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Record W2089206969 · doi:10.1115/ipc2002-27113

Effective Time Method for the Determination of Interface Diffusion of H2S Slugs in Natural Gas Pipeline Systems

2002· article· en· W2089206969 on OpenAlexaff
D. Sennhauser, K. K. Botros, Trent van Egmond

Bibliographic record

Venue4th International Pipeline Conference, Parts A and B · 2002
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsTransCanada (Canada)Nova Chemicals (Canada)
Fundersnot available
KeywordsThermal diffusivityMechanicsDiffusionMass diffusivityPipeline (software)Flow (mathematics)Interface (matter)Slug flowFick's laws of diffusionWork (physics)Materials scienceThermodynamicsTwo-phase flowEngineeringPhysicsMechanical engineeringBubble

Abstract

fetched live from OpenAlex

The primary objective of the work described in this paper is to examine the fate of H2S contaminated natural gas slugs as they travel through a gas pipeline network. The important phenomenon that affects the spread of the H2S slug as it travels downstream of a pipe is the diffusion with the sweet gas at the front and back interface of the slug. It was determined that the diffusivity constant (D) used in the calculation of the interface spread varies along the pipeline, which prohibits the use of a closed form solution of the Fick’s law equation. An effective time parameter has been introduced to account for the variation in the diffusivity in a “marching in time” scheme of solution. The model has been utilized to demonstrate the effects of pipe diameter, mean flow velocity and pipe internal roughness on the contamination spread. A test loop has also been constructed to validate the diffusion coefficient in gaseous flows. Excellent agreement was obtained between the measured vs. predicted results. The mean error in predicting the interface spread was approximately 6.2%.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.247
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2002
Admission routes1
Has abstractyes

Explore more

Same venue4th International Pipeline Conference, Parts A and BSame topicWater Systems and OptimizationFrench-language works237,207