Semi-Empirical Correlation to Quantify the Effects of Pipe Diameter and Internal Surface Roughness on the Decompression Wave Speed in Natural Gas Mixtures
Bibliographic record
Abstract
GASDECOM is typically used in the design of gas pipelines for calculating decompression speed in connection with the Battelle two-curve method used throughout the pipeline industry for the control of propagating ductile fracture. GASDECOM idealizes the decompression process as isentropic and one-dimensional, taking no account of pipe wall frictional effects. Previous shock tube tests showed that decompression wave speeds in smaller diameter and rough pipes are consistently slower than those predicted by GASDECOM for the same conditions of mixture composition and initial pressure and temperature. Preliminary analysis based on perturbation theory and the fundamental momentum equation showed a correction term to be subtracted from the ‘ideal’ value of the decompression speed. One parameter in this correction term involves a dynamic spatial pressure gradient of the outflow at the rupture location. While this is difficult to obtain without a shock tube or actual rupture test, data from 14 shock tube tests, as well as from 14 full scale burst tests involving a variety of gas mixture compositions, were analyzed to quantify the variation of this pressure gradient with gas conditions and outflow Mach number. A semi-empirical relationship was found to correlate this pressure gradient parameter with two basic parameters representing the natural gas mixture, namely the molecular weight of the mixture and its higher heating value (HHV). For lean gas mixes, the semi-empirically obtained correlation was found to fit very well the experimentally determined decompression wave speed curve. For rich gas mixes, the correlation fits both branches of the curve; above and below the plateau pressure. This paper provides the basis for the derived semi-empirical correlation, and suggests a procedure (with examples) to correct the ‘ideal’ (frictionless) GASDECOM prediction to account for both the effects of pipe diameter and pipe internal wall surface roughness.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".