Dynamic response analysis of the aircraft-snow runway coupling system during taxiing
Bibliographic record
Abstract
Compared to traditional runways, compacted snow runways exhibit a reduced surface smoothness and modulus, leading to intensified dynamic responses during aircraft taxiing. This study establishes an aircraft-snow runway interaction model using ANSYS software to quantitatively analyze the effects of the runway wavelength, amplitude, modulus, and aircraft taxiing speed on system dynamics. The results of the study are largely in agreement with the results computed by the ADAMS dynamic analysis software. Specifically, as the wavelength-to-wheelbase ratio increases, the peak acceleration of the landing gear and runway surface decrease rapidly and then gradually stabilise, while the peak runway strain first increases rapidly and then stabilises. As the amplitude increases, the peak acceleration of the landing gear and runway surface continuously increase. Furthermore, the peak vertical strain of the runway decreases. As the runway modulus increases, the peak acceleration of both the landing gear and the runway, as well as the peak runway strain, continuously decrease. With increasing aircraft speed from 2 to 30 m/s, the peak landing gear acceleration rises sharply, while the peak runway acceleration increases correspondingly. The findings of this study offer valuable theoretical guidance for the design and construction of snow-covered runways.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".