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Record W4294704528 · doi:10.31399/asm.cp.itsc2006p0289

Simulation of Particle-Shock Interaction in a HVOF Process

2006· article· en· W4294704528 on OpenAlexaff
V.R. Srivatsan, Ali Dolatabadi

Bibliographic record

VenueThermal spray · 2006
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsConcordia University
Fundersnot available
KeywordsMaterials scienceMechanicsNozzleShock (circulatory)Particle (ecology)Bow shock (aerodynamics)Substrate (aquarium)Flow (mathematics)Thermal sprayingParticle velocityComposite materialAerospace engineeringShock wavePhysicsEngineeringGeologyCoating

Abstract

fetched live from OpenAlex

Abstract High Velocity Oxygen Fuel Process (HVOF) involves supersonic two-phase flow of gas-solid particles. Two kinds of shocks are formed in a typical HVOF process. Adjustment of the over-expanded flow to the atmospheric pressure at the exit of the nozzle results in formation of shock diamonds while high speed flow impingement on a substrate creates bow shock. The latter is found to be responsible for deviation of the injected particles from their trajectories near the substrate which significantly reduces the chance of some particles landing on the substrate. An attempt is made to study the behavior of particle trajectory as it interacts with the bow shock formed near the substrate. The strength and location of bow shock was found to vary for different substrate geometries and stand-off distances. In this work, various particle sizes impinging on substrates with various configurations (flat, concave and convex) are simulated and the effect of shock diamonds and bow-shock, on particle trajectory is studied.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.288

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 head0.011
GPT teacher head0.261
Teacher spread0.250 · 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 designSimulation or modeling
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
Published2006
Admission routes1
Has abstractyes

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