Derivation of an Empirical Model to Estimate the Power Spectral Density of Turbulent Flow Wall Pressure Fluctuations Using Machine Learning Regression Techniques
Bibliographic record
Abstract
Aircraft cabin noise is a significant contributor to health risks in regular air travellers and crew, being associated with an elevated risk of cardiovascular disease, hearing loss, and sleep deprivation.At cruise conditions, the noise is primarily caused by random pressure fluctuations in the aircraft turbulent boundary layer, and as such the search for an accurate empirical model to predict these fluctuations is an important ongoing research topic.The earliest models by Lowson and Robertson were derived by simplifying and solving the governing Reynolds-averaged Navier-Stokes equations for the fluctuating pressure term, while subsequent models were usually derived via the application of statistical and mathematical techniques to simplify earlier models, or by making appropriate modifications to address apparent shortcomings.However, past research has yet to yield a universally applicable model, with most only being accurate near the Mach and Reynolds numbers they were designed for.However, more recent work by Dominique demonstrated that artificial neural networking, a type of machine learning technique, could potentially produce a model that was accurate under most flight conditions.This thesis extends Dominique's research by creating a new equation via the application of a different machine learning technique (nonlinear least squares regression analysis) and a novel iterative process to develop the model form.The resulting equation was accurate at most Reynolds numbers and low airspeeds (approximately 11 m/s), though more outside data will be needed to fully understand its accuracy and shortcomings.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".