Study on the Effects of Nozzle Fuel Spray Pattern on Cetane Number Measurement as Determined in the Ignition Quality Tester (IQT™)
Bibliographic record
Abstract
The spatial distribution of drops in sprays is critical to the performance of many atomization systems, including diesel engine injectors, gas turbine injectors, spray coating systems, and furnace burners. This paper presents an investigation of the effects of nozzle fuel spray pattern on the measurement of cetane number as determined in the Ignition Quality Tester (IQT™). To determine the effects of spray pattern on cetane number, an optical spray pattern test rig was developed. The test rig was comprised of the IQT™ fuel injection system and data acquisition system, and the Optical Spray Pattern Analyzer from Nexum. Several nozzles from different manufacturers where chosen for this study. Cetane numbers for a diesel reference fuel were obtained for each of the test nozzles in the IQT™ and where compared qualitatively to spray pattern images for each of the test nozzles observed from the spray pattern test rig. As a result, a typical evolution of the spray pattern across the main injection was identified; however the spray pattern did not appear to have any noticeable effect on the cetane number as measured in the IQT™.
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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 | 0.006 |
| 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".