The Correlation of Wind-Tunnel and Flight-Test Stability and Control Data For an SB2C-1 Airplane

1948 ◽  
Vol 15 (1) ◽  
pp. 5-17
Author(s):  
MARION T. HOCKMAN ◽  
ROBERT E. EISIMINGER
Keyword(s):  
Wind Tunnel ◽  
Flight Test ◽  
Control Data ◽  
Stability And Control ◽  
And Control

scholarly journals Method for designing multi-input system identification signals using a compact time-frequency representation

2021 ◽  
Author(s):  
Mathias Stefan Roeser ◽  
Nicolas Fezans
Keyword(s):  
System Identification ◽  
Design Method ◽  
Flight Test ◽  
Compact Representation ◽  
Time Frequency ◽  
Stability And Control ◽  
Frequency Representation ◽  
Aerodynamic Parameters ◽  
Definition Of ◽  
And Control

AbstractA flight test campaign for system identification is a costly and time-consuming task. Models derived from wind tunnel experiments and CFD calculations must be validated and/or updated with flight data to match the real aircraft stability and control characteristics. Classical maneuvers for system identification are mostly one-surface-at-a-time inputs and need to be performed several times at each flight condition. Various methods for defining very rich multi-axis maneuvers, for instance based on multisine/sum of sines signals, already exist. A new design method based on the wavelet transform allowing the definition of multi-axis inputs in the time-frequency domain has been developed. The compact representation chosen allows the user to define fairly complex maneuvers with very few parameters. This method is demonstrated using simulated flight test data from a high-quality Airbus A320 dynamic model. System identification is then performed with this data, and the results show that aerodynamic parameters can still be accurately estimated from these fairly simple multi-axis maneuvers.

Rotorcraft Lateral-Directional Oscillations: The Anatomy of a Nuisance Mode

2021 ◽  
Author(s):  
Dheeraj Agarwal ◽  
Linghai Lu ◽  
Gareth D. Padfield ◽  
Mark D. White ◽  
Neil Cameron
Keyword(s):  
Simulation Model ◽  
Simulation Models ◽  
Flight Test ◽  
Flight Simulation ◽  
Stability And Control ◽  
Systems Research ◽  
Control Derivatives ◽  
Research Aircraft ◽  
Level Of Understanding ◽  
And Control

High-fidelity rotorcraft flight simulation relies on the availability of a quality flight model that further demands a good level of understanding of the complexities arising from aerodynamic couplings and interference effects. One such example is the difficulty in the prediction of the characteristics of the rotorcraft lateral-directional oscillation (LDO) mode in simulation. Achieving an acceptable level of the damping of this mode is a design challenge requiring simulation models with sufficient fidelity that reveal sources of destabilizing effects. This paper is focused on using System Identification to highlight such fidelity issues using Liverpool's FLIGHTLAB Bell 412 simulation model and in-flight LDO measurements from the bare airframe National Research Council's (Canada) Advanced Systems Research Aircraft. The simulation model was renovated to improve the fidelity of the model. The results show a close match between the identified models and flight test for the LDO mode frequency and damping. Comparison of identified stability and control derivatives with those predicted by the simulation model highlight areas of good and poor fidelity.

VECTOR Program Background and Plan

1998 ◽  
Author(s):  
J. Patrick Schondel ◽  
Michael R. Robinson
Keyword(s):  
Flight Control ◽  
System Integration ◽  
Flight Test ◽  
The United States ◽  
Data System ◽  
Demonstration Program ◽  
Thrust Vectoring ◽  
Stability And Control ◽  
Short Takeoff And Landing ◽  
And Control

The U.S. Navy in cooperation with the Ministries of Defense of Germany and Sweden are initiating a 3-year demonstration program in 1998 to evaluate and define the benefits of thrust vectoring beyond those already understood for Close-in-Combat (CiC). The VECTOR (Vectoring ESTOL Control and Tailless Operational Research) program will capitalize on the X-31 airframe and a contractor team that includes Boeing, G.E., DASA, Volvo, and SAAB to demonstrate the following technologies: • AVEN® Nozzle - a G.E. designed vectoring nozzle applicable to the F404 family of engines • Extremely Short Takeoff and Landing (ESTOL) - employ thrust vectoring and precision control for poststall flight in approach to landing and during take off • Reduced Tail/Tailless - rely on thrust vectoring for primary aircraft stability and control • Advanced Air Data System (AADS) - flush air data ports or optical air data system integrated with the control system to handle the extensive angle-of-attack and sideslip envelope. The flight test activity will be conducted in the United States. However, technical development activities will be conducted in all three countries. Germany and Sweden will contribute technical expertise primarily related to flight control and propulsion system integration, respectively.

scholarly journals ESTIMATION OF THE LATERAL AERODYNAMIC COEFFICIENTS FOR SKYWALKER X8 FLYING WING FROM REAL FLIGHT-TEST DATA

2018 ◽  
Vol 58 (2) ◽  
pp. 77
Author(s):  
Rahman Mohammadi Farhadi ◽  
Vyacheslav Kortunov ◽  
Andrii Molchanov ◽  
Tatiana Solianyk
Keyword(s):  
Test Data ◽  
Flight Test ◽  
Least Square ◽  
Stability And Control ◽  
Information Parameter ◽  
Control Derivatives ◽  
Aerial Vehicle ◽  
Aerodynamic Parameters ◽  
Priori Information ◽  
And Control

Stability and control derivatives of Skywalker X8 flying wing from flight-test data are estimated by using the combination of the output error and least square methods in the presence of the wind. Data is collected from closed loop flight tests with a proportional-integral-derivative (PID) controller that caused data co-linearity problems for the identification of the unmanned aerial vehicle (UAV) dynamic system. The data co-linearity problem is solved with a biased estimation via priori information, parameter fixing and constrained optimization, which uses analytical values of aerodynamic parameters, the level of the identifiability and sensitivity of the measurement vector to the parameters. Estimated aerodynamic parameters are compared with the theoretically calculated coefficients of the UAV, moreover, the dynamic model is validated with additional flight-test data and small covariances of the estimated parameters.

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