Jet mixing optimization using a flexible nozzle, distributed actuators, and machine learning
Bibliographic record
Abstract
In this paper, we introduce the first jet nozzle allowing simultaneous shape variation and distributed active control, termed “Smart Nozzle” in the sequel. Our Smart Nozzle manipulates the jet with an adjustable flexible shape via 12 equidistant stepper motors and 12 equidistantly placed inward-pointing minijets. The mixing performance is evaluated with a 7 × 7 array of Pitot tubes at the end of the potential core. The experimental investigation is carried out in three steps. First, we perform an aerodynamic characterization of the unforced round jet flow. Second, we investigate the mixing performance under five representative nozzle geometries, including round, elliptical, triangular, squared, and hexagonal shapes. The greatest mixing area is achieved with the square shape. Third, the symmetric forcing parameters are optimized for each specified nozzle shape with a machine learning algorithm. The best mixing enhancement for a symmetric active control is obtained by the squared shape, which results in a 1.93-fold mixing area increase as compared to the unforced case. Symmetrically unconstrained forcing achieves a nearly 4.5-fold mixing area increase. The Smart Nozzle demonstrates the feasibility of novel flow control techniques that combine shape variation and active control, leveraging the capabilities of machine learning optimization algorithms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".