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NUMERICAL STUDY OF NANOFLUID FLOW AND HEAT TRANSFER IN A PLATE HEAT EXCHANGER

2013· article· en· W2003031932 on OpenAlexaff
Iulian Gherasim, Nicolas Galanis, Cong Tam Nguyen

Bibliographic record

VenueComputational Thermal Sciences An International Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversité de MonctonUniversité de Sherbrooke
Fundersnot available
KeywordsNanofluidLaminar flowTurbulenceMaterials scienceMechanicsHeat transferHeat exchangerReynolds numberThermodynamicsHeat transfer enhancementDynamic scraped surface heat exchangerFluid dynamicsTurbulence kinetic energyHeat transfer coefficientCritical heat fluxPhysics

Abstract

fetched live from OpenAlex

This paper presents a numerical investigation of the flow and heat transfer behavior of two nanofluids, CuO−water and Al2O3−water, inside a plate heat exchanger. Both laminar and turbulent flows are studied under steady-state conditions. In the turbulent regime, the Reynolds-averaged Navier−Stokes-based realizable к-ε turbulence model was used. The homogeneous single-phase fluid model was employed to characterize the nanofluids. All fluid properties were considered temperature dependent. The adopted unstructured mesh possessed approximately 9.63 × 106 elements and was used for both the laminar and turbulent flows. The results show that a considerable heat transfer enhancement was achieved using these nanofluids, and the energy-based performance comparisons indicate that some of them represent a more efficient heat transfer medium for this type of application. In general, all nanofluids caused higher pressure losses due to friction than water.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.368

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 communication0.0000.001
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 head0.022
GPT teacher head0.267
Teacher spread0.245 · 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 designSimulation or modeling
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

Citations11
Published2013
Admission routes1
Has abstractyes

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