Decompression Behaviour of Natural Gas-Hydrogen Mixtures: Shock Tube Test and Numerical Prediction
Bibliographic record
Abstract
Abstract Blending hydrogen into existing natural gas pipelines is a transition option to progressively increase the energy share of hydrogen. As part of the fracture control plan, pipelines must be capable of controlling a running ductile fracture for all blend ratios used in operations. It is important to understand the decompression characteristics of Natural Gas-Hydrogen (NGH2) mixtures at those ratios. Under the aegis of the Future Fuels Cooperative Research Centre (FFCRC), a project was initiated to investigate the implication of the use of NGH2 blends on the Australian network, under the assumption that pipe properties are not affected by hydrogen during ductile fracture propagation. In this work, a desktop study is carried out to evaluate the impact of hydrogen on the toughness requirements. The potential increase in toughness requirement with a rich gas mixture within a given range of hydrogen fraction is revealed. This provides a basis for the design of shock tube tests. Eleven shock tube tests of NGH2 mixtures were carried out at TC Energy’s Gas Dynamic Test Facility in Didsbury, Canada. The mixtures targeted concentrations of 0%, 9%, 30% and 100% hydrogen. The outcomes of the shock tube tests are reported. 1D isentropic numerical decompression wave speed predictions using the GERG-2008 equation of state are compared with experimental 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".