Topology Optimization of Heat Sinks for High Efficiency Electronics Employing Simplified Convection Model
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Bibliographic record
Abstract
Electronics packaging relies heavily on thermal performance, or more specifically on the heat dissipation from electronic components to the environment, in order to increase the power capability and/or to prevent damage from overheating. Heat sinks are the most common method used to dissipate thermal energy from electronic components, utilizing both conduction and convection. The thermal performance of heat sinks can be improved using topological optimization methods. Heat sink designs can be optimized to extract heat energy more efficiently, thus increasing the capability of the product and/or the lifetime of the component by preventing heat related damages. In this study, passively cooled heat sink design optimization is performed employing a design-dependent simplified convection model for topology optimization, which assumes a uniform convection heat transfer coefficient on the surface of the structure. The design-dependent nature of this coefficient prevents the development of invalid or undesired solutions. As the optimization process iterates the design, the fluid-structure interface defining the convection boundary must be updated to reflect any changes in the design. Without this process, the convection becomes independent of the design, producing results that may not be favorable and would be more suited for conduction solely. This simplified analysis method significantly reduces the computational time and cost in comparison to a full Navier-Stokes or computational fluid dynamics approach. The optimization is implemented in SIMULIA-Tosca, which performs an adjoint sensitivity analysis and uses a gradient-based optimizer to search for an optimized design. Once the optimization process satisfies the convergence criteria, the final performance is verified through thermal analysis in SIMULIA-Abaqus. Several optimized designs are generated in this study, by varying manufacturing and volume constraints. The best performing optimized design in this paper resulted in a 24% reduction in maximum temperature, corresponding to a 41% increase in thermal efficiency compared to a traditional state-of-the-art finned heat sink design.
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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 | 0.000 | 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) | 0.000 | 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 it