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Record W4296918799 · doi:10.31399/asm.cp.itsc2010p0467

Development of a Low-Temperature, Oxy-Fuel (LTOF) Thermal Spray Gun

2010· article· en· W4296918799 on OpenAlexaff
R. Dhiman, F. Farhadi, L. Pershin, S. Chandra, J. Mostaghimi

Bibliographic record

VenueThermal spray · 2010
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsCanadian Dairy Network
Fundersnot available
KeywordsNozzleMaterials scienceCombustionParticle (ecology)Adiabatic flame temperatureNuclear engineeringCombustion chamberThermal sprayingComposite materialGas dynamic cold sprayMechanicsCoatingCombustorMechanical engineeringChemistry

Abstract

fetched live from OpenAlex

Abstract This paper presents the development of a new thermal spray gun for the so-called warm spraying process in which powder particles are not melted but heated to temperatures much higher than those typically found in a cold spray process. The increased heating leads to a reduction in the particle impact velocity required to deposit the coating and hence reduces operating cost. The new gun utilizes methane-oxygen combustion for particle heating and features a swirl-type combustion chamber to create a turbulent mixture of the fuel and oxidizer for efficient combustion. Powder can be fed axially or radially into the gun. To control particle temperature independently, combustion gases are diluted by adding nitrogen gas through axial or radial ports provided in the gun. A converging-diverging nozzle with a downstream cylindrical barrel accelerates the burnt gases to supersonic velocities. The design of the nozzle and barrel was optimized using numerical simulations. Mass flow rates of methane, oxygen, and nitrogen were calculated using energy balance, stoichiometric combustion, and nozzle flow rate equations. The gun is designed to operate up to 200 kW and is water-cooled. Experiments were conducted to test the performance of the new gun in which tungsten carbide coatings were deposited on aluminum substrates. Coatings were analyzed using standard methods and showed promising results.

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), Insufficient payload (model declined to judge)
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 score0.040
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.216
Teacher spread0.208 · 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 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

Citations1
Published2010
Admission routes1
Has abstractyes

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