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 associated with an elevated risk of cardiovascular disease, hearing loss, and sleep deprivation for regular air travellers and crew. At cruise conditions, the noise is primarily caused by random pressure fluctuations in the aircraft turbulent boundary layer, and the search for an accurate empirical model to predict these fluctuations is an important ongoing research topic. 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.
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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 | 0.001 | 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".