Predicting Flow-Induced Noise Based on an Improved Four-Dimensional Acoustic Analogy Model and Multi-Domain Feature Analysis
Bibliographic record
Abstract
Flow-induced noise issues are widely present in practical engineering fields.Accurate prediction of noise signals is fundamental to studying the mechanism of noise generation and seeking effective noise suppression methods.Complete acoustic field information often includes both acoustic pressure and velocity vectors.However, the classic acoustic analogy theory can only consider the feature distribution of acoustic pressure.This study starts from the dimensionless Navier-Stokes equations followed by fluid motion and, with the concept of electromagnetic analogy, introduces a vector form of the fluctuation equation that includes density perturbations and velocities in three directions.By choosing the permeable integral surface surrounding the object as the sound source surface, this study further analyzes the composition of the volume source term and extract the complete load source term, proposing the time-domain integral analytical formula T4DC and the frequencydomain integral formula F4DC.Numerical predictions for stationary dipoles and rotating monopoles are carried out in the time domain, frequency domain, and spatial domain.The numerical results show that the time-domain and frequency-domain noise obtained by this method can be consistent with the analytical solution, while the method of Dunn has a significant difference from the analytical solution, especially for dipole noise distribution.Compared with the accurate solution, the acoustic velocity amplitude error obtained by Dunn's method reached more than 35% at m=1 frequency, fully demonstrating that our method can accurately predict far-field acoustic pressure and velocity vectors.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".