MétaCan
Menu
Back to cohort
Record W2535168475 · doi:10.1115/ipc2000-249

Understanding the Physical Phenomena of Pipeline Decompression

2000· article· en· W2535168475 on OpenAlexaff
Xuejin Zhou, R.G. Moore, G. G. King

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPipeline (software)DecompressionPipeline transportHeat transferMultiphase flowFluid dynamicsFluid mechanicsFlow (mathematics)Process (computing)MechanicsPetroleum engineeringComputer scienceMechanical engineeringEngineeringPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Pipeline decompression is an important aspect of risk assessment during the design and operation of high-pressure gas transmission pipelines. As numerical simulation technology improves, more sophisticated multiphase decompression flow models are emerging. A complete understanding of physical phenomena occurring during rapid pipeline decompression is essential in developing an accurate, advanced and fundamentally sound multiphase flow model. Pipeline decompression is a complex process that involves many thermodynamic and hydrodynamic non-equilibrium phenomena that govern the characteristics of fluid flow in the pipe. It is affected by parameters such as pipeline geometry, heat transfer, fluid characteristics, and various interactions between them. In this paper, we describe and discuss the pipeline decompression process, critical flow phenomena, fluid phase behavior, thermodynamic and hydrodynamic non-equilibria, characteristics of fluid mechanics, heat transfer and pipeline mechanics. Hopefully, this will enhance understandings of the predictive capabilities and limitations of various types of pipeline decompression models currently used for this process.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.157

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 head0.028
GPT teacher head0.220
Teacher spread0.191 · 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

Citations0
Published2000
Admission routes1
Has abstractyes

Explore more

Same topicOffshore Engineering and TechnologiesFrench-language works237,207