An Optical Diagnostic for Instantaneous Measurements of Soot Volume Fraction, Primary Particle Diameter and Mean Aggregate Radius of Gyration in Large Turbulent Flames
Bibliographic record
Abstract
This thesis details the development of an optical diagnostic capable of making simultaneous, instantaneous measurements of the soot volume fraction, soot primary particle diameter, and soot mean aggregate radius of gyration in large, turbulent, nonpremixed flames.A combination of auto-compensating laser induced incandescence and elastic light scattering was used to make the measurements.The produced optical measurement system was validated by quantifying soot within a reference co-annular laminar diffusion flame.Results agreed with the published data at the same flame conditions within precisely calculated measurement uncertainties obtained with Monte Carlo analysis.This analysis revealed that with larger optical measurement volumes the overall uncertainties are dominated by uncertainties in the optical and fractal properties of soot which are common to all optical diagnostics.The results also showed that if the optical and fractal properties are assumed to be constant, the relative uncertainties arising from measurement noise only are significantly lower.Finally, experiments were completed to investigate the minimum achievable optical measurement volume, where small volumes resulted in overall uncertainties being dominated by measurement noise.The results demonstrate that the developed soot measurement system is ready to be used to make measurements on large turbulent flames.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".