Species correlation measurements in turbulent flare plumes: considerations for field measurements
Bibliographic record
Abstract
Abstract. Field measurement of flare emissions in turbulent flare plumes is an important and complex challenge. Incomplete combustion from these processes results in emissions of black carbon, unburnt fuels (methane), CO2, and other pollutants. Many field measurement approaches necessarily assume that combustion species are spatially and/or temporally correlated in the plume, such that simple species ratios can be used to close a carbon balance to calculate species emission factors and flare conversion efficiency. This study examines the veracity of this assumption and the associated implications for measurement uncertainty. A novel tunable diode laser absorption spectroscopy (TDLAS) system is used to measure the correlation between H2O and black carbon (BC) volume fractions in the plumes of a vertical, turbulent, non-premixed, buoyancy-driven lab-scale gas flare. Experiments reveal that instantaneous, path-averaged concentrations of BC and H2O can vary independently and are not necessarily well correlated over short time intervals. The scatter in the BC/H2O ratio along a path through the plume was well beyond that which could be attributed to measurement uncertainty and was asymmetrically distributed about the mean. Consistent with previous field observations, this positive skewness toward higher BC/H2O ratios implies short, localized, and infrequent bursts of high BC production that are not well correlated with H2O. This demonstrates that the common assumption of fixed species ratios is not universally valid, and measurements based on limited samples, short sampling times, and/or limited spatial coverage of the plume could be subject to potentially large added uncertainty. For BC emission measurements, the positive skewness of the BC/H2O ratio also suggests that results from small numbers of samples are more likely to be biased low. However, a bootstrap analysis of the results shows how these issues can be avoided with sufficient sample size and provides initial guidance for creating sampling protocols for future field measurements using analogous path-averaged techniques.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".