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Numerical simulation of compressible gas flow in flat channels in the narrow channel approximation

2023· article· en· W4387500849 on OpenAlexaboutno aff
S. Khodjiev

Bibliographic record

VenueIzvestiya Vysshikh Uchebnykh Zavedenii Matematika · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and Geomechanical Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsMechanicsCompressibilityPressure gradientCompressible flowPerfect gasFlow (mathematics)Computer simulationSupersonic speedNozzlePlane (geometry)DimensioningChannel (broadcasting)Numerical analysisChoked flowPhysicsMathematicsGeometryMathematical analysisThermodynamicsComputer scienceEngineering

Abstract

fetched live from OpenAlex

In this paper, numerical simulation of a compressible gas in plane channels of constant and variable cross-section is performed within the framework of two-dimensional parabolized Navier-Stokes equations. The "narrow channel approximation" model is used for the numerical solution of the uranation system. A number of transformations are described in detail, such as de-dimensioning the equation system, reducing the considered area to a square, as well as thickening the calculated points in large gradients of gas dynamic parameters. Flow conservation conditions are used to determine the pressure gradient. An effective method is given for simultaneously determining the pressure gradient and the longitudinal velocity, then other gas dynamic parameters stable for subsonic and supersonic flows, as well as a method for determining the critical flow rate for solving Laval nozzle problems. The results of methodical calculations are presented, with the aim of verifying the effectiveness of the developed calculation methodology, as well as confirming the reliability of the results obtained by comparing them with data from other authors

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.023
GPT teacher head0.239
Teacher spread0.215 · 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.

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
Published2023
Admission routes1
Has abstractyes

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