Forced Convection in Porous Medium Using Triply Periodical Minimum Surfaces
Bibliographic record
Abstract
Recent developments in the 3D printing of metals are attracting many researchers and engineers. Tailoring a porous structure using triply periodic minimum surfaces is becoming an excellent approach for cooling electronic equipment. The availability of metallic 3D printing encourages researchers to study cooling systems using porous media. In the present article, we designed a porous structure using a gyroid model produced using 3D printing. Porous aluminum has a 0.7, 0.8, and 0.9 porosity, respectively. The porous medium is tested experimentally using distilled fluid as the cooling liquid, while the structure is subject to bottom heating with a heat flux of 30,000 W/m2. A different inlet velocity from 0.05 m/s to 0.25 m/s is applied. On the numerical side, the porous medium is modeled as a porous structure, and only the Navier–Stokes equations and the energy equation were solved using the finite element technique. In addition, an excellent agreement between the experimental measurement and numerical calculation, an optimum porosity of 0.8 was obtained. The performance evaluation criterion led us to believe that pressure drop plays a significant role in heat enhancement for this type of gyroid structure. As the porosity increases, the boundary layer becomes more noticeable.
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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 | 0.000 |
| 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) | 0.000 | 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".