Experimental study of the flow downstream of a 3 x 3 square matrix of swirling droplet laden air jets
Bibliographic record
Abstract
Due to the small fuel and air channels of the nozzle used, seeding the airflow with solid particles to perform PIV experiments was not an option. Instead liquid was injected in the air stream and a MATLAB routine was developed to identify the size of the seeding drops and filter out those that were larger than 3 x 3 pixels. The key to reducing combustion emissions such as nitrogen oxides (NOx) and carbon monoxide (CO) is fast and thorough mixing of fuel and air. The aim of multi-point lean direct injection (LDI) technique is to improve mixing and atomization by injecting fuel in small amounts through multiple injection ports. Multiple small injectors are used instead of a main injector introducing fuel and air streams in the combustion chamber. Various combustion studies reported in the literature showed that multi-point lean direct injection (LDI) nozzles had lower emissions compared to other ordinary nozzles. Besides these overall combustion performance studies there are no detailed investigations of the nozzle flow and the role of mixing and atomization in achieving the low emission performance. The objectives of this study were: (1) Characterize turbulence and spray fields; and (2) Investigate the effect of lateral jet interaction on spray quality and mixing. This work is an experimental investigation of the flow downstream of a multi-point LDI nozzle. The nozzle is a 3 x 3 square matrix of 9 small swirling air jets with fuel inject at the center of each jet. The design swirl number was 0.8 and the Reynolds number of the flow was 40 000. The experiments were conducted under non-burning conditions at atmospheric conditions. Particle image velocimetry (PIV) and Phase Doppler particle anemometry (PDPA) were used to map the velocity and spray properties. The investigation covered the region within the first 18 jet diameters downstream of the nozzle exit plane. The results showed that: (1) Lateral jet interaction slows the spreading rate of the jets and improves turbulence, thus mixing. The improved turbulence is due mostly to the opposing direction of the tangential component; (2) Increased spray number density and droplets relative velocity in the matrix favors coalescence; (3) Although smaller injectors achieve better atomization than ordinary nozzles, jet-jet interaction occurring when matrices of small jets are used offsets this gain in atomization quality; and (4) Results suggest that the emission reduction is due to improved mixing.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".