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Record W3102020125 · doi:10.22215/etd/2020-14289

Experimental Modelling of Black Carbon Emissions from Gas Flares in the Oil and Gas Sector

2020· dissertation· en· W3102020125 on OpenAlex
Parvin Mehr

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnergy
TopicOil, Gas, and Environmental Issues
Canadian institutionsCarleton University
Fundersnot available
KeywordsSootMethaneFroude numberTurbulenceTurbulent diffusionPlumeCarbon blackCarbon fibersReynolds numberEnvironmental scienceChemistryAtmospheric sciencesCombustionMeteorologyFlow (mathematics)MechanicsMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Experiments examined the effects of flow conditions and fuel chemistry on the soot emissions from turbulent buoyant diffusion flames burning methane-dominated alkane fuels mixtures representative of upstream oil and gas sector flares. Soot (elemental carbon) in the captured plumes was measured via thermal-optical analysis. Yields were calculated within precisely-quantified uncertainties following a mass-balance procedure using CO2, CO, and CH4 gas analyzers. Experiments considered six flare diameters (12.7-76.2 mm), exit velocities up to 9.5 m/s, and thirteen multi-component fuel mixtures. Reynolds number times Froude number squared was shown to be a useful criterion to separate differing soot emissions trends which were aligned with the transition buoyant and transition shear sub-regimes of turbulent buoyant flames as defined by Delichatsios (1993).

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.986

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 head0.026
GPT teacher head0.241
Teacher spread0.215 · 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

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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