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Record W2080580736 · doi:10.2118/131239-pa

A Fast and Efficient Numerical-Simulation Method for Supersonic Gas Processing

2011· article· en· W2080580736 on OpenAlexaffabout
Dengyu Jiang, Changliang Wang, Lin Tang

Bibliographic record

VenueSPE Projects Facilities & Construction · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsNozzleSupersonic speedMechanicsNucleationSupersaturationCondensationFluentComputer simulationChoked flowFlow (mathematics)ThermodynamicsMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Summary Supersonic-swirling-separation technology is an innovative gasconditioning technology that separates heavy hydrocarbon and water vapor from natural gas. The Laval nozzle, where the condensation occurs, is used to generate supersonic flow and achieve a high degree of supersaturation in this natural-gas dehydration unit. Therefore, the nozzle shape has a strong impact on the nonequilibrium phase transition and plays a decisive role in distribution of nucleation and growth rate. To optimize the structure of the Laval nozzle and achieve higher separation efficiency, numerical simulation plays an important role in accelerating development cycles and cutting down the cost of experiment. To avoid the complexity of using the multiphase model and real-gas model, a quick and efficienct method is validated and used to determine the location of the nucleation zone and the droplet-growth zone in this paper. On the basis of the Fluent software, this paper presents a numerical-simulation method for condensing flow using user-defined function (UDF). This method itself is an extension of Fluent software for simulating condensing flow by adding a condensation model. The corrected internally-consistent-classical-theory (ICCT) model and Gyarmathy model (gya82) are employed to prescribe this phase transition. Actually, this problem is solved by coupling the Navier-Stokes (N-S) equation and condensate mass equation. Condensing flow in a Laval nozzle is simulated at different nozzle-pressure ratios (NPRs) and initial supersaturations. The results show that high cooling rate results in a high value of supersaturation and nucleation rate in a supersonic expansion Laval-nozzle flow. When condensation occurs, the flow is affected by the latent heat released and the total temperature is increased. This method can accurately predict the distribution of the condensing flow parameters, find an optimized flow state to obtain larger droplets, and ensure that the latent heat released is moderate to maintain a steady flow. Finally, this method is applied to the numerical simulation of a full-scale supersonic-swirling-separator flow field under different work conditions.

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 score0.939
Threshold uncertainty score0.980

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)0.0010.000

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 head0.046
GPT teacher head0.271
Teacher spread0.225 · 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

Citations9
Published2011
Admission routes2
Has abstractyes

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