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Record W4389782876 · doi:10.3103/s1066369x23090074

Numerical Simulation of Compressible Gas Flow in Flat Channels in the Narrow Channel Approximation

2023· article· en· W4389782876 on OpenAlexaboutno aff
S. Khodjiev

Bibliographic record

VenueRussian Mathematics · 2023
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsnot available
Fundersnot available
KeywordsCompressibilityCompressible flowMathematicsMechanicsSupersonic speedPlane (geometry)NozzleFlow (mathematics)Pressure gradientChoked flowChannel (broadcasting)Computational fluid dynamicsOpen-channel flowMathematical analysisGeometryPhysicsThermodynamicsComputer science

Abstract

fetched live from OpenAlex

Abstract A compressible gas in plane channels of constant and variable cross sections is numerically simulated using two-dimensional parabolized Navier–Stokes equations. The system of equations is solved numerically using the narrow-channel approximation model. A number of transformations, such as nondimensionalization of the system of equations to reduce the given domain to a square and refinement of computational points with large gradients of gas-dynamic parameters, are described in detail. Pressure gradient is determined from the flow-rate conservation condition. An efficient method is given for simultaneously determining the pressure gradient and longitudinal velocity, followed by other gas-dynamic parameters of stability 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 to validate the calculation methodology and confirm the reliability of the results by comparing them with data obtained by 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.017
GPT teacher head0.245
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

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