MétaCan
Menu
Back to cohort
Record W2898989845 · doi:10.1063/1.5065160

Optical study of supersonic jet structure in atmospheric plasma spraying

2018· article· en· W2898989845 on OpenAlexaboutno aff
I. P. Gulyaev, Maxim Golubev, V. I. Kuzmin, Pavel Tyryshkin

Bibliographic record

VenueAIP conference proceedings · 2018
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsnot available
FundersRussian Foundation for Basic Research
KeywordsPlasma torchSchlierenSupersonic speedJet (fluid)Choked flowPlasmaShock waveNozzleMach numberShock diamondMaterials scienceShock (circulatory)OpticsSchlieren imagingMechanicsTurbulenceOblique shockPhysicsThermodynamicsNuclear physics

Abstract

fetched live from OpenAlex

The paper presents the results of optical studies of supersonic plasma flows of the DC plasma torch PNK-50 using shadow (schlieren) diagnostics, traditional video-registration using narrow-band light filters and spectrometry. The operation of a plasma torch with de Laval (convergent-divergent) nozzle of a critical section diameter of 6 mm was studied, in the gas flow range 5.25÷10.5 g /s (260-520 slpm), arc current 140-230 A, in the initial section of the 100 mm jet. Air is used as the plasma-forming gas. The use of the system of shadow diagnostics made it possible to reveal the turbulent structure of the plasma flow, to determine the angle of the jet opening, and also the thickness of the boundary layer of a gas flow impinging on a flat surface (substrate with a diameter of 25 mm). Video registration of the supersonic jet made it possible to visualize the shock structure of the flow, including up to 7-8 shock waves (“Mach disks” or “shock diamonds”). It is established that the injection of the sprayed powder into the supersonic flow has little effect on its gas-dynamic structure.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.239
Teacher spread0.224 · 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 teacher head, not a consensus.

Study designObservational
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

Citations6
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueAIP conference proceedingsSame topicHigh-Temperature Coating BehaviorsFrench-language works237,207