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The rapid growth of AI is increasing heat loads and needs for high-performance thermal management. Liquid-cooled cold plates often face a thermal-hydraulic trade-off. Although topology optimization helps to alleviate this trade-off, it typically generates optimal fin architectures that are difficult to fabricate, with sub-100 μm feature scales. Here, we report a cold plate design workflow that couples topology optimization with electrochemical additive manufacturing to directly print high-resolution pure-copper coolers. Experiments show that the topology-optimized cold plate achieves up to 32% lower thermal resistance at a fixed flow rate and up to 68% lower pressure drop at equal thermal resistance compared with pin fin designs. A data center energy analysis indicates that, under the stated assumptions, the proposed solution requires only 1.1% of total data center energy use for cooling. By bridging the gap between computational design freedom and manufacturing capability, this approach provides a pathway for liquid cooling of future electronics.
Bazmi et al. (Fri,) studied this question.