Spectroscopic Measurements of Path-Averaged Species Correlations in Turbulent Flare Plumes
Bibliographic record
Abstract
Flaring is the common upstream oil and gas industry practice of disposing unwanted combustible gas in a turbulent diffusion flame open to atmosphere. Pollutant emissions from flaring remain uncertain and most measurement techniques rely on the assumption that combustion-derived species are well-mixed and therefore well-correlated in the flare plume. This thesis presents a spectroscopic measurement technique used to measure path-averaged species correlation in turbulent flare plumes to assess this assumption. Tunable diode laser absorption spectroscopy (TDLAS) and line-of-sight attenuation (LOSA) techniques are used to measure H2O and soot, respectively. The spectroscopic techniques were first validated using synthetic data generated from a large eddy simulation of a turbulent flare plume. An experimental apparatus was subsequently developed and used to measure species correlation in lab-scale turbulent flare plumes. Results suggest that the instantaneous ratio of path-averaged H2O and soot in the plume follows a skewed distribution, such that flare emission measurements based on limited transects or short-duration sampling would be subject to bias and uncertainty.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.005 | 0.001 |
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 teacher head, 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".