Soot Optical Diagnostic Development and Application to Turbulent Non-Premixed Buoyant Flames
Bibliographic record
Abstract
This thesis details the development and application of laser-based optical diagnostics capable of measuring soot volume fraction, mean aggregate radius of gyration and primary particle size in turbulent flames.Research focussed on techniques suitable for making instantaneous measurements in turbulent flames, initially investigating a 2D auto-compensating laser-induced incandescence (2D-AC-LII) diagnostic prior to developing a unique combined AC-LII and elastic light scattering (ELS) system, which was used in subsequent experiments.Comprehensive Monte Carlo-based uncertainty analyses performed on each system indicated that the imprecise knowledge of soot properties was the largest source of uncertainty for all measurements.Use of a newly-developed ELS calibration method allowed a reduction in uncertainties resulting from calibration and instrument noise.The AC-LII/ELS technique was applied to turbulent buoyant non-premixed flames relevant to solution gas flares used throughout the upstream energy industry, resulting in several important insights.First, the results demonstrate that decreases in soot volume fraction seen near the flame tip are attributable to increased flame intermittency rather than decreases in soot volume fraction within soot-bearing structures, providing support for a recent suggestion from the literature that this could occur in momentum-dominated flames and further extending it to the buoyancydominated flames studied here.Secondly, and contrary to a suggestion in the I would like to thank Professor Matthew Johnson for the advice, support, and motivation he offered during my doctoral studies.He is an inspiring role model & leader and an all-around good person.I am fortunate to have had the opportunity to work with him for so long.I would like to thank Dr. Kevin
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".