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Effect of Exit Wear Length on the Behavior of Coherent Jet

2024· article· en· W4401899678 on OpenAlexaboutno aff
Fuhai Liu, Bin Tong, Rong Zhu, Guangsheng Wei, Kai Dong

Bibliographic record

VenueISIJ International · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicVacuum and Plasma Arcs
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsJet (fluid)Materials scienceMechanicsMetallurgyPhysics

Abstract

fetched live from OpenAlex

The copper was the main manufacturing material to produce the coherent lance for enhancing the cooling effect. Due to the low hardness of copper and high-temperature environment, the exit of Laval nozzle would be worn off, resulting in suppressing the impaction ability of supersonic oxygen jet. In order to investigate the effect of wear length on the behavior of coherent jet, both high-temperature experiment and numerical simulation have been carried out, and the axial velocity, total temperature and oxygen fraction were measured in the experimental test to verify the accuracy of simulation model. Based on the result, the overexpand phenomenon was generated due to the Laval nozzle exit wear off, which improved the shock wave intensity at the tip of Laval nozzle, resulting in a lower axial velocity at the velocity potential core. With a longer wear length, the vorticity of the coherent jet periphery is increased, which causes more thermal energy of combustion flame being released prematurely near the coherent lance tip, leading to a shorter velocity potential core.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.285
Teacher spread0.275 · 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 designBench or experimental
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

Citations2
Published2024
Admission routes1
Has abstractyes

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