Experimental and Numerical Dynamic Investigation of a Swirling Jet: Application to Improve the Efficiency of Air Diffusion in an Occupied Zone
Bibliographic record
Abstract
When reducing the energy prerequisites of buildings, the correct distribution of fresh air flows injected into the living rooms poses a problem. If the problem of mixing the injected air in the ambient air is not effectively solved, there will be a strong deterioration in air quality and comfort. In this research, a new design of swirling diffuser is investigated experimentally and numerically using large eddy simulations. The influence of fins is studied for the improvement of air diffusion and jet mixing with ambient air. The study was carried out for a fins angle of 30° with the jet's axis and 60° with the blowing orifice's plane with the condition of uniform heat flux of the air. The working fluid used is air. It has been validated that using fins leads to a greater spreading of the jet and good air mixing compared to those obtained from smooth tubes (circular nozzle). To enhance the accuracy of the turbulence models' predictions, three turbulence models are tested: the wall-adapting local eddy-viscosity turbulence model (LES/WALEVM), Smagorinski-Lilly (LES/S-LM) model and the kinetic-energy transport model (LES/K-ETM). It is worth highlighting that the LES/K-ETM model is well established in the prediction of swirling flows, which have been successfully compared with experimental results.
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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 | 0.000 | 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".