Ducted Fuel Injection: A Numerical Soot-Targeted Duct Geometry Optimization
Bibliographic record
Abstract
Ducted Fuel Injection (DFI) is a recently developed concept to curtail soot formation in diesel flames and based on fuel injection along the axis of a small cylindrical pipe within the combustion chamber, enhancing mixture preparation upstream the autoignition zone. Experimental observations have shown a remarkable DFI effectiveness in soot mitigation; however, the mechanisms enabled by duct adoption are not yet fully clear, especially when different duct geometries are considered. This article proposes an experiment-simulation coupled approach for the analysis of DFI in a constant volume vessel, operating in both non-reacting and reacting conditions. In particular, a previously calibrated three-dimensional computational fluid dynamics (3D-CFD) spray model was further validated against experimental liquid penetration considering different duct geometries, proving its reliability for testing duct geometrical variations. Afterward, the validated spray model was employed to investigate the influence of the main geometrical features (stand-off distance, duct length and diameter, inlet and outlet shape) on the ducted spray characteristics and on the combustion and emissions formation processes. The reduction of both stand-off distance and duct length, up to the flow area limit in which the air entrainment is almost zeroed, leads to the best soot mitigation performance. Furthermore, a chamfer at the duct inlet enhances the duct adoption benefits due to improved air entrainment, confirming previous experimental observations. Thereby, it was possible to figure out an optimal duct configuration in terms of soot emission minimization by evaluating air entrainment and turbulent mixing at duct inlet and outlet, and flame lift-off length, achieving a soot mass curtailing of more than an order of magnitude.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".