MétaCan
Menu
Back to cohort
Record W2745815786 · doi:10.11159/ffhmt17.182

Numerical Investigation into the Effect of Fins on Fluid Natural Convection in Coaxial Annuli

2017· article· en· W2745815786 on OpenAlexvenueno aff
Mohammad Reza Tavakoli, Mahsa Farzaneh, Arash Shadlaghani

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2017
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsNatural convectionCoaxialMaterials scienceMechanicsFinConvectionMechanical engineeringPhysicsEngineeringComposite material

Abstract

fetched live from OpenAlex

This paper attempted to numerically examine the involvement of serrated fins on natural convection heat transfer between coaxial cylinders. The outer channel of annular cylinders was circular, while the inner channels involved three cross-sections including circular, square and triangular. As two geometric constraints, the area of annular cylinders and the diameter of outer channel were assumed to be identical in each scenario explored in this study. The fins had equal areas placed on the inner surface, so as to compare their effects on thermal properties of annular cylinders under constant temperature boundary within the range of Rayleigh numbers from 10 5 to 10 8 . The results indicated that higher a Rayleigh number is directly correlated with higher convection heat transfer coefficient of surfaces. However, the inclusion of fins reduced the rate near the fins, thus mitigating the heat transfer coefficient of inner channel. This trend intensified at higher Rayleigh numbers. Therefore, the involvement of fins at lower Rayleigh numbers brings about greater efficiency in heat transfer. The comparison of fins in terms of efficiency revealed that maximum heat is transferred when the fins have been mounted on a circular channel.

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 score0.095
Threshold uncertainty score0.630

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 science0.0010.000
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 head0.013
GPT teacher head0.231
Teacher spread0.218 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the ... International Conference on Fluid Flow, Heat and Mass TransferSame topicNanofluid Flow and Heat TransferFrench-language works237,207