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Record W2047597775 · doi:10.1139/p08-022

Flow and heat transfer of a micropolar fluid sandwiched between viscous fluid layers

2008· article· en· W2047597775 on OpenAlexvenueno aff
J. C. Umavathi, J. Prathap Kumar, Ali J. Chamkha

Bibliographic record

VenueCanadian Journal of Physics · 2008
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsPrandtl numberViscosityMechanicsNewtonian fluidThermodynamicsHeat transferFlow (mathematics)Flow velocityFluid dynamics

Abstract

fetched live from OpenAlex

In the present analysis, a two-fluid model for blood flow through a horizontal channel is studied. The model essentially consists of a core region assumed to be a micropolar fluid and two viscous (Newtonian) fluid regions. Using the boundary and interface conditions proposed by Ariman et al. (J. Appl. Mech. ASME, 41, 1 (1974)), analytical expressions for velocity, microrotation velocity, and temperature are obtained. The solutions are also evaluated numerically and shown graphically for various governing parameters such as the material parameter, viscosity ratio, conductivity ratio, Eckert and Prandtl numbers on velocity, microrotation velocity, and temperature profiles. In addition, results for the rate of heat transfer, mass flow rate, and skin friction for different values of the physical parameters are presented in tabular form. It is found that effect of the material parameter is to suppress the flow and the viscosity ratio promotes the flow. It is also interesting to note that the material parameter and viscosity ratio affect the position of the point of flow separation for which the flow nature is reversed. Also, considering the solvent viscosity for air, water, and glycerin, the cell rotation on the flow has been tabulated for 0%, 20%, and 40% concentration. PACS Nos.: 44.15+a, 44.35.+c

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 score0.020
Threshold uncertainty score0.690

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)0.0000.000

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 head0.013
GPT teacher head0.189
Teacher spread0.176 · 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

Citations25
Published2008
Admission routes1
Has abstractyes

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