Optimization of Compact Non-Fibrous Silencer for the Control of Compressor Noise
Bibliographic record
Abstract
The concept of IC engine downsizing is a well-adapted industry standard, enabling better fuel conversion efficiency and the reduction of tailpipe emissions. This is achieved by utilizing different type of superchargers. As a consequence, the additional charger noise emission, at the IC engine inlet, can become a problem. In order to address such problem, the authors of this work have recently proposed a novel dissipative silencer for effective and robust noise control of the compressor. Essentially, it realizes an optimal flow channel impedance, referred to as the Cremer impedance. This is achieved by means of a straight flow channel with a locally reacting wall consisting of air cavities covered by an acoustic resistance, e.g., a micro-perforated panel (MPP). In this paper, an improved optimization method of this silencer is presented. The classical Cremer impedance model is modified to account for mean flow dependence of the optimal wave number. This modified model leads to significantly different impedance values compared to the classical model and consequently, the high damping of the classical model (hundreds of dB/m) is further increased. Moreover, the modeling herein, is performed by solving the convective wave equation, vital for accounting mean flow effects. The presented model is finally validated by experimental results included in the paper.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".