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Temperature Corrected Turbulence Model for Supersonic Oxygen Jet at High Ambient Temperature

2017· article· en· W2591839282 on OpenAlexaboutno aff
Ziliang Li, Lingling Zhang, Daqiang Cang

Bibliographic record

VenueISIJ International · 2017
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSupersonic speedTurbulenceJet (fluid)MechanicsThermodynamicsNozzleCompressibilityIsothermal processMixing (physics)PhysicsMaterials science

Abstract

fetched live from OpenAlex

It is known that the two-equation turbulence models under-predict the turbulence mixing shear process for the compressible non-isothermal jet. In this study, a temperature corrected turbulence model on the basis of the SST k-ω model was proposed to predict the behavior of the cold supersonic oxygen jet injecting into the high-temperature environment. The results show that the corrected SST k-ω model is superior to previous corrected k-ε models when simulating the whole process of the supersonic oxygen jet flowing from a Laval nozzle into free space. Meanwhile, the calculation results of the outlet free jet at 285 K, 772 K and 1002 K are in good agreement with experimental data and empirical formulas in the literature. Furthermore, the behavior of the supersonic oxygen jet at 1873 K is predicted by the proposed model. In addition, the effects of the ambient temperature on the jet core length and the interaction between multiple jets were also studied.

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)0.000
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: none
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.008
GPT teacher head0.228
Teacher spread0.220 · 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

Citations13
Published2017
Admission routes1
Has abstractyes

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