Blending of hydrogen into a natural gas distribution pipeline in BritishColumbia through a tee junction for reducing GHG emissions
Bibliographic record
Abstract
Abstract: Integrating renewable hydrogen into the existing natural gas infrastructure network can reduce greenhouse gas (GHG) emissions and enhance electricity storage efficiency. To ensure a functional and durable network, attaining homogeneous mixing as quickly as possible is crucial. This study employs CFD analysis to evaluate hydrogen and methane mixing homogeneity in a pipeline with a vertical top-side Tee junction. The coefficient of variation (CoV) of hydrogen mole fraction is analyzed along cross-sectional slices of a distribution pressure (DP) pipe where the ideal gas law is applicable due to low pressures. The findings reveal that reducing the diameter of the side pipe while maintaining the main pipe diameter results in a shorter mixing homogeneity length due to enhanced side flow jet penetration and increased diffusion. Notably, a sharp drop in the CoV figure is evidenced when reducing the side pipe from NPS 1.25 to NPS 1.0, leading to a substantial decrease in the mixing homogeneity length from 143 main pipe diameters from the branch center to 38 diameters.
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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 | 0.001 |
| 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".