Prediction of the Location and Duration of Water Condensation in Nominally Dry Gas Transmission Pipelines
Bibliographic record
Abstract
Gas transmission companies limit the extent of internal corrosion on their systems by imposing strict gas quality specifications for water and other potential contaminants. However, upsets do occur and water can be introduced from a number of sources. One such source is water vapour in the gas at levels that may be above or below the specification. Water vapour may condense as a thin liquid film on the inner surfaces of the pipe, leading to the possibility of internal corrosion. A model (MHICE Model for Humidity and Internal Corrosion Estimation) has been developed to predict the location and duration of water film formation as a function of various operating parameters. The code predicts the time-of-wetness (TOW) of the pipe wall, which could then be used to predict the extent of internal corrosion using separate corrosion models. MHICE simulates a straight, level pipe with a number of inlets at which moist natural gas can be introduced. Any number of upset conditions can be specified at the main inlet, each described by a background and upset water concentration. Single upsets at up to ten downstream producers can also be simulated. The model accounts for the pressure and temperature drop along the pipe, both of which affect the saturated water vapour concentration or dew point. Water condensation and evaporation is simulated using a mass-transfer coefficient, with the rate dependent on the degree of over- or under-saturation, respectively. The code predicts the spatial and temporal distribution of liquid and vapour-phase water, integration of which gives the TOW along the length of the pipe. A single upset is predicted to move down the pipe as a slug of moist gas with little mixing with the drier gas ahead of and behind the slug. Water condenses at locations where the vapour concentration exceeds the saturation value. The rates of condensation and evaporation are fast compared with the gas velocity, so the wetted region of the pipe wall is predicted to move down the pipe with the slug of water vapour until such point that the gas is no longer super-saturated with water vapour. There is some broadening of the slug due to dispersion and, at the trailing edge of the wetted film, possibly because the rate of evaporation is not instantaneous. Downstream inputs can reinforce or dilute the main upset, depending upon their location, timing, and the level of the upsets.
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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 | 0.000 |
| 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".