Effectiveness in Cooling a Heat Sink in the Presence of a TPMS Porous Structure Comparing Two Different Flow Directions
Bibliographic record
Abstract
The triply periodic minimal surface (TPMS) is receiving much interest among researchers. The advantage of using this TPMS structure is the ability to design a structure based on engineering need. In the present context, experimental measurement was conducted and compared with numerical models using a foam porous medium and TPMS porous structure, leading to an accurate calibration of the model. A porous medium, metal foam, was heated experimentally at the bottom, and forced convection was investigated for different heating conditions. Then, the porous foam was replaced with a TPMS, and the experiment was repeated under similar conditions. The experimental data were compared with the numerical model using COMSOL software. Besides the model’s accuracy, the TPMS showed a uniform heating condition contrary to the metal foam case. At a later stage, the numerical model was used to investigate the importance of flow direction (two flow directions) in cooling hot surfaces. The first flow was parallel to the hot surface, and the second perpendicular to the hot surface. The TPMS structure was located on the top of the hot surface and acted as a fin in both cases. The Nusselt number exceeded 80 in the presence of the TPMS. As the porosity of the TPMS decreases below 0.7, a more considerable pressure drop is observed. The performance evaluation criterion was found to be greater than 70 when the porosity of the TPMS structure was 0.8.
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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".