Shape optimization of a synthetic jet actuator enclosure for aerodynamic efficiency and noise reduction using COMSOL Multiphysics
Bibliographic record
Abstract
Synthetic jet actuators are devices that generate a pulsatile flow through cyclic suction and blowing while maintaining a zero net mass flux. Their distinct ability to operate with no external fluid source, short response time, and compactness makes them suitable for use in flow control in aircraft, thrust vectoring of jet engines, and sensor-cleaning in automobiles. However, synthetic jet actuators’ acoustic emissions are an undesirable by-product of their performance. Therefore, this study presents the design and optimization of a streamlined enclosure that enhances the actuator’s aerodynamic efficiency while reducing the radiated noise. A preliminary parametric study is conducted to determine the best initial design of the enclosure based on the enclosure efficiency calculated. Geometric and shape optimization are performed with the objective of minimizing the pressure drop (thus increasing the enclosure’s aerodynamic efficiency). The determined far-field pressure, which quantifies the radiated noise, is also investigated. This study explores several factors, such as computational mesh construction, the suitable optimization algorithm method, and the maximum displacement setting (dmax) in COMSOL Multiphysics, to converge to the optimum enclosure design. The optimum enclosure is 3-D printed and experimentally tested to validate the computational results. [Work supported by McGill University.]
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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 | 0.001 |
| 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 | 0.001 | 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".