An Efficient Far‐Field Noise Prediction Framework for the Next Generation of Aircraft Landing Gear Designs
Bibliographic record
Abstract
The numerical simulations of a full three-dimensional (3D) landing gear (LG) model are computationally expensive. These simulations are not suitable for quick estimate during the design stage. In this research, a physics-based approach of multiple two-dimensional (2D) simulations is proposed and validated with the available 3D numerical and experimental data. This novel approach is more effective and efficient in compromising between the computational cost and accuracy of the far-field noise calculation of such complex structure as the LG. The prediction of 3D LG noise relies on the results of multiple 2D near-field flow simulations. The 3D LG model is divided into different 2D cross-sections located at various locations along the longitudinal axis. Using Ffowcs Williams and Hawkings acoustic analogy, far-field noise is calculated. For the compensation of missing near-field data along the span, a source correlation length is considered. The proposed method is applied to a two-wheel nose landing gear to validate the model. Overall, the results are within a reasonable accuracy compared with the experimental and 3D numerical data using low computational cost. Therefore, the proposed method has the potential to be used as an effective framework for assessing different LG designs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".