Shape optimization of a quiet and efficient synthetic jet actuator enclosure using COMSOL Multiphysics
Bibliographic record
Abstract
Synthetic jet actuators are devices that generate a pulsatile flow through cyclic suction and discharge while maintaining a zero net mass flux. Their distinct ability to operate with no external fluid source, short response time, and compactness make them suitable for use in flow control in aircraft, thrust vectoring in jet engines, and sensor cleaning in automobiles. Due to their potential use in engineering systems, it is important to investigate and improve their performance. This study performed a shape optimization study on a streamlined enclosure for an actuator to be used as a surface-cleaning device. The main goal was to produce a compact, aerodynamic-efficient enclosure that enhances the intrinsic dipole cancellations between the sound emissions of two orifices, the actuator orifice and the enclosure orifice, with minimal impact on cleaning performance. To improve the aerodynamic efficiency of the enclosure, several steps were taken towards producing an optimized enclosure shape. A preliminary parametric study was conducted on three enclosure designs to determine the best initial design based on fluid dynamics accuracy and enclosure efficiency. Geometric and shape optimization were performed with the objective of minimizing the pressure drop, which maximizes the enclosure’s aerodynamic efficiency. Factors such as mesh construction and adaptive mesh refinement, numerical turbulence models, optimization algorithms, and the maximum displacement setting were explored to converge to the optimum enclosure design. The enclosure designs were modeled in COMSOL Multiphysics. A study was conducted to compare two turbulence models, Menter’s Shear Stress turbulence model and the Turbulence Spalart-Allmaras model, across different mesh sizes, and the Turbulence Spalart-Allmaras model had the most efficient performance. The results from the mesh study show that finer meshes improve the accuracy of the solution, and adaptive mesh refinement produces more accurate results at a low computer run time. A parameter sensitivity analysis was performed to estimate the optimum enclosure length using parametric sweep and gradient-free methods, BOBYQA, COBYLA, and Nelder-Mead, and both methods produced similar results. Shape optimization was performed using two gradient-based methods, SNOPT and IPOPT, at different maximum displacements. The overall optimum shape was obtained at 10mm maximum displacement, with an efficiency of 0.96 and a pressure drop of 108.7Pa. To validate the results, a 3-D model of the optimized shape was designed in SOLIDWORKS, and the model was printed and tested using hot wire anemometry and a microphone to determine the influence of the enclosure on the actuator’s centerline velocity and noise emissions.
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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 | 0.001 |
| 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".