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Record W2315400051 · doi:10.1115/ht2005-72664

Convection-Free Thermodiffusion in a Water-Ethanol Mixture Subject to Varying Thermal Boundary Conditions

2005· article· en· W2315400051 on OpenAlexafffund
M. Chacha, M. Ziad Saghir

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicField-Flow Fractionation Techniques
Canadian institutionsToronto Metropolitan University
FundersCanadian Space Agency
KeywordsMechanicsWork (physics)Control volumeThermodynamicsBoundary value problemHeat transferConvectionTransient (computer programming)ThermalMaterials scienceMass transferThermal diffusivityBoussinesq approximation (buoyancy)Finite volume methodNatural convectionPhysicsMathematicsRayleigh numberComputer scienceMathematical analysis

Abstract

fetched live from OpenAlex

The paper presents a precise numerical simulation of the transport processes in a rectangular cavity saturated with a water-ethanol mixture. The full transient Navier-Stokes equations coupled with the heat and mass transfer equations are solved by the means of the finite volume method. The mixture properties are drawn from the recent work by Dutrieux et al. [1]. The density is assumed to vary linearly with temperature and concentration (Boussinesq approximation) in the working temperature range while other thermo physical properties are held constant. After validation the present code is used for a series of numerical experiments. Thermodiffusion in a liquid-mixture of Ethanol and Water is analyzed under zero gravity condition. Different thermal boundary conditions scenarios are considered to simulate possible thermal control system shortcomings. Results of investigations might help in the preparation and monitoring of the heat sources control systems during the direct Soret coefficient measurement experiments.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

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 studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.226
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 source (direct Gemma or distilled Codex), 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

Citations0
Published2005
Admission routes2
Has abstractyes

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