An experimental investigation of the use of an outlet silencer to quiet ejectors
Bibliographic record
Abstract
Ejectors are simple fluid movers and mixers used in a range of industries; however, the attractiveness of their simplicity can be offset by high levels of noise generation. This work experimentally investigates the use of a silencer affixed to the outlet of a subsonic air–air ejector as a means of quieting the ejector. An emphasis is placed on finding a silencer design which has a minimal impact on the mass flow rate exhausting from the ejector (pumping performance). This paper discusses the results of 10 different silencer designs, tested in an attempt to further understand noise generation mechanisms and to find a practical method to reduce the noise of ejectors. It is found that the placement of a perforated cone at the mid-length of the silencer is the only solution tested which provides a significant acoustic advantage with only a small drop in pumping performance. Other solutions tested provide either no acoustic advantage or have too great of a reduction in pumping performance. It is found that the size and shape of the ejector can be designed in such a way to reduce the overlap of natural modes and thus the overall noise levels of the ejector caused by high levels of resonance. The use of acoustic foam to dampen acoustic natural modes proves that the natural modes of the ejector are a significant contributor to the overall noise levels.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".