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Record W4225121147 · doi:10.1149/10701.7713ecst

Pore-Scale Modeling in Metal Foam Heat Exchanger

2022· article· en· W4225121147 on OpenAlexaff
Adam Reduan Chin, Jundika C. Kurnia, Agus P. Sasmito

Bibliographic record

VenueECS Transactions · 2022
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsMcGill University
Fundersnot available
KeywordsMetal foamHeat transferHeat exchangerMaterials scienceMicro heat exchangerPressure dropMechanicsPlate heat exchangerComputer simulationPlate fin heat exchangerMechanical engineeringComposite materialEngineeringPorosity

Abstract

fetched live from OpenAlex

Increasing heat transfer performance of heat exchangers has been the primary focus in the thermal engineering field. Various improvement strategies have been proposed. One that is gaining considerable attention recently is application of metal foam. It offers larger heat transfer area and flow disruption that enhance overall heat transfer performance. All the while, most studies on metal foam are either experimental or simplified numerical model adopting homogeneous approach model which does not provide high fidelity information. Thus, this study is conducted to numerically investigate transport processes in metal foam heat exchangers by adopting three-dimensional pore-scale model which is expected to provide better accuracy and details on the transport processes in a metal foam heat exchanger. The numerical study was 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 significant increase in pressure drop. 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.522
Threshold uncertainty score1.000

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)0.0010.000

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 head0.022
GPT teacher head0.215
Teacher spread0.194 · 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.

Study designSimulation or modeling
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
Published2022
Admission routes1
Has abstractyes

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