Jeffrey Ouellette | Search NASA

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Frequency Domain Quasi Maximum Likelihood Identification of Low Order Aeroservoelastic Models from Flight-Test Data

In this paper a quasi maximum likelihood method for estimating a low order model of a flexible vehicle has been developed and demonstrated. The quasi maximum likelihood method uses a large number of sensors to estimate the parameters and covariance in a method consistent with the maximum likelihood filter-error method. The cost function has been defined in a frequency domain to improve computational efficiency and to enable estimation of an unstable plant. The method has been demonstrated using flight data from the National Aeronautics and Space Administration X-56A Multi-Utility Technology Testbed flex wing tests, which include unstable flutter modes. The method was able to effectively estimate the frequency and damping of the dynamics of the aircraft and generate a transfer function that is useful for control system evaluations.

Jeffrey Ouellette↗

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Using Distributed Fiber-optic Strain Sensing to Estimate Generalized Modal Coordinates from Flight-test Data

A method for estimating the generalized modal coordinates of an aircraft during flight has been developed. The Fiber-optic Sensing System (FOSS) offers an efficient and cost-effective method of measuring the strain at thousands of points along the wings. The estimation of modal coordinates was implemented as a two-step process. First, a maximum likelihood method is used to estimate the statistical properties of the sensors and generalized modal coordinates. Second, the strain mode shapes from the finite element model are used along with the statistical properties from the first step to estimate the generalized modal coordinates over time. Using simulated data from the X-56A Multi-Utility Technology Testbed (MUTT), different methods of modal coordinate estimation were compared to demonstrate the benefits and weaknesses of each. These were compared against the exact solution and an ordinary least squares (a more traditional method) estimate. Modal coordinate estimation methods were then applied to flight-test data from the X-56A aircraft to show that the method continues to work well with actual test data. The new estimation method provides insights unavailable from more classical approaches.

Jeffrey Ouellette↗

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Using Distributed Fiber-Optic Strain Sensing to Estimate Modal Generalized Modal Coordinates from Flight-Test Data

Background and Motivation - Problem - Contemporary aircraft carry around structural mass so that the flutter instabilities lie well outside of the operational envelope. - Better methods of measuring the structural state could allow reduction of the extra structural weight. - Modal filtering - Standard method in structural analysis - Deformations are a combination of mode shapes - Modal filtering is estimating these modal coordinates from data - Often ordinary least squares methods - Often applied to simpler test articles - Factor Analysis - Classic (in psychology) analysis method - Measurements are a combination of small number of unmeasurable variables - Lessons from this factor can be adapted to improve the modal filtering methods

Jeffrey Ouellette↗

NASA NTRSDate not supplied

Frequency Domain Quasi-Maximum Likelihood Identification of Low Order Aeroservoelastic Models from Flight-Test Data

Background and Motivation - Low Order Equivalent System (LOES) - From handling qualities analysis - Traditionally simplifying complex control law and plant - More easily understood form - Extend LOES to a complex model due to aeroelasticity - Maximum likelihood (Filter Error) System Identification - Z = H_loes (U + W) + V - There are a lot of parameters - System identification usually simplifies by assuming a value - Output Error and Equation Error - Results in biased estimates of the parameters - We are proposing a new method to solving this problem

Jeffrey Ouellette↗

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3rd Aeroelastic Prediction Workshop (AePW-3) Flight Test Working Group

- Introduction - Background - X-56 Body Freedom Flutter Mechanism - Challenges in X-56 Flutter Modeling - Flight Test Working Group Challenge - Individual Presentations - Steve Massey - Jos Aalbers - Jared Grauer - Jeffrey Ouellette - Combined Results - Discussion

Jeffrey Ouellette↗

NASA NTRSDate not supplied

Using Distributed Fiber-optic Strain Sensing to Estimate Generalized Modal Coordinates from Flight-test Data

Background and Motivation - Problem - Contemporary aircraft carry around structural mass so that the flutter instabilities lie well outside of the operational envelope. - Better methods of measuring the structural state could allow reduction of the extra structural weight - Modal filtering - Standard method in structural analysis - Deformations are a combination of mode shapes - Modal filtering is estimating these modal coordinates from data - Often ordinary least squares methods - Often applied to simpler test articles - Factor Analysis - Analysis method from psychology - Measurements are a combination of small number of unmeasurable variables. - Lessons from factor analysis can be adapted to improve the modal filtering methods

Jeffrey Ouellette↗

NASA NTRSDate not supplied

Frequency Domain Quasi Maximum Likelihood Identification of Low Order Aeroservoelastic Models from Flight-Test Data

Background and Motivation - Low Order Equivalent System (LOES) - From handling qualities analysis - Traditionally simplifying complex control law and plant - More easily understood form - Extend LOES to a complex model due to aeroelasticity - Maximum likelihood (Filter Error) System Identification - Z = H_loes (U + W) + V - There are a lot of parameters - Estimate noise parameters - Extra outputs mean extra parameters to estimate - Usually assume noise model to simplify - Output Error and Equation Error - Results in biased estimates of the parameters - Sensing flexible aircraft have many outputs - Quasi maximum likelihood exploits redundancy of outputs

Jeffrey Ouellette↗

NASA NTRSDate not supplied

Finite Element Based Model of Fiber-optic Strain Sensor Mounted on a Semi-monocoque Structure

Fiber-optic strain sensing (FOSS) provides an opportunity for additional awareness and can enable other mitigations such as flutter suppression or gust load alleviation but requires an accurate computational model as part of a design process that integrates structures and controls. A modeling approach for fiber-optic strain sensors has been developed using a shell element formulation in a detailed finite element model. The new FOSS modeling approach is designed to better scale to more complex realistic structures and fiber layouts than previous approaches. The modeling method was applied to the X-56A vehicle and results were compared to experimental static ground-test data. Additionally, the experimental sensor noise of these fiber optic sensors was assessed for the correlations in time and correlations along the fiber. The noise does show significant correlations both in time and along the fibers. These correlations of the noise should also be considered in modeling and assessing algorithms that will use FOSS.

Jeffrey Ouellette↗

NASA NTRSDate not supplied

Summary of Results from the Third Aeroelastic Prediction Workshop Flight Test Working Group

This paper summarizes results of the Flight Test Working Group presented at the third Aeroelastic Prediction Workshop held in January 2023. The Flight Test Working Group was looking at the application of flutter prediction tools of a complete aircraft and comparing those predictions to flight-test data. The teams generated a total of six different predictions of the body freedom flutter exhibited by the X-56A experimental aircraft with flexible wings. The computational predictions of frequency and damping are compared with the flight-test data. All of the methods gave similar predictions of the flutter speed, but were all about 10 to 20 knots higher than the measured flutter speed. Additionally, the different generalized aerodynamic forces and the aerodynamic work from the computational tools are compared to illustrate the differences in the methods. Looking at the aerodynamic work done by the flutter mode, suggests that the pitch motion is dissipating less energy and the plunge motion is adding more in the methods which better predict the flutter. However, with only six different predictions it was not possible to develop more definitive conclusions.

Jeffrey Ouellette↗

NASA NTRSDate not supplied

Finite Element Based Model of Fiber-optic Strain Sensor Mounted on a Semi-monocoque Structure

Fiber-optic strain sensing (FOSS) provides an opportunity for additional awareness and can enable other mitigations such as flutter suppression or gust load alleviation but requires an accurate computational model as part of a design process that integrates structures and controls. A modeling approach for fiber-optic strain sensors has been developed using a shell element formulation in a detailed finite element model. The new FOSS modeling approach is designed to better scale to more complex realistic structures and fiber layouts than previous approaches. The modeling method was applied to the X-56A vehicle and results were compared to experimental static ground-test data. Additionally, the experimental sensor noise of these fiber optic sensors was assessed for the correlations in time and correlations along the fiber. The noise does show significant correlations both in time and along the fibers. These correlations of the noise should also be considered in modeling and assessing algorithms that will use FOSS.

Jeffrey Ouellette↗

NASA NTRSDate not supplied

Summary of Results from the Third Aeroelastic Prediction Workshop Flight Test Working Group

This paper summarizes results of the Flight Test Working Group presented at the third Aeroelastic Prediction Workshop held in January 2023. The Flight Test Working Group was looking at the application of flutter prediction tools of a complete aircraft and comparing those predictions to flight-test data. The teams generated a total of six different predictions of the body freedom flutter exhibited by the X-56A experimental aircraft with flexible wings. The computational predictions of frequency and damping are compared with the flight-test data. All of the methods gave similar predictions of the flutter speed, but were all about 10 to 20 knots higher than the measured flutter speed. Additionally, the different generalized aerodynamic forces and the aerodynamic work from the computational tools are compared to illustrate the differences in the methods. Looking at the aerodynamic work done by the flutter mode, suggests that the pitch motion is dissipating less energy and the plunge motion is adding more in the methods which better predict the flutter. However, with only six different predictions it was not possible to develop more definitive conclusions.

Jeffrey Ouellette↗

NASA NTRSDate not supplied

Updating High-Order Aeroservoelastic Models from Low-Order System Identification Results

Estimating aircraft models from test data requires several simplifying assumptions that introduce biases into the parameter estimates. In this paper, these biases are defined and a method for estimating the biases is discussed. Having an estimate of the bias allows the parameters estimated from test to be integrated into a high-order model. The Integrated Adaptive Wing Technology Maturation (IAWTM) wind tunnel model is discussed and the bias is demonstrated for one of the testing configurations. The methodology was able to estimate these biases and apply corrections to high-order aeroelastic models to improve the fit to test data. The consideration of the biases allows more meaningful comparisons and avoids the erroneous differences between pretest predictions and the fitted model.

Jeffrey Ouellette↗

NASA NTRSDate not supplied

Using Importance Sampling Monte Carlo to Analyze Aircraft Takeoff and Landing

Aircraft have achieved high levels of reliability, so the probability of undesirable dynamics is very low. The low probability of a bad takeoff or landing challenges nondeterministic methods to quantify the uncertainty of these improbable events. In the present work, a Monte Carlo method is defined to more effectively quantify these unlikely events that is suitable for uncertainty analysis of a mature design with many uncertainty parameters. The proposed Monte Carlo method has been applied to the takeoff and landing of the X-59 Quiet SuperSonic Technology (QueSST). Takeoff and landing simulation profiles were defined using a combination of industry standards and piloted simulation experience. The proposed method is shown to be able to quantify events with significantly fewer samples than would be required with conventional Monte Carlo. These results improve understanding of the sensitivity of the takeoff and landing dynamics to inform further studies and test planning.

Jeffrey Ouellette↗

NASA NTRSDate not supplied

Using Importance Sampling Monte Carlo to Analyze Aircraft Takeoff and Landing

Aircraft have achieved high levels of reliability, so the probability of undesirable dynamics is very low. The low probability of a bad takeoff or landing challenges nondeterministic methods to quantify the uncertainty of these improbable events. In the present work, a Monte Carlo method is defined to more effectively quantify these unlikely events that is suitable for uncertainty analysis of a mature design with many uncertainty parameters. The proposed Monte Carlo method has been applied to the takeoff and landing of the X-59 Quiet SuperSonic Technology (QueSST). Takeoff and landing simulation profiles were defined using a combination of industry standards and piloted simulation experience. The proposed method is shown to be able to quantify events with significantly fewer samples than would be required with conventional Monte Carlo. These results improve understanding of the sensitivity of the takeoff and landing dynamics to inform further studies and test planning.

Jeffrey Ouellette↗

NASA NTRSDate not supplied

Updating High-Order Aeroservoelastic Models from Low-Order System Identification Results

Estimating aircraft models from test data requires several simplifying assumptions that introduce biases into the parameter estimates. In this paper, these biases are defined and a method for estimating the biases is discussed. Having an estimate of the bias allows the parameters estimated from test to be integrated into a high-order model. The Integrated Adaptive Wing Technology Maturation (IAWTM) wind tunnel model is discussed and the bias is demonstrated for one of the testing configurations. The methodology was able to estimate these biases and apply corrections to high-order aeroelastic models to improve the fit to test data. The consideration of the biases allows more meaningful comparisons and avoids the erroneous differences between pretest predictions and the fitted model.

Jeffrey Ouellette↗