Intake noise cancellation using a manifold bridging technique
Bibliographic record
Abstract
Automobile manufacturers have expended considerable efforts to attenuate the many noise sources perceived within the passenger compartment with varying degrees of success. Given that these dominant noise sources have been attenuated, induction noise has become more noticeable. The present study investigates the feasibility of using a non-conventional noise cancellation technique. The investigation has attempted to improve the acoustic performance of the induction system by introducing a bridge between the exhaust and intake manifolds. The effectiveness of such a technique is investigated using Ricardo WAVE, a computational engine simulation technique that uses a one-dimensional finite-difference formulation. Graphical results using 1/12 th octave frequency spectra and three dimensional colour maps of both an unmodified and abridged engine are presented for both steady state and transient engine cases. A sound quality analysis is also presented using the psychoacoustic metrics of Loudness, Fluctuation Strength and Articulation Index. While a reduction in overall sound level was achieved, an additional benefit of this technique proved to be in the realized sound quality of the induction noise with the implementation of the manifold bridge. This investigation continues with verification of the theoretical model to experimental measurements on a dynamometer.
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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 | 0.000 | 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".