develop a flutter prediction method which is applicable to such new situations emerging in the process of adaptation during wind tunnel and flight tests.

T HE importance of mode shapes is undeniable in contemporary flutter analyses. Not only are modes used to transform the governing equations from spatial to modal coordinates, but they are also used to transform flutter solution eigenvectors back to physical space to facilitate viewing of the critical flutter mechanism components. Mode shapes have even been used to gain insight into nonlinear aeroelastic behavior, such as limit cycle oscillations [1]. Mode shapes are so commonplace and easy to generate that their ability to overcome weaknesses inherent in flutter solution methods is not appreciated. In essence, modes are powerful tools that are not consistently exploited to their full potential. One cause for the underutilization of mode shapes could be the difficulty in quantitatively comparing them in an efficient manner with other modes. Quantitative comparison of modal frequencies is convenient, but a subjective assessment of the deflection mode shapes is the convention. Because of this subjectivity, the results of modal comparisons in this fashion could be regarded as less reliable or of limited utility. The situation is further impaired for complex or large models that are often cumbersome and sometimes obscure key shape features of interest. Anyone who has evaluated large quantities of flutter analyses is aware of the benefits of being able to compare mode shapes of different dynamical systems. Thesemode shapes can be indicative of energy-extraction and damping pathways within the structural system. They can provide insight into the similarity of predicted solution results, thereby reducing the number of critical analysis cases that may require flutter flight testing. They can also aid in the identification of critical flutter modes during flight testing, which is necessary for damping determination [2] and flutter margin [3,4] methods. Many methods are available to identify modes, but methods to explicitly compare one mode shape with another are not well documented in the literature. Themethods that are available formode shape comparisons are predominantly visual-based, such as 3-D still plots, animations, and node lines, although some work to extend these methods to quantitative approaches is available. Of particular interest to the flutter engineer is Maxwell’s work (as reported in [5]) to depict the wingtip deformation characteristics of the flutter mode shape and Northington’s [6] basis vector approach. There are also strain energy methods [7]. In short, it appears that many in-house methods for mode shape comparison likely exist, but they are only passed from generation to generation internally and are not disseminated widely to the engineering public. The development and dissemination of concise and distinctive mode shape description methodologies would allow the flutter engineering community to take better advantage of the inherent utility of mode shapes. Some applications that would become more practicable are sorting, tracking, and screening of modes for large quantities of analyses and using modes in alternative solution methodologies, such as artificial neural networks [8]. This Note describes a simple method in which curve-fit equations are used to depict the mode shapes of the wing. Some examples will be presented that compare the conventional 3-D still plots of the mode shape to the fitted curves as well as the coefficients of the curve-fit equations.

This paper proposes the structured singular value (/spl mu/) approach to the problem of designing an output voltage regulator for a buck-boost power converter with current mode control. This approach allows a quantitative description of the effects of reactive component tolerances and operating point variations, which strongly affect the power converter dynamics. At first, a suitable linear power converter model is derived, whose parameter variations are described in terms of perturbations of the linear fractional transformation (LFT) class. Then, /spl mu/-analysis is used to evaluate the robustness of a conventional PI voltage regulator with respect to the modeled perturbations. Finally, the approximated /spl mu/-synthesis procedure, known as D-K iteration, is used to design a regulator ensuring robust performance. Simulated results are presented, describing the small and large signal behaviour of a reduced-order approximation of the /spl mu/-synthesised controller.

In this paper, we demonstrate the application of two analysis tools, the structured singular value μ and the v-gap metric, to the problem of evaluating the robustness properties of a multivariable integrated flight and propulsion control (IFPC) system for an experimental vertical/short takeoff and landing (V/STOL) aircraft configuration. Variations in aircraft parameters and dynamics over the V/STOL flight envelope are represented as linear fractional transformation (LFT) based parametric uncertainty descriptions, using a simple `black-box' type modelling approach. Tight bounds on μ for the resulting high-order real parametric uncertainty model are computed by introducing small amounts of additional complex uncertainty, in order to analyse the stability robustness properties of the IFPC system. Analysis using the v-gap metric is also shown to provide much useful information about the robustness of the controller over the V/STOL flight envelope.

789. Instability of refractory metal thermocouples: B E Walker et al, US Govt Res Develop Repts, 40 (21), 5 NOV 1965, AD-621 484, 18 pages

The authors describe an instrumentation system, the flight path reconstruction models and the aerodynamic model identification results obtained for a Cessna Citation II jet aircraft, based on actual flight test data. The CL(a) curve obtained for quasi-static environments is compared with a CFD solution that was computed using non-viscous Euler equations for a three-dimensional model of the aircraft, 1 with deviations of 2% and 6% observed for the CLa and CL0 coefficients, respectively. For the dynamic case, the same aerodynamic model structure is studied using two different identification methods: the Output Error Method and the Two Step Method. In both cases only the aircraft longitudinal dynamics are considered. A proof of match performed on a consolidated aerodynamic model showed good prediction capability.

This paper consists of six sections of original contributions to the theory of cumulative fatigue damage of airframe structures and a section which consists of extensive bibliography of important papers in cumulative fatigue damage. Classical models for predicting the average number of cycles-to-failure, using basically one form or another of the so-called Miner’s rule, are known to be inadequate, in the sense that they either underestimate or overestimate, in some cases, the average number of cycles-to-failure. This is because they do not take into consideration, or even do not know how to take into consideration, the various co-occurring or concomitant factors, other than the various types of loads themselves, and incorporate them into the formula for predicting the expected number of cycles-to-failure. We believe that, in this paper we have suggested methods to circumvent the drawbacks from which the classical Miner’s rule suffers, and we have also offered models, in the more difficult and realistic situation, in which the additional increments in the propagation of the dominant crack form a dependent sequence of chance variables. It is the fond hope of the authors that they and other workers in the field will have the opportunity of applying the realistic approach to predicting the average number of cycles of a large airframe, based on actual flight test data, and to compare its performance with that of methods advanced in a follow-up effort.

A large number of estimation methods can be used to compute aerodynamic coefficients of ballistically flown spinning vehicles. These estimation schemes often ignore possible phase shifts between measured data and the math model of the motion. Some of these problems occur because onboard instrumentation such as rate gyros or accelerometers behave like spring mass systems. If the vehicle motion is near the resonance frequency of the instrument, the output of the instrument is shifted in phase from the actual motion and probably the model representation of the motion. In order to demonstrate this problem, the aerodynamic coefficients of a spinning vehicle are computed from actual flight test data using a standard least-squares estimation scheme. Comparisons of observed and computed measurements of the vehicle motion are presented and the problem just described is shown to exist for these data. A modeling scheme is derived to compensate for the instrumentation induced changes in the data. The results obtained using the instrumentation modeling scheme are shown to be much better than those obtained using a standard scheme.

This paper examines the use of receding horizon control technique to stabilize a 6 degree-of-freedom model of a tri-turbofan remote controlled indoor airship. The lateral-directional dynamics of the airship are simulated using a linear parameter varying force model that has been verified against actual flight test data. Due to the varying dynamics of the airship as it transitions between different flight modes, the problem of developing robust controllers due to model uncertainty becomes challenging. A receding horizon control implementation formulated as a linear matrix inequality optimization problem is implemented in order to introduce robustness to the changing dynamics of the airship model.

New aerodynamic functions and variables have been developed to facilitate the modeling of the nonlinear aerodynamic phenomena that pervade during spinning or other complex motions of aircraft. The new methodology is intended to compensate for the difficulty that conventional estimation models have in accounting for the effects of large spatial variations of the flowfield or large excursions away from the steady state. The functions have been deduced from aerodynamic theory and the variables are determined primarily by the translational and rotational velocity vectors and some basic aircraft geometry. In general, the variables remain smoother over the course of a spin than the velocity components from which they are partially derived and that are often used in conventional estimation models. The model makes some use of static wind-tunnel data, however, parameter estimation techniques using actual flight test data must be applied to complete the model. The model is intended for simulation and controls applications.