Subjective Noticeability of Variations in Quasi-Steady Aerodynamic Stall Dynamics

Smets, Stephan C.; Visser, Coen C. de; Pool, Daan M.

doi:10.2514/6.2019-1485

Cited by 3 publications

(11 citation statements)

References 17 publications

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“…[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: 94%

“…These four parameters will from here on be referred to as the -parameters. Visualizations of the effect of varying these parameters on and can be found in previous research [19,21]. Because of Eq.…”

Section: A Kirchoff's Theory Of Flow Separationmentioning

confidence: 99%

“…Before that is achieved, there are still many steps to take. The current stall model is only validated at an altitude of 5,500 meters and has so far only been used for a single pilot-in-the-loop experiment [19]. Regarding the research efforts that still are to be done, three major considerations are discussed here.…”

Section: Towards a Full-envelope Stall Modelmentioning

confidence: 99%

“…These metrics might overlook properties of the model that are vital for a good simulator experience, or conversely: they might severely penalize some errors that are irrelevant for the intended purpose. Hence, early simulator experiments as described in [19] will help in steering the research focus to the most relevant issues.…”

Section: Towards a Full-envelope Stall Modelmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: Introductionmentioning

confidence: 94%

Section: A Kirchoff's Theory Of Flow Separationmentioning

confidence: 99%

Section: Towards a Full-envelope Stall Modelmentioning

confidence: 99%

Section: Towards a Full-envelope Stall Modelmentioning

confidence: 99%

See 2 more Smart Citations

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

Self Cite

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“…For measuring the JND thresholds, use was made of the same experimental paradigm as described in Ref. 16, where participating pilots experienced simulated symmetrical quasi-steady stall maneuvers at an altitude of 5,500 m (18,000 ft), induced with a 1 kt/s deceleration into the stall, as an observer. Through a subjective staircase procedure, consisting of repeated pairwise comparisons of the stall with our baseline buffet model and a stall with adjustments to the buffet model parameters, the JND thresholds for these parameter variations were determined.…”

Section: Introductionmentioning

confidence: 99%

Just Noticeable Differences for Variations in Quasi-Steady Stall Buffet Model Parameters

Imbrechts

Visser

Pool

2022

AIAA SCITECH 2022 Forum

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To gain more insight into human sensitivity to variations in simulated stall buffets, Just Noticeable Difference (JND) thresholds were estimated using a passive human-in-the-loop flight simulator experiment. Using an in-house developed flow separation-based stall and buffet model of the Cessna Citation II, JND thresholds were determined for the model's buffet characteristic frequency parameter 𝜔 0 and the buffet onset threshold parameter 𝑋 𝑡 ℎ𝑟 𝑒𝑠 for the vertical stall buffet only. With a subjective yes/no 1-up/1-down staircase procedure that uses repeated pairwise comparisons of quasi-steady symmetric stall simulations (where one is a stall with the baseline buffet model and the other one has an offset buffet parameter), upper and lower JND thresholds were measured from 21 pilots. The experiment results show that the pilots noticed the differences in simulated buffet dynamics at comparably similar percentage-wise offsets for 𝑋 𝑡 ℎ𝑟 𝑒𝑠 and 𝜔 0 with respect to the baseline parameter values. The maximum observed JND thresholds did not exceed 30-35% across all experiment conditions, indicating that pilots are fairly sensitive to even small offsets in the key stall buffet model parameters. Moreover, the estimated JND thresholds for 𝜔 0 are in agreement with the ±2 Hz tolerance currently used in stall buffet simulation qualification standards. However, for 𝑋 𝑡 ℎ𝑟 𝑒𝑠 , the results show that human pilots already notice differences in stall buffet onset characteristics well before the maximum allowed tolerance (±2.0°angle of attack) is reached, which suggests that stricter tolerances on simulated buffet onsets for quasi-steady symmetric stalls may help to further enhance stall training in simulators. NomenclatureRoman Symbols 𝑎 1 Stall abruptness parameter, -𝑎 𝑦 Lateral stall buffet acceleration, 𝑚/𝑠 2 𝑎 𝑧 Vertical stall buffet acceleration,𝑚/𝑠 2 𝐶 𝐿 Lift coefficient, -𝐻 0 Buffet shaping filter gain, -𝐾 {𝑞, 𝑥,𝑧 } Motion filter gain in pitch, surge and heave, -𝑄 0 Buffet shaping filter quality factor, -𝑋 Flow separation point, -𝑋 𝑡 ℎ𝑟 𝑒𝑠 Buffet onset threshold on 𝑋, -Greek Symbols 𝛼 Angle of attack, rad or deg 𝛼 * Angle of attack for which 𝑋 = 0.5, rad 𝜁 {𝑞,𝑥,𝑧 } Motion filter damping coefficient in pitch, surge and heave, -𝜏 1 Lag time constant of flow separation point 𝑋, s 𝜏 2 Hysteresis time constant of flow separation point 𝑋, s 𝜔 0 Buffet shaping filter natural frequency, rad/s 𝜔 𝑏 {𝑞, 𝑥,𝑧} Motion filter break frequency in pitch, surge and heave, rad/s 𝜔 𝑛 {𝑞, 𝑥,𝑧} Motion filter natural frequency in pitch, surge and heave, rad/s

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Identification and Modeling Method of Longitudinal Stall Aerodynamic Parameters of Civil Aircraft Based on Improved Kirchhoff Stall Aerodynamic Model

Wang

Zhao

et al. 2023

Aerospace

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The stall aerodynamic model based on Kirchhoff’s theory of flow separation is widely used in the identification and modeling of stall aerodynamic parameters. However, it has two defects. First, its model structure is significantly different from the pre-stall model used for a small attack angle, meaning the identification results cannot be combined with the pre-stall model to form the full flight envelope model. Second, the pitching moment model, which is used in conjunction with the Kirchhoff lift model, cannot accurately describe the aircraft stall pitching moment characteristics. To ensure the compatibility of the two models, this paper proposes a method to determine some unknown parameters in the stall model. The mechanism for the pitching moment generation of the aircraft stall is analyzed, and two high-order correction terms are added to the pitching moment model to better describe the longitudinal stall aerodynamic characteristics. Based on the identification of aerodynamic parameters, a longitudinal stall aerodynamic modeling method used for the aircraft stall process is developed. The identification and simulation validation results based on the quasi-steady stall flight data of a civil aircraft show that the improved stall model can accurately describe the quasi-steady stall pitching moment. The established stall aerodynamic model can accurately characterize the longitudinal quasi-steady stall aerodynamic characteristics of aircraft under different stall degrees.

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Subjective Noticeability of Variations in Quasi-Steady Aerodynamic Stall Dynamics

Cited by 3 publications

References 17 publications

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

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

Just Noticeable Differences for Variations in Quasi-Steady Stall Buffet Model Parameters

Identification and Modeling Method of Longitudinal Stall Aerodynamic Parameters of Civil Aircraft Based on Improved Kirchhoff Stall Aerodynamic Model

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