The Influence of the Acoustic Transfer Functions on the Estimated Interior Noise from an Electric Rear Axle Drive
Why this work is in the frame
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Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">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 12<sup>th</sup> 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.</div></div>
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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 | 0.001 | 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 it