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F/A-18 testbed development and flight research are highlighted in this presentation. The current focus is on stability, specifically adaptive flight control, but soon the focus will move towards stability and maneuverability, examining flight planning and guidance, adaptive flight control, engine control and airframe and structures. Later research will additionally review V and V methods. Current and future IRAC plans are highlighted.
The NASA F/A-18 tail number (TN) 853 full-scale Integrated Resilient Aircraft Control (IRAC) testbed has been designed with a full array of capabilities in support of the Aviation Safety Program. Highlights of the system's capabilities include: 1) a quad-redundant research flight control system for safely interfacing controls experiments to the aircraft's control surfaces; 2) a dual-redundant airborne research test system for hosting multi-disciplinary state-of-the-art adaptive control experiments; 3) a robust reversionary configuration for recovery from unusual attitudes and configurations; 4) significant research instrumentation, particularly in the area of static loads; 5) extensive facilities for experiment simulation, data logging, real-time monitoring and post-flight analysis capabilities; and 6) significant growth capability in terms of interfaces and processing power.
The NASA Marshall Space Flight Center (MSFC) Flight Mechanics and Analysis Division developed an Adaptive Augmenting Control (AAC) algorithm for launch vehicles that improves robustness and performance by adapting an otherwise welltuned classical control algorithm to unexpected environments or variations in vehicle dynamics. This AAC algorithm is currently part of the baseline design for the SLS Flight Control System (FCS), but prior to this series of research flights it was the only component of the autopilot design that had not been flight tested. The Space Launch System (SLS) flight software prototype, including the adaptive component, was recently tested on a piloted aircraft at Dryden Flight Research Center (DFRC) which has the capability to achieve a high level of dynamic similarity to a launch vehicle. Scenarios for the flight test campaign were designed specifically to evaluate the AAC algorithm to ensure that it is able to achieve the expected performance improvements with no adverse impacts in nominal or nearnominal scenarios. Having completed the recent series of flight characterization experiments on DFRC's F/A-18, the AAC algorithm's capability, robustness, and reproducibility, have been successfully demonstrated. Thus, the entire SLS control architecture has been successfully flight tested in a relevant environment. This has increased NASA's confidence that the autopilot design is ready to fly on the SLS Block I vehicle and will exceed the performance of previous architectures.
An integrated, nonlinear simulation model suitable for aeroelastic modeling of fixed-wing aircraft has been developed. While the author realizes that the subject of modeling rotating, elastic structures is not closed, it is believed that the equations of motion developed and applied herein are correct to second order and are suitable for use with typical aircraft structures. The equations are not suitable for large elastic deformation. In addition, the modeling framework generalizes both the methods and terminology of non-linear rigid-body airplane simulation and traditional linear aeroelastic modeling. Concerning the importance of angular/elastic inertial coupling in the dynamic analysis of fixed-wing aircraft, the following may be said. The rigorous inclusion of said coupling is not without peril and must be approached with care. In keeping with the same engineering judgment that guided the development of the traditional aeroelastic equations, the effect of non-linear inertial effects for most airplane applications is expected to be small. A parameter does not tell the whole story, however, and modes flagged by the parameter as significant also need to be checked to see if the coupling is not a one-way path, i.e., the inertially affected modes can influence other modes.
A flight test program was undertaken to demonstrate the feasibility of obtaining external burning (EB) data in a flight environment, and to address the question of facility interference in ground test external burning data. Results showed that external burning was effective at reducing transonic base drag in a flight environment having dynamically changing conditions. The flight test program demonstrated that external burning is not just a laboratory phenomenon but is a viable technology for aerospace vehicles.
Major objectives of the NASA High-Alpha Technology Program are the accurate prediction of the internal (inlet) aerodynamics of an aircraft operating at attitudes of up to 60 deg pitch and 10 deg yaw and the calibration of CFD codes for predicting the internal performance of inlets. Numerical results are presented for the three cases of a full-scale model, a 20-percent scale model at design mass flow, and a scale model at reduced mass flow; attention is given to the effects of Reynolds number. All three cases are at 30 deg angle of attack and zero deg yaw. The results thus obtained are helpful to experimentalists in determining some of their instrumentation requirements.
A 1/6-scale F-18 wind-tunnel model was tested in the Transonic Dynamics Tunnel at the NASA Langley Research Center as part of the Actively Controlled Response Of Buffet-Affected Tails (ACROBAT) program to assess the use of active controls in reducing vertical tail buffeting. The starboard vertical tail was equipped with an active rudder and other aerodynamic devices, and the port vertical tail was equipped with piezoelectric actuators. The tunnel conditions were atmospheric air at a dynamic pressure of 14 psf. By using single-input-single-output control laws at gains well below the physical limits of the control effectors, the power spectral density of the root strains at the frequency of the first bending mode of the vertical tail was reduced by as much as 60 percent up to angles of attack of 37 degrees. Root mean square (RMS) values of root strain were reduced by as much as 19 percent. Stability margins indicate that a constant gain setting in the control law may be used throughout the range of angle of attack tested.
Buffeting is an aeroelastic phenomenon occurring at high angles of attack that plagues high performance aircraft, especially those with twin vertical tails. Previous wind-tunnel and flight tests were conducted to characterize the buffet loads on the vertical tails by measuring surface pressures, bending moments, and accelerations. Following these tests, buffeting responses were computed using the measured buffet pressures and compared to the measured buffeting responses. The calculated results did not match the measured data because the assumed spatial correlation of the buffet pressures was not correct. A better understanding of the partial (spatial) correlation of the differential buffet pressures on the tail was necessary to improve the buffeting predictions. Several wind-tunnel investigations were conducted for this purpose. When compared, the results of these tests show that the partial correlation scales with flight conditions. One of the remaining questions is whether the wind-tunnel data is consistent with flight data. Presented herein, cross-spectra and coherence functions calculated from pressures that were measured on the High Alpha Research Vehicle indicate that the partial correlation of the buffet pressures in flight agrees with the partial correlation observed in the wind tunnel.
The Active Aeroelastic Wing will demonstrate technologies related to aeroservoelastic effects such as wing twist and load minimization. This paper presents a control design based on H-infinity synthesis that simultaneously considers the multiple objectives associated with handling qualities, actuator limitations, and loads. The controller is realized as a filter and gain set approximation to a state-space H-infinity controller. This approximation allows scheduling of the controller over a flight envelope.
The X-33 reusable launch vehicle demonstrator has identified the need to implement a vehicle health monitoring system that can acquire data that monitors system health and performance. Sanders, a Lockheed Martin Company, has designed and developed a commercial off-the-shelf (COTS)-based open architecture system that implements a number of technologies that have not been previously used in a flight environment. NASA Dryden Flight Research Center and Sanders teamed to demonstrate that the distributed remote health nodes, fiber optic distributed strain sensor, and fiber distributed data interface communications components of the X-33 vehicle health management (VHM) system could be successfully integrated and flown on a NASA F-18 aircraft. This paper briefly describes components of X-33 VHM architecture flown at Dryden and summarizes the integration and flight demonstration of these X-33 VHM components. Finally, it presents early results from the integration and flight efforts.
The X-33 reusable launch vehicle demonstrator has identified the need to implement a vehicle health monitoring system that can acquire data that monitors system health and performance. Sanders, a Lockheed Martin Company, has designed and developed a COTS-based open architecture system that implements a number of technologies that have not been previously used in a flight environment. NASA Dryden Flight Research Center and Sanders teamed to demonstrate that the distributed remote health nodes, fiber optic distributed strain sensor, and fiber distributed data interface communications components of the X-33 vehicle health management (VHM) system could be successfully integrated and flown on a NASA F-18 aircraft. This paper briefly describes components of X-33 VHM architecture flown at Dryden and summarizes the integration and flight demonstration of these X-33 VHM components. Finally, it presents early results from the integration and flight efforts.
The vertical tail buffet problem of fighter aircraft occurs at high angles of attack when the vortical flow breaks down ahead of the vertical tails resulting in unsteady and unbalanced pressure loads on the vertical tails. The buffet loads imposed upon the vertical tails resulted in a premature fatigue failure of the tails, and consequently limits the performance and super maneuverability of twin-tail fighter aircraft. An active smart material control system using distributed piezoelectric actuators has been developed for buffet alleviation and is presented. The inboard and outboard surfaces of the vertical tail are equipped with piezoelectric actuators to control the buffet responses in the first bending and torsion modes. The electrodynamics of the piezoelectric actuators are expressed with a three-dimensional finite-element model. A single-input-single-output controller is designed to drive the active piezoelectric actuators. High-fidelity multidisciplinary analysis modules for the fluid dynamics, structure dynamics, electrodynamics of the piezoelectric actuators, control law, fluid structure interfacing, and grid motion are integrated into a multidisciplinary computing environment that controls the temporal synchronization of the analysis modules. At 30 degree angle of attack, RMS values of tip acceleration are reduced by as much as 12%. The peak values of the power spectral density of tail-tip acceleration are reduced by as much as 22% in the first bending mode and by as much as 82% in the first torsion mode. The actively controlled piezoelectric actuators were also effective in adding damping at wide range of angles of attack.
Buffeting is an aeroelastic phenomenon which plagues high performance aircraft, especially those with twin vertical tails, at high angles of attack. Previous wind-tunnel and flight tests were conducted to characterize the buffet loads on the vertical tails by measuring surface pressures, bending moments, and accelerations. Following these tests, buffeting estimates were computed using the measured buffet pressures and compared to the measured responses. The estimates did not match the measured data because the assumed spatial correlation of the buffet pressures was not correct. A better understanding of the partial (spatial) correlation of the differential buffet pressures on the tail was necessary to improve the buffeting estimates. Several wind-tunnel investigations were conducted for this purpose. When combined and compared, the results of these tests show that the partial correlation depends on and scales with flight conditions. One of the remaining questions is whether the windtunnel data is consistent with flight data. Presented herein, cross-spectra and coherence functions calculated from pressures that were measured on the high alpha research vehicle (HARV) indicate that the partial correlation of the buffet pressures in flight agrees with the partial correlation observed in the wind tunnel.
A patched-grid algorithm for the analysis of complex configurations with an implicit, upwind-biased Navier-Stokes solver is presented. Results from both a spatial-flux and a time-flux conservation approach to patching across zonal boundaries are presented. A generalized coordinate transformation with a biquadratic geometric element is used at the zonal interface in order to treat highly stretched viscous grids and arbitrarily-shaped zonal boundaries. Applications are made to the F-18 forebody-strake configuration at subsonic, high-alpha conditions. Computed surface flow patterns compare well with ground-based and flight-test results; the large effect of Reynolds number on the forebody flow-field is shown.
The Active Aeroelastic Wing airplane was successfully flight-tested in March 2005. During phase 1 of the two-phase program, an onboard excitation system provided independent control surface movements that were used to develop a loads model for the wing structure and wing control surfaces. The resulting loads model, which was used to develop the control laws for phase 2, is described. The loads model was developed from flight data through the use of a multiple linear regression technique. The loads model input consisted of aircraft states and control surface positions, in addition to nonlinear inputs that were calculated from flight-measured parameters. The loads model output for each wing consisted of wing-root bending moment and torque, wing-fold bending moment and torque, inboard and outboard leading-edge flap hinge moment, trailing-edge flap hinge moment, and aileron hinge moment. The development of the Active Aeroelastic Wing loads model is described, and the ability of the model to predict loads during phase 2 research maneuvers is demonstrated. Results show a good match to phase 2 flight data for all loads except inboard and outboard leading-edge flap hinge moments at certain flight conditions. The average load prediction errors for all loads at all flight conditions are 9.1 percent for maximum stick-deflection rolls, 4.4 percent for 5-g windup turns, and 7.7 percent for 4-g rolling pullouts.
Successful flight-testing of the Active Aeroelastic Wing airplane was completed in March 2005. This program, which started in 1996, was a joint activity sponsored by NASA, Air Force Research Laboratory, and industry contractors. The test program contained two flight test phases conducted in early 2003 and early 2005. During the first phase of flight test, aerodynamic models and load models of the wing control surfaces and wing structure were developed. Design teams built new research control laws for the Active Aeroelastic Wing airplane using these flight-validated models; and throughout the final phase of flight test, these new control laws were demonstrated. The control laws were designed to optimize strategies for moving the wing control surfaces to maximize roll rates in the transonic and supersonic flight regimes. Control surface hinge moments and wing loads were constrained to remain within hydraulic and load limits. This paper describes briefly the flight control system architecture as well as the design approach used by Active Aeroelastic Wing project engineers to develop flight control system gains. Additionally, this paper presents flight test techniques and comparison between flight test results and predictions.
Successful flight-testing of the Active Aeroelastic Wing airplane was completed in March 2005. This program, which started in 1996, was a joint activity sponsored by NASA, Air Force Research Laboratory, and industry contractors. The test program contained two flight test phases conducted in early 2003 and early 2005. During the first phase of flight test, aerodynamic models and load models of the wing control surfaces and wing structure were developed. Design teams built new research control laws for the Active Aeroelastic Wing airplane using these flight-validated models; and throughout the final phase of flight test, these new control laws were demonstrated. The control laws were designed to optimize strategies for moving the wing control surfaces to maximize roll rates in the transonic and supersonic flight regimes. Control surface hinge moments and wing loads were constrained to remain within hydraulic and load limits. This paper describes briefly the flight control system architecture as well as the design approach used by Active Aeroelastic Wing project engineers to develop flight control system gains. Additionally, this paper presents flight test techniques and comparison between flight test results and predictions.
The Active Aeroelastic Wing airplane was successfully flight-tested in March 2005. During phase 1 of the two-phase program, an onboard excitation system provided independent control surface movements that were used to develop a loads model for the wing structure and wing control surfaces. The resulting loads model, which was used to develop the control laws for phase 2, is described. The loads model was developed from flight data through the use of a multiple linear regression technique. The loads model input consisted of aircraft states and control surface positions, in addition to nonlinear inputs that were calculated from flight-measured parameters. The loads model output for each wing consisted of wing-root bending moment and torque, wing-fold bending moment and torque, inboard and outboard leading-edge flap hinge moment, trailing-edge flap hinge moment, and aileron hinge moment. The development of the Active Aeroelastic Wing loads model is described, and the ability of the model to predict loads during phase 2 research maneuvers is demonstrated. Results show a good match to phase 2 flight data for all loads except inboard and outboard leading-edge flap hinge moments at certain flight conditions. The average load prediction errors for all loads at all flight conditions are 9.1 percent for maximum stick-deflection rolls, 4.4 percent for 5-g windup turns, and 7.7 percent for 4-g rolling pullouts.