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Record W2805444336 · doi:10.11159/ffhmt18.175

Role of Curvature on Heat Flow Visualization and Irreversibilities during Natural Convection in Enclosures

2018· article· en· W2805444336 on OpenAlexvenueno aff
Damodara Priyanka, Tanmay Basak

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2018
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCurvatureNatural convectionFlow visualizationVisualizationMechanicsFlow (mathematics)ConvectionConvective flowHeat flowComputer sciencePhysicsThermodynamicsThermalGeometryMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

This article presents a detail analysis of heatlines and entropy generation during natural convection in various enclosures with curve (concave and convex) walls. The dimensions of enclosures are fixed in such a way that thedimensionless area of the cavity is one and the dimensionless length of the wall is one. Two heating strategies are considered such as (a) type 1: hot bottom wall, cold side walls in the presence of adiabatic top wall and (b)type 2: hot left, cold top and bottom walls in the presence of adiabatic right wall. Numerical simulations have been carried out for fluid with Prandtl number Pr = 0. 7 at different Rayleigh number (10 3 Ra 10 5 ). The distributions of isotherms, streamlines, heatlines and entropy generation due to heat transfer and fluid friction are compared for curved walled enclosures with those of square enclosure. The effect of Ra on the total entropy generation, average Bejan number and average Nusselt number is illustrated for considered enclosures involving both the heating strategies. The optimal configuration and optimal heating strategy is chosen based on the less entropy generation rate and higher heat transfer 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)0.000
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 score0.122
Threshold uncertainty score0.514

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 head0.010
GPT teacher head0.225
Teacher spread0.214 · 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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the ... International Conference on Fluid Flow, Heat and Mass TransferSame topicHeat Transfer and Boiling StudiesFrench-language works237,207