Performance Evaluation of Triply Periodic Minimal Surface Heat Exchangers Using Nanofluids at High Flow Rates for Enhanced Energy Efficiency
Bibliographic record
Abstract
Triply Periodic Minimal Surface (TPMS) heat exchangers have attracted significant attention for their high surface area and effective thermal performance. This study evaluates the performance of TPMS heat exchangers under turbulent flow conditions using aluminum (Al) and silver (Ag) materials with pure water and nanofluid as working fluids. The implementation of Ag TPMS structures resulted in approximately 15% enhancement in thermal performance compared to Al structures due to superior thermal conductivity. The introduction of nanofluid (0.6% volume concentration) improved overall heat transfer efficiency by 12% compared to pure water. Performance evaluation criteria (PEC) analysis demonstrated that Ag TPMS structures achieved up to 30% higher values than Al structures. Temperature homogeneity analyses revealed significant improvements, with Al TPMS structures showing a 24% reduction in temperature variation when using nanofluid, while Ag TPMS structures exhibited up to 40% better temperature uniformity. Computational fluid dynamics analyses validated the experimental findings with deviations less than 7%, confirming the model’s reliability. These results demonstrate the significant potential of TPMS structures in high-performance cooling applications and provide valuable insights for future heat exchanger designs. The enhanced efficiency of thermal management system (TMS) heat exchangers using nanofluids contributes to reduced energy consumption, supporting environmentally conscious decision-making in industrial and energy systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".