Water vapour condensation behaviour within hydrogen-blended natural gas in laval nozzles
Bibliographic record
Abstract
Hydrogen-blended natural gas (NG) pipeline network transport is the most effective approach for solving the problem of large-scale hydrogen use. Hydrogen-blended NG that contains water vapour is prone to water vapour condensation when it passes through complex NG pipeline networks, leading to pipeline network failures. To analyse the condensation behaviour of hydrogen-blended NG containing water vapour in a Laval nozzle, a condensation model of water vapour was established. A computational fluid dynamics approach was used to calculate the condensation process of hydrogen-blended NG containing water vapour in Laval nozzles for four countries: Iran, USA, Russia, and Australia. Hydrogen-blended NG components affect the flow characteristics of the gas mixture in the nozzle. The gas components have the greatest effect on the Mach number. The difference between the maximum and minimum Mach numbers at the outlet was 0.02 Mach. Hydrogen-blended NG containing water vapour condenses downstream of the throat of the Laval nozzle. Hydrogen-blended NG from Russia had the largest condensation ratio (79.63 %). The largest droplet radius and liquid mass fraction were observed in the hydrogen-blended NG from Australia. The condensation process can accelerate the future research and engineering application of water vapour into hydrogen-blended NG.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".