Calibration of a Subscale F/A-22 Engine Inlet Empirical Model Using Ground and Flight Test Data

2006 ◽  
Author(s):  
Jeffrey F. Monaco ◽  
David S. Kidman ◽  
Randall L. Bickford ◽  
Donald J. Malloy
Keyword(s):  
Parameter Estimation ◽  
System Identification ◽  
Modeling And Simulation ◽  
Real Time ◽  
Air Force ◽  
Test Data ◽  
Model Updating ◽  
Flight Test ◽  
Simulation Tools ◽  
Identification Parameter

The US Air Force’s two main aeropropulsion test centers, Arnold Engineering Development Center and the Air Force Flight Test Center, are developing a common suite of modeling and simulation tools employing advanced predictive modeling technologies. This common set of modeling and simulation tools incorporates real-time data validation, system identification, parameter estimation model calibration, and automated model updating as new test results or operational data become available. The expected benefit is improved efficiency and accuracy for online diagnostic monitoring of Air Force assets. These resultant models could also be used for flight manual development, determining compliance to specifications, or to aid in real-time equipment monitoring. This paper describes the integrated approach to system identification, parameter estimation, and model updating. Implementation of a software package to enable efficient model handoff between test groups and centers is discussed. An F/A-22 inlet model is used to demonstrate the approach. Compact polynomial function models of the distortion and recovery flow descriptors and 40-probe pressure values are derived from quasi-steady and instantaneous subscale wind tunnel data. The model parameters are then calibrated with F/A-22 flight test data. Results show that the modeling algorithm captures the relevant nonlinear physics of the application, and the calibration and updating procedure improves the model match to flight data. A companion paper provides preliminary results from integrating the calibrated total-pressure inlet distortion and recovery models into a real-time equipment health monitoring system to support test operations.

Ground Test Data Validation Using a Subscale F/A-22 Engine Inlet Empirical Model

2006 ◽  
Author(s):  
Randall L. Bickford ◽  
Donald J. Malloy ◽  
Jeffrey F. Monaco ◽  
David S. Kidman
Keyword(s):  
Parameter Estimation ◽  
Real Time ◽  
Air Force ◽  
Model Updating ◽  
Integrated Approach ◽  
Data Validation ◽  
Time Data ◽  
Simulation Tools ◽  
Real Time Data ◽  
Identification Parameter

The US Air Force’s two main aeropropulsion test centers, Arnold Engineering Development Center and the Air Force Flight Test Center, are developing a common suite of modeling and simulation tools employing advanced predictive modeling technologies. These modeling and simulation tools incorporate real-time data validation, system identification, parameter estimation, model calibration, and automated model updating as new test results or operational data become available. The expected benefit is improved efficiency and accuracy for online diagnostic monitoring of Air Force assets. This paper describes the integrated approach to real-time data validation. Implementation of a software package to enable efficient model handoff between test groups and centers and extension of the capability to aeropropulsion models is discussed. An F/A-22 inlet model is used to demonstrate the approach. Compact polynomial function models of the distortion and recovery flow descriptors and 40-probe pressure values are derived from quasi-steady and instantaneous subscale wind tunnel data. The total-pressure inlet distortion and recovery models are integrated in a real-time equipment health monitoring system designed to support test operations, and preliminary results are given. A companion paper describes the integrated approach to system identification, parameter estimation, and model updating.

Identification of Physical Helicopter Models Using Subspace Identification

2020 ◽  
Vol 65 (2) ◽  
pp. 1-14
Author(s):  
Sevil Avcıoğlu ◽  
Ali Türker Kutay ◽  
Kemal Leblebicioğlu
Keyword(s):  
Parameter Estimation ◽  
State Space ◽  
Nonlinear Optimization ◽  
Physical Parameter ◽  
Test Data ◽  
Flight Test ◽  
The State ◽  
Physical Parameters ◽  
Subspace Identification ◽  
Study Results

Subspace identification is a powerful tool due to its well-understood techniques based on linear algebra (orthogonal projections and intersections of subspaces) and numerical methods like singular value decomposition. However, the state space model matrices, which are obtained from conventional subspace identification algorithms, are not necessarily associated with the physical states. This can be an important deficiency when physical parameter estimation is essential. This holds for the area of helicopter flight dynamics, where physical parameter estimation is mainly conducted for mathematical model improvement, aerodynamic parameter validation, and flight controller tuning. The main objective of this study is to obtain helicopter physical parameters from subspace identification results. To achieve this objective, the subspace identification algorithm is implemented for a multirole combat helicopter using both FLIGHTLAB simulation and real flight-test data. After obtaining state space matrices via subspace identification, constrained nonlinear optimization methodologies are utilized for extracting the physical parameters. The state space matrices are transformed into equivalent physical forms via the "sequential quadratic programming" nonlinear optimization algorithm. The required objective function is generated by summing the square of similarity transformation equations. The constraints are selected with physical insight. Many runs are conducted for randomly selected initial conditions. It can be concluded that all of the significant parameters can be obtained with a high level of accuracy for the data obtained from the linear model. This strongly supports the idea behind this study. Results for the data obtained from the nonlinear model are also evaluated to be satisfactory in the light of statistical error analysis. Results for the real flight-test data are also evaluated to be good for the helicopter modes that are properly excited in the flight tests.

scholarly journals Identification of Aeroelastic Models for the X-56A Longitudinal Dynamics Using Multisine Inputs and Output Error in the Frequency Domain

Aerospace ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 24 ◽  
Author(s):  
Jared Grauer ◽  
Matthew Boucher
Keyword(s):  
System Identification ◽  
Test Data ◽  
Flight Control ◽  
Fourier Transforms ◽  
Flight Dynamics ◽  
Flight Test ◽  
Multiple Control ◽  
Output Error ◽  
Short Period ◽  
Flight Dynamics Model

System identification from measured flight test data was conducted using the X-56A aeroelastic demonstrator to identify a longitudinal flight dynamics model that included the short period, first symmetric wing bending, and first symmetric wing torsion modes. Orthogonal phase-optimized multisines were used to simultaneously excite multiple control effectors while a flight control system was active. Non-dimensional stability and control derivatives parameterizing an aeroelastic model were estimated using the output-error approach to match Fourier transforms of measured output response data. The predictive capability of the identified model was demonstrated using other flight test data with different inputs and at a different flight conditions. Modal characteristics of the identified model were explored and compared with other predictions. Practical aspects of the experiment design and system identification analysis, specific to flexible aircraft, are also discussed. Overall, the approach used was successful for identifying aeroelastic flight dynamics models from flight test data.

scholarly journals Adaptive Flight Path Control of Airborne Wind Energy Systems

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 667 ◽  
Author(s):  
Tarek N. Dief ◽  
Uwe Fechner ◽  
Roland Schmehl ◽  
Shigeo Yoshida ◽  
Mostafa A. Rushdi
Keyword(s):  
System Identification ◽  
Wind Energy ◽  
Real Time ◽  
Dynamic Model ◽  
Path Following ◽  
Flight Test ◽  
Adaptive Controller ◽  
System Model ◽  
Pole Placement ◽  
Identification Algorithm

In this paper, we applied a system identification algorithm and an adaptive controller to a simple kite system model to simulate crosswind flight maneuvers for airborne wind energy harvesting. The purpose of the system identification algorithm was to handle uncertainties related to a fluctuating wind speed and shape deformations of the tethered membrane wing. Using a pole placement controller, we determined the required locations of the closed-loop poles and enforced them by adapting the control gains in real time. We compared the path-following performance of the proposed approach with a classical proportional-integral-derivative (PID) controller using the same system model. The capability of the system identification algorithm to recognize sudden changes in the dynamic model or the wind conditions, and the ability of the controller to stabilize the system in the presence of such changes were confirmed. Furthermore, the system identification algorithm was used to determine the parameters of a kite with variable-length tether on the basis of data that were recorded during a physical flight test of a 20 kW kite power system. The system identification algorithm was executed in real time, and significant changes were observed in the parameters of the dynamic model, which strongly affect the resulting response.

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