Optimization of Compact Non-Fibrous Silencer for the Control of Compressor Noise
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">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).</div><div class="htmlview paragraph">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.</div></div>
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".