Optimization of the TPMS Heat Exchanger Toward Cooling the Heat Sink
Bibliographic record
Abstract
The subject of the current paper is cooling heat sinks using the TPMS structure. An experiment was conducted using water and a mixture of 10% vol. ethylene glycol in water, which was used to cool heat sinks in the presence of the TPMS structure. The gyroid was developed using 3D printing with three different porosities: 0.7, 0.8, and 0.9, respectively. The shell network is a single domain, and fluid is circulated at various flow rates. A comparison with the numerical model, as simulated using COMSOL software (version 6.2), showed good agreement. A uniform temperature distribution is a clear indication of uniform cooling. Then, the TPMS structure is changed from one domain to two unconnected domains, and a different flow rate is applied to each domain entry. This approach is unique in that it investigates the cooling of the heat sink with a two-domain structure, which has not been previously studied. The novelty of this paper lies in utilizing two TPMS structure domains to cool the heat sink. Thus, dual-domain TPMS heat sinks are implemented and optimized with separate inlets. Statistical testing of the model for the Nusselt number and the performance evaluation criterion is performed using Fisher’s statistical test to analyze variance (ANOVA). It was found that the cooling heat sink is more accurate with two-domain systems. The average Nusselt number polynomial is found to vary linearly with the two-inlet velocity, the porosity and the fluid Prandtl number. Similar linearity is found for the performance evaluation criterion. The optimum Nusselt number equals 77, the PEC equals 49 for a porosity of 0.85, and the Prandtl number is 36.9.
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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 | 0.000 |
| 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".