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Record W2331775965 · doi:10.1115/icnmm2011-58187

Analysis of Energy and Exergy for Mixed Convection Flow in Microstructure Filled Vented Enclosures

2011· article· en· W2331775965 on OpenAlexaff
Muinul H. Banna, A. K. M. Sadrul Islam, Abbas S. Milani, Mina Hoorfar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsHeat transferPorous mediumExergyEnclosureMechanicsMaterials scienceThermodynamicsVolumetric flow rateFluid dynamicsNatural convectionInletForced convectionNuclear engineeringMechanical engineeringPorosityEngineeringElectrical engineeringPhysicsComposite material

Abstract

fetched live from OpenAlex

Understanding heat transfer through saturated porous media is of great importance to many engineering and geophysical systems such as cooling the electronic devices and solar power collectors, and post-accidental heat removal in nuclear reactors. Large numbers of research studies have been and are conducted on the expanding field of porous media due to the high rate of heat transfer in these systems. Despite the efforts made towards the study of the mechanics of fluid flow through porous media, little is studied the rate of exergy which is the only factor presenting the rate of reusable energy potentially produced by a heat generating body. The objective of this study is to develop a design of experiment to carry out a numerical analysis of heat transfer in a rectangular enclosure filled with a saturated porous medium. The optimum heat transfer rate will be obtained for various configuration-related parameters, namely different inlet to outlet ratios and different inlet width to cavity width ratios. These parameters will be optimized to achieve maximum rate of heat transfer and minimum rate of entropy generation. The results of this study will also help to determine relationships for predicting the heat transfer characteristics of the enclosure.

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 score0.177
Threshold uncertainty score0.283

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 head0.195
Teacher spread0.185 · 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
Published2011
Admission routes1
Has abstractyes

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