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Exposed-area dependent forced convective heat transfer in periodic lattice structures

2025· article· en· W4406274710 on OpenAlexafffund
Jiaxi Zhao, Kim Leong Liaw, Mohammad Zolfagharroshan, Minghan Xu, Abdolhamid Akbarzadeh, Agus P. Sasmito

Bibliographic record

VenueInternational Journal of Heat and Mass Transfer · 2025
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceConvective heat transferConvectionMechanicsHeat transferThermodynamicsPhysics

Abstract

fetched live from OpenAlex

This study presents numerical investigations of heat transfer and fluid flow in metal foams made of distinctive topologies. Two bio-inspired structures referred to as sponge and body-centered sponge (BCS), which exhibit identical thermophysical properties along three orthogonal axes, are proposed. Initially, the pore-scale computational model is validated, showing a deviation of less than 3 % when compared to the existing literature. Despite the intricate conductive pathways of the BCS structure, it is found to be a highly promising porous material as a heat exchanger, exhibiting the highest Nusselt number (twice as much as in cubic structures in water flow), friction factor (4.7 times as that in cubic structures in water flow), and performance evaluation criterion (PEC) (1.2 times as that in cubic structures in water flow). In addition, it is concluded that the large exposed area of fluid-foam walls and the high tortuosity of the BCS structure significantly enhance the Nusselt number . This complexity increases the frequency of flow deflection and stagnation, contributing to improved heat transfer performance. Finally, empirical equations for the Nusselt number and friction factor as a function of the unified exposed area parameter, Reynolds number, and Prandtl number have been developed with acceptable precision, accompanying with the R-squared value higher than 0.97.

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 score0.089
Threshold uncertainty score0.834

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 head0.238
Teacher spread0.229 · 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
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Heat and Mass TransferSame topicHeat and Mass Transfer in Porous MediaFrench-language works237,207