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Record W3201714011

Pore-Scale Modelling of Metal Foam Heat Exchanger

2021· article· en· W3201714011 on OpenAlexaff
Adam Reduan Chin, Jundika C. Kurnia, Agus P. Sasmito

Bibliographic record

VenueSPAST Abstracts · 2021
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsMcGill University
Fundersnot available
KeywordsHeat exchangerHeat transferMetal foamMaterials sciencePlate fin heat exchangerMicro heat exchangerMoving bed heat exchangerThermalPlate heat exchangerMechanical engineeringMechanicsThermodynamicsEngineeringMetalMetallurgyPhysics
DOInot available

Abstract

fetched live from OpenAlex

Heat exchangers have been essential in thermal energy systems. Increasing the heat transfer performance of heat exchangers has been the focus in thermal engineering field. This study aims to investigate transport processes in metal foam heat exchangers by adopting 3D pore-scale model which is expected to provide better accuracy and details on the transport processes in metal foam heat exchanger. The numerical study done has been initiated with the model development, followed by numerical implementation and finally numerical investigation. The numerical investigation reaffirms past studies that the presence of metal foam does bring enhancement to the heat transfer. However, it comes with the sacrifice on the huge change of pressure of the fluid. The adoption of pore-scale model revealed details of transport processes inside metal foam heat exchangers.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.602

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 head0.025
GPT teacher head0.214
Teacher spread0.189 · 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
Published2021
Admission routes1
Has abstractyes

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