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Record W4294630735 · doi:10.31399/asm.cp.itsc2003p1011

Diagnostic of Supersonic High Frequency (HF) Plasma Flow

2003· article· en· W4294630735 on OpenAlexaffabout
Valérie Léveillé, Maher I. Boulos, D.A. Gravelle

Bibliographic record

VenueThermal spray · 2003
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMach numberNozzleStagnation enthalpySupersonic speedMechanicsJet (fluid)PlasmaChoked flowStagnation pressureFlow velocityArgonEnthalpyMaterials sciencePhysicsAtomic physicsFlow (mathematics)ThermodynamicsNuclear physics

Abstract

fetched live from OpenAlex

Abstract In this study, two complementary techniques of diagnostic are used to study the properties of a supersonic HF plasma flow: namely, an enthalpy probe measurement and a flow visualization. A PL-35 induction plasma torch operating with three convergent-divergent Laval-type nozzles generates the plasma: a Mach 3.0 velocity water-cooled (wc) nozzle, a Mach 1.5 velocity wc nozzle and a Mach 2.45 velocity radiation-cooled (rc) nozzle. The plasma plate power is fixed at 20 kW and chamber pressure varies between 1 and 10 kPa. The plasma gas is argon and its flow is fixed at 60 slpm. The enthalpy probe profiles of local enthalpy and stagnation pressure are measured, from which, temperature, velocity and Mach number are obtained. The effect of nozzle design on plasma properties is investigated. The RC nozzle creates a plasma jet hotter with a steeper thermal profile and a higher mean velocity than the wc nozzle. The enthalpy probe calculations imply the assumption that the static pressure of the flow is similar to the chamber pressure. Experimental results show that this assumption is still applicable in the jet fringes, but its value changes strongly along the radial and axial axis. Also, photographs of the oblique shock wave in front of a cone in the plasma flow allow the approximation of the flow Mach number produced by a Mach 1.5 velocity wc nozzle. Its approximate value is 2.0, which is higher than predicted.

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 score0.452
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.178
Teacher spread0.172 · 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

Citations0
Published2003
Admission routes2
Has abstractyes

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