Effective Time Method for the Determination of Interface Diffusion of H2S Slugs in Natural Gas Pipeline Systems
Bibliographic record
Abstract
The primary objective of the work described in this paper is to examine the fate of H2S contaminated natural gas slugs as they travel through a gas pipeline network. The important phenomenon that affects the spread of the H2S slug as it travels downstream of a pipe is the diffusion with the sweet gas at the front and back interface of the slug. It was determined that the diffusivity constant (D) used in the calculation of the interface spread varies along the pipeline, which prohibits the use of a closed form solution of the Fick’s law equation. An effective time parameter has been introduced to account for the variation in the diffusivity in a “marching in time” scheme of solution. The model has been utilized to demonstrate the effects of pipe diameter, mean flow velocity and pipe internal roughness on the contamination spread. A test loop has also been constructed to validate the diffusion coefficient in gaseous flows. Excellent agreement was obtained between the measured vs. predicted results. The mean error in predicting the interface spread was approximately 6.2%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".