Experimental Measurement and Numerical Modelling of Solid Network-Based Schoen’s I-Graph—Wrapped Package (I-WP) toward Cooling Heat Sink
Bibliographic record
Abstract
Triply Periodic Minimal Surfaces (TPMS) are periodic implicit surfaces with zero-mean curvature. Zero-mean surfaces are those that locally have minimum surface area for a given boundary. This study investigates the thermal performance of a TPMS geometry, Schoen’s I-graph Wrapped Package (I-WP), for application as a compact heat sink. While most prior work focuses on gyroid-based TPMS structures, this research explores the less-studied I-WP architecture fabricated from copper with a fixed porosity of 0.7. The influence of unit cell size on convective heat transfer and flow resistance is examined both experimentally and numerically. Experimental testing was conducted for unit cell sizes of 4 mm, 6 mm and 8 mm, while numerical simulations using COMSOL Multiphysics extended the analysis to sizes of up to 14 mm. Strong agreement between the measured and simulated results (within a 3% temperature deviation and 7% for the local Nusselt number) validated the numerical model. Empirical correlations for the average Nusselt number were developed based on both experimental and numerical data. The novelty of this paper lies in the performance of the experiment for different flow rates and unit cell sizes. The study identified 12 mm as the optimal unit cell size, yielding the highest Performance Evaluation Criterion (PEC) and demonstrating an effective balance between thermal enhancement and hydraulic performance. Furthermore, uniform temperature distributions observed across the flow direction underscore the advantages of TPMS structures over conventional metal foams. These findings establish the I-WP geometry as a promising candidate for next-generation, geometrically tunable heat exchangers that can be enabled by additive manufacturing.
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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".