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Simulation research on the flow field performance of the supersonic separator for natural gas

2021· article· en· W3163828648 on OpenAlexaboutno aff
Jin Lv

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2021
Typearticle
Languageen
FieldEngineering
TopicCyclone Separators and Fluid Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSeparator (oil production)Natural gasNozzleSupersonic speedWet gasNatural-gas processingChoked flowMechanicsNatural gas fieldPetroleum engineeringMechanical engineeringChemistryThermodynamicsEngineeringWaste managementPhysics

Abstract

fetched live from OpenAlex

Abstract The separation of natural gas dehydration and de-heavy-hydrocarbons is an important part of natural gas treatment, the main purpose of which is to prevent liquid water in the later processing, transportation and storage of natural gas, and to prevent acidic gas dissolving in free water which will cause the corrosion of pipelines and equipment. This paper introduces a new type of natural gas separation technology— supersonic gas-liquid separation technology. Based on the working principle of supersonic gas liquid separator, using relevant theories such as fluid mechanics, gas dynamics and thermodynamics, the structure of Laval nozzle was mainly optimized and the flow field of the separator was simulated through ANSYS software, the distribution of the characteristic parameters such as pressure, velocity, temperature of shrink segment, throat, expansion segment of the supersonic cyclone separator nozzle were studied in this paper. The results show that the design of the nozzle structure meets the needs of low temperature, can make the water vapor in natural gas condense into small droplets and separate out, so as to achieve the goal of natural gas dehydration. Then, comparing the different design methods of Laval nozzle, the most reasonable design scheme is selected to improve the separation efficiency of the separator.

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 score0.509
Threshold uncertainty score0.276

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 head0.020
GPT teacher head0.246
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 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
Published2021
Admission routes1
Has abstractyes

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