An Experimental Investigation on Spray Characteristics of Waste Cooking Oil, Jatropha, and Karanja Biodiesels in a Constant Volume Combustion Chamber
Bibliographic record
Abstract
In this study, macroscopic spray characteristics of Waste cooking oil (WCO), Jatropha oil, Karanja oil based biodiesels and baseline diesel were compared under simulated engine operating condition in a constant volume spray chamber (CVSC). The high pressure and high temperature ambient conditions of a typical diesel engine were simulated in the CVSC by performing pre-ignition before the fuel injection. The spray imaging was conducted under absence of oxygen in order to prevent the fuels from igniting. The ambient pressure and temperature for non-evaporating condition were 3 MPa and 300 K. Meanwhile, the spray tests were performed under the ambient pressure and temperature of 4.17 MPa and 804 K under evaporating condition. The fuels were injected by a common-rail injection system with injection pressure of 80 MPa. High speed Mie-scattering technique was employed to visualize the evaporating sprays. Liquid tip penetration length, spray cone angle and spray area were determined from captured images. The equivalence ratio along the axial direction of the spray was also calculated based on mathematical correlations. Results showed that biodiesels had longer spray tip penetration length and narrow spray cone angle than those of baseline diesel. Amongst the biodiesels, Jatropha and Karanja biodiesels exhibited longer spray tip penetration length and narrower spray angle than those of Waste cooking oil biodiesel. On the other hand, baseline diesel showed much shorter liquid tip penetration length and faster evaporation process than biodiesels. The reason is that higher density and viscosity of biodiesels attenuated the fuel atomization and evaporation process. However, despite deteriorated atomization characteristics, biodiesels showed lower equivalence ratio than baseline diesel due to inherent oxygen content in the fuel molecules.
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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 | 0.000 |
| 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".