Simplified flare combustion model for flare plume rise calculations
Bibliographic record
Abstract
Abstract The dispersion of plumes released from stacks depends on wind speed, plume emission rate, stack height, and other meteorological and stack variables. Plume rise is an important aspect of plume dispersion because it increases the apparent release height, which leads to lower ground‐level concentrations. Plume rise linked with flare combustion has received only minimal attention in the literature to date, despite its importance. This study develops a numerical model of plume rise with flare combustion based on material, heat, mass, and momentum balances. The basis of the model is a numerical plume rise model used in CALPUFF to model plume rise of large buoyant area sources, and is also used in PRIME (plume rise model enhancements), which models building downwash. The proposed model considers the reaction kinetics. The competition between CH4 and CO combustion causes a modification of the temperature profile of up to 3 % in comparison with an instantaneous reaction model. Moreover, emissivity, which plays an important role in the heat conservation equations but which was only parameterized in an earlier work, is calculated more directly to increase the accuracy of the model. It was found that soot is the main contributor to flame emissivity. Finally, the air dispersion model CALPUFF was run according to the proposed flare model and an empirical flare model by Beychok to compare results of the models. This new flare method is sufficiently simple to be embedded into air dispersion modelling software such as CALPUFF.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".