Measurement of Temperature and Soot (KL) Distributions in Spray Flames of Diesel-Butanol Blends by Two-Color Method Using High-Speed RGB Video Camera
Bibliographic record
Abstract
Taking advantages of high speed RGB video cameras, the two-color method can be implemented with a relatively simple setup to obtain the temporal development of the two dimensional temperature and soot (KL) distributions in a reacting diesel jet. However, several issues such as the selection of the two wavelengths, the role of bandpass filters, and the proper optical settings, etc. should be known to obtain a reliable measurement. This paper, at first, discusses about the uncertainties in the measurement of temperature and KL distributions in the diesel flame by the two-color method using the high speed RGB video camera. Since n-butanol, as an alternative renewable fuel, has the potential application in diesel engines, the characteristic of spray combustion of diesel-butanol blends under the diesel-like ambient conditions in a pre-burning constant-volume combustion chamber is studied. The Red and Blue channels of the high speed video camera are chosen for the two-color imaging because their response curve has a much smaller overlap. When adding the n-butanol into the neat diesel, the experimental result shows that the flame has a lower integral level for both flame temperature and soot density than neat diesel flame, owing to both increases in the fuel-borne oxygen and elongated ignition delays. In order to study the effect of LTC (Low Temperature Combustion), the ambient O2 concentration is set at four levels: 21%, 18%, 15% and 12%. The results show that with the decrease of O2 concentration, the flame temperature has a dropping tendency, while the KL (soot density) factor has a complicated behavior as a result of soot oxidation rate variation.
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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".