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 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) | 0.000 | 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".