Detection of Obstructions in Automotive Manifolds by Aeroacoustic Means<xref ref-type="fn" rid="FN1">*</xref>
Bibliographic record
Abstract
An aeroacoustic method has been developed to detect obstructions in automotive manifolds. The method has been tested extensively in the laboratory and on production lines of exhaust manifolds and has shown superior performance compared to other methods currently used in industry. It provides the most reliable diagnosis of small-size obstructions. Moreover, the method is very simple, provides fast results, is easy to implement into production lines, has low maintenance cost, and is insensitive to acceptable manufacturing tolerances, such as the inner surface roughness of the manifold. The developed method is based on the phenomenon of sound generation when the flow passes over obstructions or protrusions from the inner surface of a flow duct. A microphone is used to measure the noise level generated by air flow through the manifold. This noise level is compared with the averaged baseline value of unobstructed manifolds. An obstruction is detected if the noise level exceeds the baseline by a critical amount. Reliable detection is achieved only when the noise level is measured over a specific range of frequencies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".