Application of Semi-Empirical Models to Low-Noise Landing Gear Configurations
Bibliographic record
Abstract
This paper presents a calibration methodology applied to a semi-empirical model to enhance the prediction of landing gear noise. This methodology is based on large experimental and numerical databases conducted by Safran Landing Systems for more than 25 years. The objective is to provide a decision-making tool to facilitate the development of low-noise practices for landing gears and the evaluation of noise generation for novel configurations. This process is part of a low-noise design methodology for landing gears. Acoustic spectra obtained from wind tunnel or flight tests are used to refine the semi-empirical model of Smith. This approach hinges on a calibration process that involves minimizing the norm between experimental spectra and those generated by the Smith model. A new formulation for the spectral function is proposed and calibrated on experimental data. The outcomes of this calibration process are used to compute low-noise configurations. The accuracy of evaluating low-noise configurations generated noise is improved using the improved Smith model. The relevance of the calibration is demonstrated for industrial landing gear architectures. The calibrated Smith model results in an improvement in predicting low-noise configurations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".