CFD-Based Design of Experimental Analysis of Al2O3–Water Nanofluid Performance in Data Center Cooling Applications

Driven by the limitations and growing scarcity of conventional water-based cooling, this study explores nanofluids as a promising high-performance alternative due to their superior thermal properties. This study presents a computational fluid dynamics (CFD) investigation of Al2O3/water nanofluid-based cooling in a data center environment with hot server cabinets, simulated using the SIMPLE algorithms. The effects of the Richardson number (Ri), cabinet size (BR), and nanoparticle volume fraction (Φ) on the average Nusselt number were systematically analyzed using ANOVA, the full-factorial design, and response surface methodology. The model shows high accuracy (R2 = 99.32%, adjusted R2 = 98.40%). ANOVA identifies the Richardson number as the dominant factor (75%), followed by the cabinet size (25%), with the volume fraction having minimal influence. The results highlight the key role of buoyancy and geometry in optimizing efficient nanofluid-based data center cooling. The rapid growth of large-scale data centers, such as those supporting advanced platforms including ChatGPT, has driven significant cooling demands and heightened concerns about water consumption. To address the limitations and scarcity of conventional water-based cooling, nanofluids have emerged as a promising alternative due to their enhanced thermal properties. This work is original in its integration of CFD-based nanofluid cooling with advanced statistical optimization techniques specifically applied to a data center environment with hot server cabinets. Additionally, this study offers practically optimized operating conditions for sustainable data center cooling, addressing the real-world challenges of water scarcity and energy-efficient thermal management.