Exposed-area dependent forced convective heat transfer in periodic lattice structures
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".