Influence of turbulent Schmidt number on fugitive emissions source quantification
Bibliographic record
Abstract
Finding and quantifying unknown (fugitive) releases of gases such as methane from downstream concentration data is a critical environmental problem. Many proposed solutions involve wind and gas dispersion modelling for which an assumed value of the turbulent Schmidt number (Sct), the ratio of the eddy kinematic viscosity to the turbulent diffusivity, is used to scale the estimated diffusivity. This model constant has a range of physically reasonable values. Numerical simulations were performed on multiple test cases to quantify the impact of Sct uncertainty on the ability to locate and quantify fugitive emissions sources using data from a network of gas concentration sensors. The analysis further considers ways to reduce quantification uncertainty, by either specifying detected source locations based on ancillary knowledge, or by using a two-step optimization in which the presented adjoint approach is used to locate sources and simulated annealing is subsequently used to quantify emissions for these specified locations. Multiple test cases considered both real and numerically generated controlled releases using an open-field geometry and a complex bluff-body dominated geometry based on an actual gas plant in the Alberta, Canada oil and gas sector. Results suggest that correct prediction of unknown source locations is minimally affected by Sct, but emission rate quantification can be heavily influenced. The presence of bluff-bodies was found to partially mitigate these effects, such that, if repeated analyses over a range of Sct is not tractable, open field release results (including those presented here) can be used to estimate conservative error bounds on predicted emission rates. Ultimately this paper demonstrates the under-appreciated importance of considering Sct uncertainty when seeking to quantify unknown fugitive sources from downstream concentration data.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".