Influence of a perpendicular liquid flow on a cleaning process using 20 kHz ultrasound: Characterization of the agitation at vicinity of the surface opposite to the transducer
Bibliographic record
Abstract
Abstract This work is part of a project consisting of the development of an automatic cleaning station for the immersed part of boats. This self‐service station combines ultrasound for washing with a specific water treatment. Since, in this case, displacement of the transducers plus suction of the dirt removed induce circulation, we need to measure the ultrasound activity which reaches the surface despite the disruptions. The goal of this work is to quantify this ultrasound activity. For this purpose, a specific lab‐scale equipment was designed and built. Two methods were implemented for quantification of the ultrasound activity: Particle Image Velocimetry and electrochemical mass transfer measurements. From electrochemical measurements, a parietal velocity was calculated and found to be consistent with velocities obtained from both flow rate and PIV measurements in silent conditions. Moreover, it was found that, even in the presence of a liquid flow perpendicular to the main direction of propagation of ultrasound, contribution of ultrasound to the agitation on the opposite wall remained noticeable. Nevertheless, results showed that the main activity was concentrated in the area close to the transducer. Thus, to maximize the cleaning process, small distances must be maintained between the cleaning tool and the boat hull.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".