Zero-Water Thermal Architecture for High-Density Artificial Intelligence Data Centers

Abstract The rapid growth of artificial intelligence has produced a corresponding increase in computational power density and, consequently, thermal power density within modern data centers. Conventional air cooling becomes increasingly difficult as accelerator-level heat dissipation reaches hundreds of watts to kilowatts per device, while many high-density facilities increasingly employ liquid-assisted cooling and water-intensive heat-rejection systems. Although liquid cooling can provide excellent thermal performance, water consumption presents a significant sustainability challenge, particularly in regions experiencing water stress. This paper proposes a Zero-Water AI Data Center (ZW-AIDC) architecture in which heat generated by high-density AI accelerators is transported through a sealed, non-water, two-phase thermal network and rejected to the atmosphere through dry air-cooled condensers. The proposed architecture eliminates evaporative cooling towers, chilled-water distribution loops, and direct water consumption for heat rejection. At the device level, vapor chambers and high-conductivity thermal interfaces spread localized heat. At the rack level, a sealed two-phase working-fluid loop transports thermal energy away from the electronics. At the facility boundary, dry finned condensers reject heat directly to ambient air. A thermodynamic and heat-transfer framework is developed for estimating device heat flux, two-phase heat-transfer requirements, condenser area, working-fluid mass flow, fan power, compressor requirements, and overall cooling coefficient of performance. The architecture is further evaluated for hypothetical 1 MW, 10 MW, and 100 MW IT loads. The analysis shows that zero-water operation is physically feasible, but eliminating water does not eliminate the thermal-management cost: the principal penalties are increased dry-condenser area, increased airflow requirements, sensitivity to ambient temperature, and potentially higher parasitic electrical consumption. The proposed architecture therefore represents a trade between water independence and thermal-infrastructure efficiency. The paper establishes a framework for future experimental validation and optimization of zero-water cooling systems for high-density AI computing.