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Record W3126952440 · doi:10.1002/ceat.202000525

Comparative Study of the Combined Supersonic Separator and Vortex Tube Performance for Hydrocarbon Gas Drying

2021· article· en· W3126952440 on OpenAlexaboutno aff
Alexandr Yudakov, Victor S. Vlasenko, Vyacheslav Slesarenko

Bibliographic record

VenueChemical Engineering & Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicRanque-Hilsch vortex tube
Canadian institutionsnot available
FundersRussian Academy of Sciences
KeywordsSeparator (oil production)NozzleVortex tubeVortexSupersonic speedMechanicsMoistureHydrocarbonChemistryMaterials scienceThermodynamicsNuclear engineeringComposite materialPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract Experimental investigation of the combined supersonic separator (CSS) characteristics was carried out and the results were compared with those from the vortex tube. The separator is a counter‐flow vortex tube with an energy separation chamber in the shape of a Laval nozzle; it can be used for hydrocarbon gas transportation preparation and power plant fuel gas drying. Numerical modeling was carried out to confirm the flow specifics in the examined separator structure. Although the cooling capability of the CSS is 30–50 % less than that of the vortex tube, its efficiency of moisture removal is twice as large, as is shown by the experimental research. The maximal cooling power corresponds to a cold flow fraction level of 0.39, while the range of optimal values for the moisture separation is 0.4–0.6.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.008
GPT teacher head0.207
Teacher spread0.199 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations5
Published2021
Admission routes1
Has abstractyes

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