Flow, mixing, and heat transfer in fluidic oscillators
Bibliographic record
Abstract
Abstract There is an increasing emphasis on process intensification and development of compact, intensified reactors and separators in recent years. Significant efforts are being made to develop such intensified reactors and separators without any moving parts. Some of the recent research studies have proven that a liquid‐liquid extractor based on the Coanda effect and feedback oscillations exhibit excellent mixing and liquid‐liquid contacting. These fluidic oscillators can potentially be used for a variety of other multiphase reactions and systems demanding enhanced mixing and heat and mass transfer. In this work, we have computationally investigated flow, mixing, and heat transfer in fluidic oscillators based on the Coanda effect. Available information on flow and mixing in fluidic oscillators was critically reviewed and key gaps in the available knowledge with respect to the design and optimization of fluidic oscillators were identified. Computational flow models were developed to characterize key flow features like unsteady flows, secondary vortices, and internal recirculation over a range of Reynolds number (Re = 90–1538) for three different oscillator designs. Systematic numerical studies were carried out to quantify different flow regimes, oscillations, and the influence of key geometric parameters on flow, mixing, and heat transfer. Simulated results were critically analyzed and are presented in the form of dimensionless numbers. The approach and results presented in this work will provide useful insights and a systematic basis for extending the applications of the Coanda‐based feedback oscillatory devices for a wide range of engineering applications.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".