An integration of topology optimization and conformal minimal surfaces for additively manufactured liquid-cooled heat sinks
Bibliographic record
Abstract
This study introduces a novel methodology that integrates thermal-fluidic topology optimization (TopOpt) with advanced latticing techniques to design high-performance heat sinks tailored for additive manufacturing (AM). Inspired by a liquid cooling case study utilizing triply periodic minimal surface (TPMS) lattices, developed through conformal mapping by the nTop-Puntozero design team, the methodology focuses on replicating, adapting, and optimizing the original design to enhance flow characteristics while maintaining effective heat dissipation, adhering to Design for Additive Manufacturing (DfAM) guidelines and constraints. Four design variants were evaluated: a conventional serpentine cold plate, a geometrically similar replica of the reference design, and two hybrid TopOpt-latticing heat sinks. Numerical simulations were conducted to characterize performance metrics across a range of fluid pumping powers ( P pump ≤ 0.15 Watts). The results demonstrate that the proposed approach significantly enhances thermal-hydraulic performance compared to conventional designs. Additionally, prototypes of the optimized heat sinks were successfully fabricated using laser powder bed fusion (LPBF), validating their manufacturability. This work highlights the potential of hybrid TopOpt-latticing methods in achieving superior heat sink performance and underscores the necessity for holistic design workflows to fully integrate optimization, manufacturability, and application-specific requirements. Future research will focus on further development of these workflows and experimental validation of the numerical findings.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".