Linearization of Aircraft Landing Equations of Motion with Airframe Flexibility Effects
Bibliographic record
Abstract
The conventional approach in aircraft landing loads analysis, such as for shock absorber development, is using a nonlinear set of equations and a modal representation of the airframe. For preliminary shock absorber design studies, a linearized set of equations may provide a highly efficient simulation method to limit the parameter space of linear shock absorber models. This article develops a set of linearized equations of motion to simulate the landing touchdown event while capturing airframe flexibility effects using a transfer function. The linearized flexible model demonstrates the ability to generally capture flexibility effects and output responses of interest with a significantly reduced simulation time compared to both fully flexible and nonlinear reduced-order models. The linearization of a Fiala tire model is accomplished by scaling the longitudinal tire stiffness such that the peak tire drag force matches that of the nonlinear model, and the vertical tire stiffness is obtained from a linear regression of a nonlinear vertical force versus deflection curve through an expected range of tire deflection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".