Numerical simulation of leakage and diffusion behaviour of hydrogen-doped natural gas from buried pipeline
Bibliographic record
Abstract
Hydrogen, as a clean energy source, has high calorific value, low pollution, and renewability. Injecting hydrogen into natural gas pipelines is efficient and economical for transportation. Unfortunately, flammable gas pipelines often leak due to material corrosion, construction defects, and external interference. To this end, a numerical method is established to investigate the gas diffusion behaviour and accumulation of leaked natural gas from an underground pipe to the soil. The variations in gas concentrations under different leak conditions, as well as the effects of pipeline release pressure orifice diameters (4.0–5.8 MPa), orifice diameters (1, 5, 10 mm), hydrogen-doped ratio (0–30%), and soil porosity (0.1–0.4) on the mass flow rate at the leakage point are evaluated. The simulation results indicated that the mass flow rate and gas concentration increase fast with release pressure. Leakage with high hydrogen content can lead to an opposite trend between methane and hydrogen concentration. The soil porosity directly determines the gas concentration distribution of diffusion at different monitoring points. Moreover, the quantitative relationship between the mass flow rate and different influencing factors has also been fitted. Finally, an empirical correlation formula is used to explain the leakage and diffusion characteristics of hydrogen-doped natural gas in soil.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".