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Record W7133039059

A computational analysis of high speed particle-laden flows

2002· dissertation· W7133039059 on OpenAlexfundno aff
Ali Bagheri Dolatabadi

Bibliographic record

VenueTSpace · 2002
Typedissertation
Language
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsShroudNozzleSupersonic speedCoatingFlow (mathematics)Particle (ecology)Jet (fluid)CeramicShock (circulatory)
DOInot available

Abstract

fetched live from OpenAlex

Particle-laden flow has been acknowledged as an important phenomenon integral to many technological and natural systems. Analysis of a high speed particle-laden flow typical of those in the high velocity oxy-fuel (HVOF) process is presented in this thesis. Since the particulate flow in this process includes a large variety of particle suspension, the Lagrangian and Eulerian approaches have been applied in parallel to simulate the two-phase flow associated with the process. Effects of attaching a cylindrical shroud to the end of the supersonic HVOF nozzle on gas and particle flows have been analyzed. It was found that the shroud significantly reduces the oxygen content in the field by protecting the supersonic jet from ambient air entrainment. The validation experiments were performed for the majority of process parameters such as shock formation, particle conditions, and coating oxygen content. The thesis concludes with application of the numerical models in designing a nozzle that provides improved performance in the areas of deposition efficiency, particle in-flight oxidation, and flexibility to allow coating of ceramic powders. And finally, the new nozzle was built, and successfully tested with several coating materials including metallic, carbide, and ceramic powders.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.018
GPT teacher head0.296
Teacher spread0.278 · 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 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
Published2002
Admission routes1
Has abstractyes

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