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Record W4385145130 · doi:10.1177/09544062231187788

Analysis of heat transportation in a convectively heated time-dependent <i> CuAl <sub>2</sub> O <sub>3</sub> -H <sub>2</sub> O </i> hybrid nanofluid with varying thermal conductivity

2023· article· en· W4385145130 on OpenAlexaff
Nimra Muqaddass, Fazle Mabood, Sabir Ali Shehzad, Fareeha Sahar, Irfan Anjum Badruddin

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · 2023
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsFanshawe College
Fundersnot available
KeywordsNanofluidBiot numberThermal conductivityNusselt numberHeat capacityThermodynamicsThermalMaterials scienceNonlinear systemMechanicsMathematicsPhysicsReynolds number

Abstract

fetched live from OpenAlex

This study presents a numerical investigation of the hybrid nanofluid [Formula: see text] and heat transportation over a convectively unsteady heated stretching sheet. Thermal conductivity is considered temperature dependent function. The set of non-dimensional differential equations is converted into nonlinear single independent variable equations by adopting the suitable transformations. The numeric results for the present set of transformed equations are executed through the RKF (Runge-Kutta-Fehlberg) process that is based on the shooting approach. The impact of several affecting parameters on temperature, local Nusselt number [Formula: see text] skin-friction factor [Formula: see text] and velocity are illustrated through graphs. A comparison has been done with the previously published material and decent agreement is achieved. The boundary-layer thickness and temperature profile are improved for both nanoparticles [Formula: see text] and [Formula: see text] added to the base fluid. However, the velocity profile illustrates an opposite attitude. The positive values of Biot number closer to the surface are obtained by increasing heat transportation rate.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.165
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.009
GPT teacher head0.197
Teacher spread0.188 · 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.

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

Citations4
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering ScienceSame topicNanofluid Flow and Heat TransferFrench-language works237,207