Investigation of acoustic and geometric effects on the sonoreactor performance
Bibliographic record
Abstract
In this work, three design configurations of a sonoreactor are considered under various operating conditions, and the acoustic characteristics during water sonication are investigated while using an immersed-type ultrasonic flat transducer probe in a sonoreactor model. Numerical models are also developed to simulate the sonication process, and they are successfully validated and compared with available data in the literature. Several sets of numerical investigations are conducted using the finite-element method and solved by the computational acoustics module in the COMSOL Multiphysics. The effects of the acoustical and geometrical parameters are investigated, analyzed, and reported, including the ultrasonic frequency, acoustic intensity, and scaling-up the reactor. The present study includes a parametric investigation examining the change of the ultrasonic frequency, intensity, and probe immersion depth on the performance. The results of the parametric study show that the highest cavitation energy corresponds to the maximum magnitude of negative pressure that takes place in the range of 60–80 kHz. The cavitation energy analyses are conducted under the conditions of 20 kHz of frequency and at 36 W input power. It is found that the cavitation energy of 15.87 W could produce 2.98 × 10−10 mol/J of sonochemical efficiency. In addition, the effect of altering the transducer probe depth changes the acoustic pressure field insignificantly. Furthermore, a recommendation is made to improve the sonochemical efficiency by introducing more considerable ultrasound input power while operating the sonoreactor at an ultrasonic frequency lower than 60 kHz. The results presented in this paper provide a comprehensive assessment of different sonoreactors and the feasibility of scaling-up their production rate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".