Aircraft Dynamic Modeling in Turbulence

Morelli, Eugene A.; Cunningham, Kevin

doi:10.2514/6.2012-4650

Cited by 19 publications

(16 citation statements)

References 11 publications

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“…This shift was also seen in Ref. [17] and is expected to be due to an unsteady dependence on the angle of attack. 28 Because filter error includes a more general and accurate characterization of the noise and system dynamics, parameter estimates from Flight 15 data in severe turbulence were more consistent with those from Flight 41 data in light-to-moderate turbulence.…”

Section: Ve Flight Test Results In Severe Turbulencementioning

confidence: 53%

“…However, this method will produce increasingly biased parameter estimates as the turbulence level increases relative to the deterministic signal levels. 17 …”

Section: Iiib Output Error and Equation Errormentioning

confidence: 97%

“…1,16,17 Here, the data is transformed into the frequency domain using a narrow bandwidth encompassing only the dynamics of interest and the excitation. This step effectively removes the measurement noise and avoids other practical problems incurred when using equation error with time-domain data.…”

Section: Iiib Output Error and Equation Errormentioning

confidence: 99%

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A New Formulation of the Filter-Error Method for Aerodynamic Parameter Estimation in Turbulence

Grauer

Morelli

2015

AIAA Atmospheric Flight Mechanics Conference

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A new formulation of the filter-error method for estimating aerodynamic parameters in nonlinear aircraft dynamic models during turbulence was developed and demonstrated. The approach uses an estimate of the measurement noise covariance to identify the model parameters, their uncertainties, and the process noise covariance, in a relaxation method analogous to the output-error method. Prior information on the model parameters and uncertainties can be supplied, and a post-estimation correction to the uncertainty was included to account for colored residuals not considered in the theory. No tuning parameters, needing adjustment by the analyst, are used in the estimation. The method was demonstrated in simulation using the NASA Generic Transport Model, then applied to the subscale T-2 jet-engine transport aircraft in flight. Modeling results in different levels of turbulence were compared with results from time-domain output error and frequencydomain equation error methods to demonstrate the effectiveness of the approach. Nomenclaturea z normal acceleration [g] C L lift force coefficient C m pitching moment coefficient c mean aerodynamic chord [ft] E[.] expectation operator g gravitational acceleration [ft/s 2 ] I yy pitch moment of inertia [slug·ft 2 ] m mass [slug] N number of samples n x number of states n y number of outputs n θ number of parameters q body-axis pitch rate [rad/s] q dynamic pressure [lbf/ft 2 ] R 2 coefficient of determination S wing reference area [ft 2 ] t time [s] V true airspeed [ft/s] α angle of attack [rad] ∆ increment δ e elevator deflection [rad] δ t throttle σ standard deviation Subscripts 0 starting value Superscripts −1 inverse T transposė time derivativê estimated value |.|

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Section: Ve Flight Test Results In Severe Turbulencementioning

confidence: 53%

“…However, this method will produce increasingly biased parameter estimates as the turbulence level increases relative to the deterministic signal levels. 17 …”

Section: Iiib Output Error and Equation Errormentioning

confidence: 97%

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A New Formulation of the Filter-Error Method for Aerodynamic Parameter Estimation in Turbulence

Grauer

Morelli

2015

AIAA Atmospheric Flight Mechanics Conference

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“…42 Recent and ongoing research into icing effects focuses on airframe icing 43 as well as engine icing. [44][45] The modeling and detection of airframe icing effects under turbulence conditions is also the subject of current research, [46][47] as is the modeling and detection of engine icing effects. Simulation-based evaluation methods are also being used to evaluate the potential effectiveness of integrated flight and enhanced propulsion control capability for LOC prevention and recovery, 49 as well as for determining the achievable dynamics of an impaired vehicle.…”

Section: B Advanced Simulation Methodsmentioning

confidence: 99%

Validation of Safety-Critical Systems for Aircraft Loss-of-Control Prevention and Recovery

Belcastro

2012

AIAA Guidance, Navigation, and Control Conference

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Validation of technologies developed for loss of control (LOC) prevention and recovery poses significant challenges. Aircraft LOC can result from a wide spectrum of hazards, often occurring in combination, which cannot be fully replicated during evaluation. Technologies developed for LOC prevention and recovery must therefore be effective under a wide variety of hazardous and uncertain conditions, and the validation framework must provide some measure of assurance that the new vehicle safety technologies do no harm (i.e., that they themselves do not introduce new safety risks). This paper summarizes a proposed validation framework for safety-critical systems, provides an overview of validation methods and tools developed by NASA to date within the Vehicle Systems Safety Project, and develops a preliminary set of test scenarios for the validation of technologies for LOC prevention and recovery.

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“…9, much of this information would be provided to the crew for improved SA and anticipatory guidance. Research is currently being conducted in icing effects detection and identification in the presence of turbulence, [14][15] engine icing effects modeling and detection, 16 energy management, 17 LOC prediction, 18 and improved SA [19][20] . Resilient control and anticipatory guidance methods are also of key importance in preventing LOC accidents, and are indicated by the middle two subsystems of Figure 9.…”

Section: E Integrated Gcs Research Description and System Architecturementioning

confidence: 99%

Loss of Control Prevention and Recovery: Onboard Guidance, Control, and Systems Technologies

Belcastro

2012

AIAA Guidance, Navigation, and Control Conference

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Loss of control (LOC) is one of the largest contributors to fatal aircraft accidents worldwide. LOC accidents are complex in that they can result from numerous causal and contributing factors acting alone or (more often) in combination. These LOC hazards include vehicle impairment conditions, external disturbances; vehicle upset conditions, and inappropriate crew actions or responses. Hence, there is no single intervention strategy to prevent these accidents. NASA previously defined a comprehensive research and technology development approach for reducing LOC accidents and an associated integrated system concept. Onboard technologies for improved situation awareness, guidance, and control for LOC prevention and recovery are needed as part of this approach. Such systems should include: LOC hazards effects detection and mitigation; upset detection, prevention and recovery; and mitigation of combined hazards. NASA is conducting research in each of these areas. This paper provides an overview of this research, including the near-term LOC focus and associated analysis, as well as preliminary flight system architecture.

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Aircraft Dynamic Modeling in Turbulence

Cited by 19 publications

References 11 publications

A New Formulation of the Filter-Error Method for Aerodynamic Parameter Estimation in Turbulence

A New Formulation of the Filter-Error Method for Aerodynamic Parameter Estimation in Turbulence

Validation of Safety-Critical Systems for Aircraft Loss-of-Control Prevention and Recovery

Loss of Control Prevention and Recovery: Onboard Guidance, Control, and Systems Technologies

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