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Numerical study on subsonic-supersonic Laval nozzle

2021· article· en· W3197120651 on OpenAlexaboutno aff
Xingyue Ji, Junshen Zhi, Hongyu Pan

Bibliographic record

VenueJournal of Physics Conference Series · 2021
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsnot available
Fundersnot available
KeywordsNozzleSupersonic speedMach numberMechanicsDischarge coefficientChoked flowPhysicsFlow (mathematics)Aerospace engineeringEngineeringThermodynamics

Abstract

fetched live from OpenAlex

Abstract Because subsonic-supersonic Laval nozzles are widely used in many fields, the flow characteristics of the subsonic-supersonic Laval nozzle are studied by numerical simulation. First, the MacCormack scheme is used to discrete the non-conservation and conservation forms of the governing equations of the flow in the subsonic-supersonic Laval nozzle. The flow states inside the nozzle in both formats are then compared. The results show that the numerical results of the two schemes converge well. The variation of the fluid velocity and outlet pressure is more pronounced in the non-conservative form. For the conservation form, the error of the mass flow rates at different positions is constant and acceptable, which is about 1%. For the non-conservation form, the error at different locations within the nozzle is different, reaching a minimum error at the throat. In addition, studies on grid length have demonstrated that the finer the grid, the more accurate and stable the results Moreover, for the subsonic, supersonic isentropic flow in the quasi-one-dimensional nozzle, the Mach number and velocity become larger along with the nozzle. In contrast, other variables such as pressure and temperature become smaller. There is a subsonic region in front of the throat and a supersonic region behind the throat. The Mach number of the nozzle exit can be very high. Finally, the study reveals the internal flow state of the subsonic, supersonic nozzle, which can provide theoretical reference for the application of 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)0.001
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 score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.233
Teacher spread0.218 · 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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