Chuck Harris

Aeroelastic Dynamic Analysis of a Full F-16 Configuration for Various Flight Conditions

An overview is given of recent advances in a three-field methodology for modeling and solving nonlinear fluid-structure interaction problems, and its application to the prediction of the aeroelastic frequencies and damping coefficients of a full F-16 configuration in various subsonic, transonic, and supersonic airstreams is reported. In this three-field methodology the flow is described by the arbitrary Lagrangian-Eulerian form of the Euler equations, the structure is represented by a detailed finite element model, and the fluid mesh is unstructured, dynamic, and updated by a robust torsional spring analogy method. Simulation results are presented for stabilized, accelerated, low-g, and high-g flight conditions, and correlated with flight-test data. Consequently, the practical feasibility and potential of the described computational-fluid-dynamics-based computational method for the flutter analysis of high-performance aircraft, particularly in the transonic regime, are discussed.

Incorporation of Feedback Control into a High-Fidelity Aeroservoelastic Fighter Aircraft Model

Flight testing for aeroservoelastic clearance is an expensive and time consuming process. Large degree-of-freedom high-fidelity nonlinear aircraft models using computational fluid dynamics coupled with finite element models can be used for accurately predicting aeroelastic phenomena in all flight regimes including subsonic, supersonic, and transonic. With the incorporation of an active feedback control system, these high-fidelity models can be used to reduce the flight-test time needed for aeroservoelastic clearance. Accurate computational fluid dynamics/finite element models are computationally complex, rendering their runtime ill suited for adequate flight control system design. In this work, a complex, large-degree-of-freedom, transonic, inviscid computational fluid dynamics/finite element model of a fighter aircraft is fitted with a flight control system for aeroelastic oscillation reduction. A linear reduced-order model of the complete aeroelastic aircraft dynamic system is produced directly from the high-order nonlinear computational fluid dynamics/finite element model. This rapid runtime reduced-order model is used for the design of the flight control system, which includes models of the actuators and common nonlinearities in the form of rate limiting and saturation. The oscillation reduction controller is successfully demonstrated via a simulated flight test using the high-fidelity nonlinear computational fluid dynamics/finite element/flight control system model.

System Identification and Modal Extraction from Response Data

Aeroelastic stability or flutter testing is a critical part of the development of any new aircraft system, or configuration expansion. Each new aircraft and configuration needs to be assessed to ensure that it is free of any excessive and possibly destructive aeroelastic interactions. Such information could be determined from an appropriate model, but this information and insight is only as good as the model it was derived from. As such, there is a need to have a toolset that will identify necessary aeroelastic modal frequencies, damping ratios and mode shapes directly from flight test data and also compare this information against a known model. An additional complicating factor is that the driving excitation inputs to the aircraft are not always completely known, hence the identification techniques need to be capable of operating using only output sensor data. To meet these challenges, three identification methods that leverage both time and frequency domains are presented here and a variety of comparison metrics are presented to evaluate the relative accuracy of the identification approaches. Additionally, a real-time demonstration of many of these techniques was also conducted to validate the capabilities of the identification methods and comparison metrics.

Use of In-Flight Simulation to Create a Flying Qualities Database

Even with the ever expanding capabilities of ground-based simulators, there is no substitute for flight test, particularly when evaluating aircraft flying qualities. For decades now, in-flight simulators have been used for just this reason. In fact, most of the requirements found in the current version of the military fixed wing aircraft flying qualities specification were derived from data collected on just two airplanes – the variable stability NT-33A and the Total In-Flight Simulator (TIFS). TIFS is still in operation, but the NT-33A has been retired to the Air Force Museum at Wright-Patterson AFB for nearly a decade. Today, the fleet of Learjet in-flight simulators operated by Calspan Corporation is routinely used to provide access to the advantages associated with actual flight. As part of an ongoing research program for the Air Force, Systems Technology, Inc. and Calspan created a flying qualities database that is being used to develop and evaluate new system identification techniques. This paper first describes the capabilities of the Learjet in-flight simulators. Then, a description of the recent flight test program is given including; the aircraft configuration model setup process, the test techniques used in flight, the resulting flying qualities database, and example system identification results.

Wavelet-Based Techniques for Improved On-Line Systems Identification

Wavelet transform methods can be used to rapidly identify the frequency response of aerospace vehicles using on-line time series for selected input/output pairs. These methods are an alternative to windowed Fourier transforms, the main difference being that wavelet transforms more rapidly identify changes in the vehicle response at high frequency, and thus are more suited to problems such as failure detection, loss of control detection, and flutter detection. Identification methods based on wavelet transforms can improve flight safety and increase the efficiency of on-line flight test analysis methods. Two wavelet-based methods are described in this paper, with examples and a discussion of how they are implemented. In the first method the frequency response is estimated using ratios of wavelet transforms, and is recommended for use when the input spectrum is broadband and for piloted input during normal operations. In the second method the wavelets are used as a front end to the Eigensystem Realization Algorithm, and is recommended for system identification when using short duration, discrete inputs such as steps and doublets. Windowed Fourier transform methods remain the recommended choice for longer duration, discrete inputs such as frequency sweeps.

A Complete Aeroservoelastic Model: Incorporation of Oscillation-Reduction-Control into a High-Order CFD/FEM Fighter Aircraft Model

Flight testing for aeroservoelastic clearance is an expensive and time consuming process. Large degree-of-freedom nonlinear aircraft models using Computational Fluid Dynamics coupled with Finite Element Models (CFD/FEM) can be used for accurately predicting inviscid aeroelastic phenomena in all flight regimes including subsonic, supersonic and transonic. With the incorporation of an active feedback control system (FCS), these models can be utilized to reduce the flight test time needed for aeroservoelastic clearance. A complete CFD/FEM/FCS model can be used for full simulations including the dynamics of the fluid, the airframe, the actuators, and the FCS. Accurate CFD/FEM models are computationally complex, rendering their runtime ill suited for adequate FCS design. In this work, a complex, large-degree-of-freedom, transonic, inviscid CFD/FEM model of a fighter aircraft is fitted with a FCS for oscillation reduction. A linear reduced order model (ROM) of the complete aeroelastic aircraft dynamic system is produced directly from the high-order non-linear CFD/FEM model. This rapid runtime ROM is utilized for the design of the FCS, which includes models of the actuators and common nonlinearities in the form of rate limiting and saturation. An oscillation reduction controller is successfully demonstrated via a simulated flight test utilizing the high-order non-linear CFD/FEM/FCS model.

Residualization of an Aircraft Linear Aeroelastic Reduced Order Model to Obtain Static Stability Derivatives

Adverse aeroservoelastic (ASE) interaction is a problem in modern high performance aircraft that merits continued study and analysis. Interactions between the aircraft structure, the aerodynamics and flight control system can lead to oscillations which can be divergent and cause catastrophic failure. Flight testing is, and will continue to be, an integral part of validating a flight vehicle for adverse ASE prevention. With the increasing speed and efficiency of todays modern computers, higher fidelity analysis of the ASE problem (including CFD and non-linear finite element models) is achievable. However, simulation with these higher fidelity methods is still not rapid enough for flight control system design. A reduced order model (ROM) can be created, which is based on the high fidelity solution linearized around an operating point. This ROM, although simplified, still retains much of the valuable information that the high fidelity computational model provides. The ROM provides a means for much faster simulation, lending itself valuable for FCS design. While providing a means for much more rapid simulation, this ROM also serves other uses and contains a wealth of information. One use of the aeroelastic ROM that is exploited in this study is residualization of the ROM for the extraction of static stability derivatives. Traditional stability derivatives, which are based on a rigid body model, will differ from those that are a result of the residualization process described herein. The stability derivatives that result from this method will include the flexible structure effects that the ROM contains.

Aeroservoelastic Predictive Analysis Capability

Modern flight control systems augment aircraft dynamics so the pilot can more effectively accomplish complex missions. These handling and performance benefits typically use high gain control systems that can result in adverse aeroservoelastic (ASE) interactions. Modeling and simulation tools such as AERO combine structural dynamics and aerodynamics with thousands or even millions of degrees of freedom. In this paper, the feasibility of including a flight control system and actuator models as part of the high fidelity simulation is demonstrated. An F/A-18C fighter aircraft is used as an example problem. The combined modeling tool will more reliably predict adverse ASE interactions and thereby improve flight-testing near envelope boundaries. Furthermore, a CFD-based reduced-order model can be obtained for use in control system design and faster simulation. Technical issues addressed include developing movable CFD grids that conform to a moving control surface, and including a stable projection in place of a longitudinal control system.

System Identification Methods for Improving Flutter Flight Test Techniques

Abstract : Classic flutter flight testing involves the evaluation of a given configuration at a stabilized test point before clearance is given to expand the envelope further. At each stabilized point flight test data are compared with computer simulation models to assess the accuracy of predicted flutter boundaries. Because of the time constraints associated with these procedures, the Air Force has been seeking methods to improve current flight test methods. This paper describes a technique that provides a rapid, on-line tool for the identification of aeroservoelastic (ASE) systems. The technique involves the use of discrete wavelet transforms to compute the impulse response (Markov parameters) of the estimated system. This is then used in the Eigensystem Realization Algorithm (ERA) method to compute the discretized state-space matrices. The technique used herein includes metrics that are used to assess the validity of the identified system. Although the method does require that the identification begin from stabilized initial conditions, it has been shown to be relatively insensitive to input forcing function. A model of a modern naval fighter aircraft was used to evaluate the capabilities of the identification method. The identification techniques were evaluated with and without an active oscillation controller in place.

DESCRIBING FUNCTIONS FOR HIGHER ORDER ACTUATOR MODELS WITH RATE LIMITING

Although powered actuation of aircraft control surfaces has been commonplace for decades, detailed models of the actuators themselves are often not readily available. For most applications including the analysis of flight control and pilot-vehicle systems, simplified lower order models are used with great success. When actuator rate limiting became an important focus of recent pilot-induced oscillation research, generalized describing functions representing the actuator dynamics in the presence of rate limiting were developed from a simplified first order model. However, work by NASA and others has shown that lower order models are not appropriate for aeroservoelastic analysis applications, such as flutter analysis, involving higher frequency dynamics. Furthermore, the generalized describing functions do not adequately capture the nonlinear effects of rate limiting at the higher frequencies that may be encountered with an active flutter suppression system. Using available higher order models for aileron and stabilator actuators from a modern, high performance aircraft, an attempt is made to develop generalized describing functions with rate limiting. The results indicate good success for these examples, especially in the more highly saturated region. Further work is needed to better understand the impact of forward path gain variations and additional higher order models are needed to better validate the normalized results shown.