Interfacial Turbulent Diffusivity Between Air and Natural Gas/Hydrogen Blends During Pipeline Purging Process and Implication on the Extent of the Interfacial Mixing Zone
Bibliographic record
Abstract
Abstract The aspiration for blending hydrogen (H2) into natural gas (NG) in gas transmission systems is high and is happening globally. However, several design and operational aspects need to be developed to ensure the safe and reliable delivery of these blends to the end users. One of these aspects is the development of an effective purging procedure of air with NG+H2 blends during the commissioning of a pipe section. An important factor in achieving effective and safe purging is to accurately determine the extent of the mixing zone between the purged air and the purging medium (NG+H2 blend in this case). Since hydrogen is recognized as having a much smaller molecular size than air and/or NG, its molecular diffusivity would be higher leading to a lower Schmidt number. The present research examines the aspects of interfacial turbulent diffusivity, which is key in determining the extent of the mixing zone between air and NG+H2 blends during purging. The investigation was carried out via large eddy simulation (LES) computational fluid dynamics (CFD) simulations at different Reynolds numbers varying from 10,000 to 400,000. The spatial and temporal average of the turbulent diffusivity for an NG+H2 blend at 30% by mole H2 was extracted from the CFD results. It was found that the normalized turbulent diffusivity for the case of the NG+H2 blend studied is at least one-half order-of-magnitude higher than that from Taylor Theory, which is typically used for un-blended natural gas. The implication of this will be elucidated by examples to show that the extent of the mixing zone is longer for NG+H2 blends than that for un-blended NG. The ultimate application of the finding is to ensure that effective purging is achieved to prevent any possibility of an explosion of unpurged air, particularly when H2 is a constituent in the hydrocarbon gas mixture.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".