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Record W4317209605 · doi:10.5510/ogp20220400790

Development of new technological processes based on supersonic flow of natural gas

2022· article· en· W4317209605 on OpenAlexaboutno aff
Э. Х. Искендеров, A. N. Bagirov, S. A. Bagirov, P. Sh. Ismayilova

Bibliographic record

VenueProceedings of OilGasScientificResearchProjects Institute SOCAR · 2022
Typearticle
Languageen
FieldEngineering
TopicCyclone Separators and Fluid Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsNatural gasSupersonic speedGas compressorCompressor stationNozzlePetroleum engineeringEnvironmental scienceProcess engineeringMechanical engineeringEngineeringWaste managementAerospace engineering

Abstract

fetched live from OpenAlex

The article is devoted to the study of supersonic movement of natural gas in a pipeline and the possibility of developing new technological processes for cooling, drying and separating liquid hydrocarbons. Technological processes and a set of equipment created using the supersonic movement of natural gas are studied, their advantages and disadvantages are analyzed. It is known that a change in the process of gas injection into UGS facilities in a wide range of pressure during the season creates opportunities for more efficient use of compressor equipment. The thermobaric parameters of gas cooling due to supersonic motion in various designs have been calculated, and the existence of ample opportunities for creating new technological processes has been proved. Recommendations have been developed on the throughput capacity of gas installations to ensure the regulation of cooling systems created for underground gas storage facilities. It was noted that the cooling and gas separation systems created on the basis of thermobaric parameters and principles of regulation will be useful not only for underground gas storages, but also for other sub-sectors of the gas industry. Keywords: natural gas; supersonic movement; laval nozzle; underground gas storage; gas cooling; separation; compressor.

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)0.001
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 score0.237
Threshold uncertainty score0.905

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.250
Teacher spread0.226 · 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 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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of OilGasScientificResearchProjects Institute SOCARSame topicCyclone Separators and Fluid DynamicsFrench-language works237,207