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Record W4376881379 · doi:10.31399/asm.cp.itsc2023p0214

Independent Control of Particle Temperature and Velocity Using a Novel Powder Preheater Design for Low Pressure Cold Spray

2023· article· en· W4376881379 on OpenAlexaff
D. MacDonald, B. Jodoin

Bibliographic record

VenueThermal spray · 2023
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPropellantGas dynamic cold sprayParticle (ecology)Air preheaterMaterials scienceParticle velocityParticle sizeParticle depositionComposite materialEnthalpyThermodynamicsChemical engineeringMechanicsChemistryCoatingRange (aeronautics)

Abstract

fetched live from OpenAlex

Abstract In high-pressure cold spray, the enthalpy of the particle carrier gas has a significant effect on the propellant gas conditions and ultimately on particle impact velocities and temperatures. Through modelling and experimentation, the current work demonstrates that in low-pressure cold spray, the particle carrier gas enthalpy has a minimal effect on the particle velocity and is rather limited to affecting the particle impact temperature. Consequently, particle impact temperature can be controlled independently from impact velocity. This is a valuable tool when dealing with temperature sensitive substrates: low propellant temperatures can be used in combination with high particle temperatures enabling particle deformation while minimizing substrate heat input. Particle preheating was used to inject pure aluminum particles in a commercial low-pressure cold spray to temperatures up to 500°C. This was accomplished without clogging because of the development of a novel particle preheater, which eliminated the particles exposure to hot metal surfaces. Even after substantial spray time, no evidence of wear or clogging was found. The particle preheating resulted in a deposition efficiency increase of 3.6 times when compared to the injection of room temperature particles.

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 score0.074
Threshold uncertainty score0.799

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 head0.023
GPT teacher head0.242
Teacher spread0.219 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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