| Title |
Design Strategies for Cooling Infrastructure Modules and Energy Performance Assessment of Direct Liquid Cooling in AI Data Centers |
| DOI |
https://doi.org/10.5659/JAIK.2026.42.7.289 |
| Keywords |
AI Data Center; Direct Liquid Cooling; Cooling Module & Infrastructure; Energy Efficiency; Power Usage Effectiveness (PUE) |
| Abstract |
This study investigates the design strategy and energy performance of direct liquid cooling (DLC) infrastructure for high-density AI data
centers. As AI accelerators and high-performance computing systems increase rack-level power density, conventional air-cooling systems face
limitations in cooling capacity and energy efficiency. To address these challenges, this study proposes a modular cooling infrastructure
architecture based on chip-level cold plate cooling and evaluates its performance under different cooling configurations. Three cooling
scenarios were considered for a 10 MW edge data center: a conventional air-cooling system using fan wall units (FWU), a retrofit liquid
cooling system combining single-phase DLC with rear-door heat exchangers (1P DLC + RDHx), and a two-phase direct-to-chip system (P2P
DLC). The cooling infrastructure configuration and energy consumption of each system were analyzed using system-level energy modeling.
The results show that the required white space decreases from approximately 3,000 m2 for the air-cooling system to 300 m2 for the 1P DLC
system and 120 m2 for the P2P DLC system. In addition, the PUE improved from 1.305 to 1.104 and 1.082, respectively. These results
indicate that liquid-based cooling systems can significantly improve both spatial efficiency and energy performance in high-density AI data
centers. |