Modeling of Passive Pilot, Pilot Seat, and Inceptor for Aircraft-Pilot-Coupling (APC) Induced Oscillation Investigations
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-0337.vid Aviation is evolving in the direction of increasing the usage of composite material and reduction of structural weight for civil and business jets, resulting in increased aircraft flexibility. The increased flexibility leads to an impingement on the bandwidth of the pilot biodynamics and the flight control resulting from the structural modes of vibration of the aircraft, which gives rise to Aircraft-Pilot-Coupling (APC). APC is a sustained oscillation resulting from a coupling between the pilot dynamics and the aircraft structural response while holding the inceptor, which can lead to passenger discomfort and in worst case scenario, an aircraft accident. The presented research project aims to develop the processes and tools required to predict and determine the margins of APC events using desktop simulation models. A developed lumped-discrete hybrid biomechanical model of the pilot, as well as a simplified lumped model of the seat and inceptor characteristics, are used to obtain their respective transfer functions and the transmissibility from the seat acceleration to the inceptor response. The transfer functions will then be integrated within a generic business aircraft aeroservoelastic model to form an Aircraft-Pilot-System (APS). The APS will be used to conduct open- and closed-loop stability analysis to identify APC conditions and their margins. Parametric studies representative of variations in pilot physical characteristics, aircraft loading, and flight conditions to identify the contribution of the aforementioned variations to APC are conducted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".