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Record W4391527742 · doi:10.1115/imece2023-111150

Challenges of Purging Air With Natural Gas and Hydrogen Blends in Pipe Segments

2023· article· en· W4391527742 on OpenAlexaff
K. K. Botros, Colin Hill, Paul Ziadé, Craig T. Johansen, Greg Van Boven

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsAlberta EnergyNova Chemicals (Canada)
Fundersnot available
KeywordsNatural gasHydrogenMaterials scienceChemical engineeringWaste managementEngineeringChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The aspiration for blending hydrogen (H2) into natural gas (NG) in gas transmission systems is high and is happening globally. However, several design and operational aspects need to be developed to ensure the safe and reliable delivery of these blends to the end users. One of these aspects is the development of an effective purging procedure of air with NG+H2 blends during the commissioning of a pipe section. The present research examines various aspects of the purging procedures and attempts to address salient parameters and conditions when the NG+H2 blend is the purging gas. Three principal aspects were investigated, namely: 1) The stratification velocities of the air and gas wave fronts for different NG+H2 blend ratios, 2) The interfacial turbulent diffusivity coefficients between air and NG+H2 blends that determine the extent of the mixing spread as purging progresses, and 3) The applicability of the minimum stratification velocity to smaller pipe sizes ≤ NPS 4. The present investigation found that the stratification velocity of the air wave is higher than that of the NG+H2 wave, and both deviate from the classical value corresponding to Fr # = 0.707 assumed in AGA Purging Principles and Practices. Initial results of the diffusivity coefficients show an upward deviation from that currently in use in the gas transmission industry.

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: Empirical
Teacher disagreement score0.485
Threshold uncertainty score0.296

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 head0.009
GPT teacher head0.193
Teacher spread0.185 · 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
Published2023
Admission routes1
Has abstractyes

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