A Study of the Influence of Nozzle Orifice Geometries on Fuel Evaporation using Laser-Induced Exciplex Fluorescence
Bibliographic record
Abstract
Projected stringent emissions legislation will make tough demands on engine development. For diesel engines, in which combustion and emissions formation are governed by the spray formation and mixing processes, fuel injection plays a major role in the future development of cleaner engines. It is therefore important to study the fundamental features of the fuel injection process. In an engine the fuel is injected at high pressure into a pressurized and hot environment of air, which causes droplet formation and fuel evaporation. The injected fuel then forms a gaseous phase surrounding the liquid phase. The amount of evaporated fuel in relation to the total amount of injected fuel is of importance for engine performance, i.e. ignition delay and mixing rate. In this paper, the fraction of evaporated fuel was determined for sprays, using different orifice diameters ranging from 0.100 mm up to 0.227 mm, with the aid of a high-pressure spray chamber. The conicity of the orifice was also varied. Three injection pressures and two chamber temperatures were also used. The amount of evaporated fuel was determined by laser-induced exciplex fluorescence measurement of the gaseous and liquid phases of the fuel spray. The measurement of the phases consists of time-resolved, two-dimensional images. The fuel in this study consisted of a mixture of 1-cyanonaphthalene, N,N-dibutylaniline and n-dodecane. The fuel was excited by a laser wavelength of 355 nm. The results show a strong reduction of fuel penetration for one of the conical orifices. It was also found that both increased injection pressure and reduced orifice diameter reduce the liquid volume fraction of the spray, i.e. the gas volume fraction increases.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".