Predicting Far-Field Noise Generated by a Landing Gear Using Multiple Two-Dimensional Simulations
Bibliographic record
Abstract
In this paper, a new approach is proposed to predict the far-field noise of a landing gear (LG) based on near-field flow data obtained from multiple two-dimensional (2D) simulations. The LG consists of many bluff bodies with various shapes and sizes. The analysis begins with dividing the LG structure into multiple 2D cross-sections (C-Ss) representing different configurations. The C-Ss locations are selected based on the number of components, sizes, and geometric complexities. The 2D Computational Fluid Dynamics (CFD) analysis for each C-S is carried out first to obtain the acoustic source data. The Ffowcs Williams and Hawkings acoustic analogy (FW-H) is then used to predict the far-field noise. To compensate for the third dimension, a source correlation length (SCL) is assumed based on a perfectly correlated flow. The overall noise of the LG is calculated as the incoherent sum of the predicted noise from all C-Ss. Flow over a circular cylinder is then studied to examine the effect of the 2D CFD results on the predicted noise. The results are in good agreement with reported experimental and numerical data. However, the Strouhal number (St) is over-predicted. The proposed approach provides a reasonable estimation of the LG far-field noise at a low computational cost. Thus, it has the potential to be used as a quick tool to predict the far-field noise from an LG during the design stage.
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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".