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Record W2082610812 · doi:10.2514/1.t4261

Unsteady Internal Forced-Convective Flow Under Dynamic Time-Dependent Boundary Temperature

2014· article· en· W2082610812 on OpenAlexaff
M. Fakoor-Pakdaman, Mehran Ahmadi, Majid Bahrami

Bibliographic record

VenueJournal of Thermophysics and Heat Transfer · 2014
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMechanicsNusselt numberFilm temperatureHeat transferDimensionless quantityMaterials scienceHeat fluxThermodynamicsForced convectionChurchill–Bernstein equationSuperposition principleConvective heat transferInternal flowFlow (mathematics)PhysicsReynolds numberTurbulence

Abstract

fetched live from OpenAlex

A new all-time analytical model is developed to predict transient internal forced-convection heat transfer under arbitrary time-dependent wall temperature. Slug flow condition is assumed for the velocity profile inside the tube. The solution to the time-dependent energy equation for a step wall temperature is generalized for arbitrary time variations in surface temperature using Duhamel’s theorem. A harmonic boundary temperature is considered, and new compact closed-form relationships are proposed to predic: 1) fluid temperature distribution; 2) fluid bulk temperature; 3) wall heat flux; and 4) the Nusselt number. An optimum value is found for the dimensionless angular frequency of the wall temperature to maximize the heat transfer rate of the studied unsteady forced-convective process. Such dimensionless parameter depends upon the imposed-temperature angular frequency, fluid thermophysical properties, and tube geometrical parameters. A general surface temperature is considered, and the temperature field inside the medium is obtained using a superposition technique. An independent numerical simulation is performed using ANSYS® Fluent. The comparison between the obtained numerical data and the present analytical model shows good agreement: a maximum relative difference less than 4.9%.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.891

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 integrity0.0000.001
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 head0.003
GPT teacher head0.184
Teacher spread0.181 · 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 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

Citations6
Published2014
Admission routes1
Has abstractyes

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