Representative Post-Stall Modeling of T-tail Regional Jet and Turboprop Aircraft for Flight Training Simulator

Teng, Tianhang; Zhang, Tony Shu; Liu, Stacey F.; Grant, Peter R.

doi:10.2514/6.2015-2032

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…This model structure is shown in Eq. (19). The stall buffet frequency response function (H (jω)) can be estimated from the PSD as shown in Figure 15, assuming that the driving white noise input signal u has an intensity of one.…”

Section: Ivb Buffet Modelmentioning

confidence: 99%

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Aerodynamic Stall and Buffet Modeling for the Cessna Citation II Based on Flight Test Data

Horssen

Visser

Pool

2018

2018 AIAA Modeling and Simulation Technologies Conference

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To meet the required flight simulator-based stall recovery training that will become mandatory for all aircarrier pilots in 2019, many flight simulators will need to be updated with accurate flight models at high anglesof-attack. Using a database of 69 symmetrical and quasi-steady recorded stalls performed with TU Delft's Cessna Citation II laboratory aircraft, this paper aims to show to what extent relevant stall characteristics can still be modeled using flight test data from such "natural" stall flight test data (i.e., without additional flight test inputs). For modeling the (low-frequency) changes to the aerodynamic characteristics, the well-known stall model structure based on Kirchoff's flow separation theory is used. This aerodynamic stall model is augmented with a high-frequency stall buffet model identified based on power spectral density analysis of the flight test linear acceleration data. The stall model identification results obtained from a proposed methodology in which the aerodynamic and buffet model parameters were deliberately jointly estimated shows that the dynamic parameters capturing the stall hysteresis effect can indeed be estimated using quasi-steady stall maneuvers. Aerodynamic terms related to the pitch rate, however, are difficult to estimate for such quasi-steady maneuvers, given the correlation between pitch rate and angle-of-attack. It was found that estimating stall transient effects (i.e., hysteresis time constants), which normally require highly dynamic flight test maneuvers, was found to be improved with with explicit use of the measured accelerations caused by the stall buffet in the identification methodology.

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Section: Ivb Buffet Modelmentioning

confidence: 99%

“…18 A similar research has been done in modeling the stall characteristics due to different parts of the aircraft. 19 Current practice is thus that stall models are created using wind tunnel, CFD and…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic Stall and Buffet Modeling for the Cessna Citation II Based on Flight Test Data

Horssen

Visser

Pool

2018

2018 AIAA Modeling and Simulation Technologies Conference

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“…Aircraft design is an evolving area and with the availability of advanced numerical techniques such as finite element methods and computational fluid dynamics (CFD), it is now possible to do an initial detailed design virtually (Raymer 1999;Okonkwo and Smith 2016;Rizzi et al 2010). A challenging area of any design is the prediction of aerodynamic coefficients during and beyond aircraft stall (Ciliberti et al 2017;Lutz et al 2015;Grote and Radespiel 2006;Teng et al 2015;Lutz et al 2016;Teng et al 2016). More specifically, the pitching moment and lift and drag coefficients have complex characteristics (Phillips 2010), which at high angles of attack may change significantly post-stall, in some cases leading to an autorotation and the development of a spin (Rao and Go 2019;Bennett and Lawson 2018).…”

Section: Introductionmentioning

confidence: 99%

DES of a Slingsby Firefly Aircraft: Unsteady Flow Feature Extraction Using POD and HODMD

et al. 2022

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“…Recently, novel modeling methods have appeared, such as those based on CFD [16] or representative methods based on aircraft geometry and/or configuration. [17,18] Regardless the used modeling method, a major unanswered question is what level of fidelity is actually required for effective pilot stall training. While pilots are found to be able to notice even small changes in key stall model parameters in some experiments [19], recent pilot-in-the-loop evaluations of stall models of varying fidelity levels found no differences in subjective ratings from a group of experienced pilots [20].…”

Section: Introductionmentioning

confidence: 99%

Stall Model Identification of a Cessna Citation II from Flight Test Data Using Orthogonal Model Structure Selection

Ingen

Visser

Pool

2021

AIAA Scitech 2021 Forum

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Since 2019, a key element of simulator-based training of airline pilots is stall training. A major and still largely open research question is which level of model fidelity is required for effective training. As part of an effort to answer this question, a model of the quasi-steady stall dynamics of a Cessna Citation II aircraft is identified from flight test data that was specifically collected for this research at an altitude of 5,500 m. To ensure any reductions in elevator and aileron effectiveness during stall were also explicitly measured, the test pilots used additional quasi-random flight test inputs. The considered stall model structure is based on Kirchoff's theory of flow separation. During identification, the nonlinear and linear parameters of the model are estimated in separate, recursively executed, steps. This separation enables the application of a semi-objective model structure selection method using multivariate orthogonal functions for the aerodynamic coefficients included in the model. This approach shows that stall-related effects should be included in the model equations for lift, drag, and pitch moment. Overall, it is found that the model parameters were consistently estimated from the flight test data and that the model accurately describes the aircraft's stall dynamics in the considered flight condition. The developed methodology is concluded to be well-suited for the direct identification of stall models from flight test data.Nomenclature Roman Matrix of regression variables * Specific force in * -direction [m/s 2 ] a Regression variable vector * Force/moment coefficient [-] Average chord length [m] * Angular moment of inertia around * -axis [kgm 2 ] Cost function value Mach number [-] Mass [kg] Amount of time samples Amount of terms in a model structure Roll rate [rad/s] Pitch rate [rad/s] 2

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Representative Post-Stall Modeling of T-tail Regional Jet and Turboprop Aircraft for Flight Training Simulator

Cited by 5 publications

References 10 publications

Aerodynamic Stall and Buffet Modeling for the Cessna Citation II Based on Flight Test Data

Aerodynamic Stall and Buffet Modeling for the Cessna Citation II Based on Flight Test Data

DES of a Slingsby Firefly Aircraft: Unsteady Flow Feature Extraction Using POD and HODMD

Stall Model Identification of a Cessna Citation II from Flight Test Data Using Orthogonal Model Structure Selection

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