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Record W4386072846 · doi:10.11159/htff23.146

A Study of the Application of Newtonian Fluids in Heat Transfer

2023· article· en· W4386072846 on OpenAlexvenueno aff
Dominga Guerrero, Surupa Shaw

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsNon-Newtonian fluidHeat transferThermodynamicsHeat transfer fluidMaterials scienceMechanicsComputer sciencePhysics

Abstract

fetched live from OpenAlex

This paper reviews the experimental and numerical findings on the impact of Newtonian fluids in heat transfer.Newtonian fluids are characterized by a constant viscosity that does not depend on the shear rate.They are commonly used in heat transfer applications because they exhibit predictable and stable flow behaviour, making them easier to model and analyze.A popular application of Newtonian fluids in heat transfer is electronic cooling.The heat generated by electronic components can be dissipated by circulating the Newtonian fluids, such as water or oil, through a cooling system.Newtonian fluids are also used as heat transfer fluid in heat exchangers, which use a series of tubes to transfer heat between two fluids separated by a conductive barrier.Newtonian fluids are also used for industrial processes like mixing and agitation in mixing tanks and reactors to transfer heat between different phases or to maintain a consistent temperature within the vessel.The predictable behaviour and low viscosity make Newtonian fluids efficient in heat transfer across the barrier.This study explores the use of Newtonian fluids in heat transfer applications in both industrial and consumer settings.

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 score0.038
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.205
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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Mechanical, Chemical, and Material EngineeringSame topicHeat Transfer and OptimizationFrench-language works237,207