The Influence of Liquid Viscosity on Atomized Fuel Mean Droplet Size Determined by the Laser Diffraction Method
Bibliographic record
Abstract
Abstract The article presents the impact of viscosity of fuel on its atomization, which constitutes an important element of controlling the quality of the fuel-air mixture in compression ignition and direct injection engines. An experiment has been made using a three-hole atomizer of an engine with nominal power of 110 kW and revolutions n = 2800 min−1. Fuel was delivered by a PRW-2M injection pump intended for testing injectors. Fuel was sprayed in the atmospheric air. Three petroleum product liquids used for the experiment had a viscosity of, respectively, 3.93, 16.73 and 36.41 mm2/s. The fuel droplet size in a spray was determined by the laser diffraction method by means of a Spraytec STP 5929 analyzer. The quantity adopted for comparative purposes was the Sauter Mean Diameter D32. The results confirmed that a change of fuel viscosity within the range recommended by ship engine manufacturers has a strong impact on the size of sprayed fuel droplets. Shipowners have a limited choice of low sulphur fuel grade (up to 0.1% S), which forces the engine room personnel to use currently available fuels. Depending on the supplier, marine fuels may vary in viscosity and, according to the recommendations of engine manufacturers, they do not require heating. The increase in the size of the droplets injected into the fuel combustion chamber may affect the quality of the fuel-air mixture, increase of fuel consumption and a greater content of harmful exhaust constituents.
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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 | 0.002 |
| 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".