The Influence of the Acoustic Transfer Functions on the Estimated Interior Noise from an Electric Rear Axle Drive
Bibliographic record
Abstract
In the vehicle development process, targets are defined to fulfill customers' expectations on acoustic comfort. The interior complete vehicle acoustic targets can be cascaded down to system and component targets, e.g. insulation properties and source strengths. The acoustic transfer functions (ATFs) from components radiating airborne noise play a central role for the interior sound pressure levels. For hybrid vehicles fitted with an electric traction motor, the contribution of high frequency tonal components radiated from the motor housing needs to be controlled. The interior sound pressure due to an airborne motor order can be estimated by surface velocities and ATFs. This study addresses the ATFs measured from a large number of positions located around an electric rear axle drive (ERAD) and their influence on estimated interior noise. First, the magnitude variation between the individual ATFs and how it clearly can be visualized was presented. Displaying all ATFs in a color map revealed the magnitude at each geometrical location of the respective microphone. Secondly, the influence of the ATF's spatial resolution on estimated interior sound pressure was investigated. This was done for theoretical models of the stator shell source shape and also for measured surface velocities. By reducing the spatial resolution from 0.05 to 0.10 m between each microphone, the difference in noise contribution is typically within three decibels with a 12th octave smoothing filter applied to the narrow-band data. The findings from this work provide insight in the risks of compromising with the number of ATF's needed for contribution analysis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".