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Record W4210515810 · doi:10.1115/imece2021-69858

Prediction of Shock Wave Position Considering Non-Equilibrium Phase Change of Wet Natural Gas in Nozzle

2021· article· en· W4210515810 on OpenAlexaboutno aff
Yang Liu, Xuewen Cao, Jiang Bian

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsnot available
Fundersnot available
KeywordsMechanicsShock waveNozzleShock tubeMoving shockOblique shockShock (circulatory)Isentropic processSeparator (oil production)ThermodynamicsMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Abstract The existence of back pressure will cause shock wave in the Laval nozzle of the supersonic separator, which will lead to the condensed droplets to evaporate and reduce the separation efficiency of the separator. It is of great significance to determine the shock position and avoid the secondary evaporation of droplets for improving the separation efficiency. In this paper, one-dimensional isentropic flow function method was used to calculate the shock position in nozzle. A mathematical model of methane-water vapor was established. The results show that the pressure energy recovery leads to the shock wave. With the increase of energy recovery efficiency, the position of shock wave moves to the throat, which shortens the droplet growth space and reduces the liquefaction of Laval nozzle. When the pressure energy recovery efficiency is 56.25%, the liquid mass fraction is 1.63% at the outlet. The shock wave position error of theoretical calculation and numerical simulation increases with the enlargement of divergent angle when considering condensation. The average error between numerical simulation and theoretical calculation results was less than 5%, thus the one-dimensional isentropic flow function method can provide a theoretical basis for predicting the shock position.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.436

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 head0.030
GPT teacher head0.246
Teacher spread0.216 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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