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Record W3211442776 · doi:10.1115/imece2000-1525

A Fully Eulerian Approach to Particle-Laden Compressible Flows

2000· article· en· W3211442776 on OpenAlexaff
Ali Dolatabadi, J. Mostaghimi, Mihajlo Ivanovic

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMechanicsEulerian pathCompressibilityMomentum (technical analysis)Compressible flowMixing (physics)Shock waveFlow (mathematics)PhysicsParticle velocityTorchClassical mechanicsMaterials science

Abstract

fetched live from OpenAlex

Abstract Dense particles in highly compressible gas flows are analyzed using the Eulerian-Eulerian approach. Simulations are applied to a High Velocity Oxy-Fuel (HVOF) thermal spray torch. In this analysis, by using a fully Eulerian approach, the dispersed flow like the continuous flow is considered in the Eulerian frame whereby most of the physical aspects of the gas-particle flow in the HVOF process can be incorporated. These two phases are coupled through momentum and energy exchanges that are expressed in the form of source terms appearing in the governing equations. The numerical simulations show large variations in gas velocity and temperature both inside and outside the torch due to flow features such as mixing layers, shock waves, and expansion waves. The characteristics of the particles such as velocity and temperature are analyzed.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.223
Threshold uncertainty score0.999

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)0.0010.003

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 head0.013
GPT teacher head0.209
Teacher spread0.196 · 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; both teacher heads agree on what is shown here.

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
Published2000
Admission routes1
Has abstractyes

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