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3D NUMERICAL SIMULATION OF THE EFFECT OF DROPLET INITIAL CONDITIONS ON THE EVAPORATION PROCESS

2009· article· en· W1969035337 on OpenAlexaff
M.M. Abou Al-Sood, Madjid Birouk

Bibliographic record

VenueComputational Thermal Sciences An International Journal · 2009
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Properties and Applications
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsTurbulenceVaporizationThermodynamicsMomentum (technical analysis)Materials scienceMechanicsEvaporationConservation of massTurbulence kinetic energyAmbient pressureReynolds-averaged Navier–Stokes equationsPhysics

Abstract

fetched live from OpenAlex

A three-dimensional numerical model is developed to simulate the effect of a droplet's initial conditions on the vaporization process in a turbulent convective environment at ambient pressure and temperature higher than the standard conditions. A hydrocarbon (n-heptane) droplet with two different initial diameters, 0.1 mm and 1.5 mm, and initial temperatures, 253 K and 320 K, is examined. The droplet is exposed to turbulent stream of nitrogen with a mean velocity of 2 m/s, and turbulence intensity ranging between 0 and 60%. The ambient pressure and temperature range is between 0.5 MPa and 4 MPa and 324 K and 1350 K, respectively. The numerical model solves the complete set of time-dependent conservation equations of mass, momentum, energy, and species concentration in both the gas phase and liquid phase. The turbulence terms in the conservation momentum (RANS) equations of the gas phase are modeled by using the shear stress transport model. Variable thermophysical properties, gas and liquid phase transients, and radiation are all accounted for. Moreover, the effect of high pressure such as nonideal gas behavior, solubility of ambient gas into the droplet, and pressure dependence of gas- and liquid-phase thermophysical properties are also considered.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.366
Teacher spread0.334 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2009
Admission routes1
Has abstractyes

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Same venueComputational Thermal Sciences An International JournalSame topicMaterial Properties and ApplicationsFrench-language works237,207