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Record W2291098161 · doi:10.1007/s00170-016-8502-y

Analysis of De-Laval nozzle designs employed for plasma figuring of surfaces

2016· article· en· W2291098161 on OpenAlexaboutno aff
Nan Yu, Renaud Jourdain, Mustapha Gourma, Paul Shore

Bibliographic record

VenueThe International Journal of Advanced Manufacturing Technology · 2016
Typearticle
Languageen
FieldEngineering
TopicLaser-induced spectroscopy and plasma
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research Council
KeywordsFiguringNozzlePlasma torchComputational fluid dynamicsInductively coupled plasmaTorchMechanical engineeringAerodynamicsContext (archaeology)PlasmaJet (fluid)MechanicsMaterials scienceEngineeringOpticsAerospace engineeringPhysicsGeology

Abstract

fetched live from OpenAlex

Plasma figuring is a dwell time fabrication process that uses a locally delivered chemical reaction through means of an inductively coupled plasma (ICP) torch to correct surface figure errors. This paper presents two investigations for a high temperature jet (5000 K) that is used in the context of the plasma figuring process. Firstly, an investigation focuses on the aerodynamic properties of this jet that streamed through the plasma torch De-Laval nozzle and impinged optical surfaces. Secondly, the work highlights quantitatively the effects of changing the distance between the processed surface and nozzle outlet. In both investigations, results of numerical models and experiments were correlated. The authors’ modelling approach is based on computational fluid dynamics (CFD). The model is specifically created for this harsh environment. Designated areas of interests in the model domain are the nozzle convergent-divergent and the impinged substrate regions. Strong correlations are highlighted between the gas flow velocity near the surface and material removal footprint profiles. In conclusion, the CFD model supports the optimization of an ICP torch design to fulfil the demand for the correction of ultra-precision surfaces.

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 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 score0.182
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.258
Teacher spread0.245 · 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.

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

Citations33
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueThe International Journal of Advanced Manufacturing TechnologySame topicLaser-induced spectroscopy and plasmaFrench-language works237,207