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Numerical Study on Subsonic-Supersonic Laval Nozzle Using MacCormack Scheme

2021· article· en· W3197094861 on OpenAlexaboutno aff
Boyang Li, Jingbo Wu, Yuzhou Liu

Bibliographic record

VenueJournal of Physics Conference Series · 2021
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsnot available
Fundersnot available
KeywordsNozzleIsentropic processMechanicsSupersonic speedMach numberShock waveDischarge coefficientChoked flowShock (circulatory)PhysicsFlow (mathematics)Rocket engine nozzleSupercritical flowAerospace engineeringEngineeringThermodynamics

Abstract

fetched live from OpenAlex

Abstract Due to the wide application of the Delaware nozzle in many fields, the flow characteristics of the subsonic-supersonic isentropic flow in the De-Laval nozzle are analyzed numerically. The flow in the nozzle can be simplified to a quasi-one-dimensional flow problem. First, the MacCormack format is employed to discretize the control equations in conservative form. Then, the results with and without artificial viscosity are compared. Grid independence is also discussed. The results show that the numerical solution and the theoretical solution agree very well, indicating that the numerical simulation results are very reliable. In addition, a higher pressure will reduce the peak and valley values of Mach number, pressure, density, temperature and velocity in the nozzle, and these extreme values of subsonic supersonic isentropic nozzle appear earlier. Additionally, the shock wave is accurately captured, and the shock wave is behind the throat. This research is of great significance to understand the flow characteristics in the nozzle.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.025
GPT teacher head0.256
Teacher spread0.230 · 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
Published2021
Admission routes1
Has abstractyes

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