Effects of Pipeline Elevation Changes on Optimum Expelling Procedures for Gas Pipelines
Bibliographic record
Abstract
Expeller performance has been evaluated in terms of the capability to create suction pressure at the throat. This formulation has been used to assess the effectiveness of evacuating combustible gases from an isolated, depressurized, pipeline section involving mainline block valves up to two times normal spacing with an intermediate vent stack. Additionally, the effects of elevation changes that promote buoyancy driven flows are accounted for in time as the interface between air and gas travels along the pipeline section during expelling. Two expelling strategies were introduced and assessed. These are simultaneous expelling, in which gas is expelled from the pipeline section from both ends, and sequential expelling, in which an intermediate vent stack is used to expel gas from the upstream and downstream segments. The effects of elevation changes and the location of the intermediate vent stack determine the best strategy for expelling so as to maximize the purge velocity in the section of a pipeline to be purged, while maintaining the mean flow velocity in the pipe above the minimum purge velocity to prevent air-gas stratification. It was found that for a ‘Flat-’ or a ‘Cusp-type’ elevation profile it is advantageous to follow a sequential expelling procedure using one expeller at the intermediate vent stack location. In the case of a ‘Vee-type’ elevation profile, a simultaneous expelling procedure is a better option in terms of expelling time, at the cost of needing to deploy two expellers to different sites quite far apart. Air ingress location depends on the expelling strategy and elevation profile.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".