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Record W2978745222 · doi:10.3390/inventions4040060

Investigation of Mixed Convection in a Cylindrical Lid Driven Cavity Filled with Water-Cu Nanofluid

2019· article· en· W2978745222 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInventions · 2019
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsNusselt numberStreamlines, streaklines, and pathlinesNanofluidReynolds numberMaterials scienceMechanicsHeat transferFinite volume methodCombined forced and natural convectionThermodynamicsVortexNatural convectionPhysicsTurbulence

Abstract

fetched live from OpenAlex

The present numerical research studies the effect of nano-materials in a lid-driven cylindrical cavity with rotation of circumferential top wall. The heat is transferred from two lateral walls to the domain by constant temperature conditions while other walls are kept isolated. The non-dimensional equations are solved by Finite Volume Method (FVM) and SIMPLEC method. The effect of Reynolds (Re = 100, 400, 1000), Ryleigh (Ra = 104, 105, 106) numbers are studied. In addition, the effect of concentration of nano materials ( ϕ = 0%, 1%, 5%), the Height Ratio (HR = 1, 0.5, 2) on Nusselt number, isotherm lines and streamlines are studied. The results show that Reynolds number also can change the effect of nano particles on the heat transfer rate. It is observed that the height ratio increase can improve the Nusselt number since the number and the size of vortices inside the cavity changes. In addition, increase of Ra number can change the flow structure inside the cavity which can help in increasing of Nusselt number.

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.

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 score0.057
Threshold uncertainty score0.358

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 head0.011
GPT teacher head0.193
Teacher spread0.182 · 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