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Record W2607188914 · doi:10.1002/cjce.22872

Exact analysis of the transient free convection flow of nanofluids between two vertical parallel plates in the presence of radiation

2017· article· en· W2607188914 on OpenAlexvenueno aff
Marneni Narahari, Nikhila Alaparthi, Ioan Pop

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsNanofluidNusselt numberMaterials scienceMechanicsHeat fluxThermodynamicsThermal radiationCombined forced and natural convectionHeat transferNatural convectionTurbulencePhysicsReynolds number

Abstract

fetched live from OpenAlex

The transient free convection flow of nanofluids between two long vertical parallel plates has been investigated analytically in the presence of thermal radiation by considering prescribed wall temperature (PWT) and prescribed wall heat flux (PWHF) at one boundary while the other boundary is maintained at the initial fluid temperature. The exact analytical solutions for the nanofluid velocity, temperature, skin friction, and Nusselt number are derived in the form of rapidly converging series with the help of Laplace transform technique. Five different types of water‐based nanofluids containing copper (Cu), silver (Ag), copper oxide (CuO), titanium oxide (TiO 2 ), and aluminium oxide (Al 2 O 3 ) are considered in the analysis. The effects of nanoparticle volume fraction, radiation parameter, and temporal variable on the velocity, temperature, skin friction, Nusselt number, volume flow rate, and vertical heat flux have been discussed in detail. Some new heat and fluid flow characteristics of nanofluids have been presented. The present results can be used as a benchmark to validate the numerical solutions of transient free convection flow of nanofluids in a vertical channel for limiting cases. Also, the results are useful in gaining a deeper insight into the relevant practical systems.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.388
Threshold uncertainty score0.254

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 science0.0010.000
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 head0.011
GPT teacher head0.208
Teacher spread0.198 · 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

Citations16
Published2017
Admission routes1
Has abstractyes

Explore more

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