Thermal analysis of ternary hybrid nanofluids for advanced cooling of AI chips and data centers using dual numerical techniques.

The problem of controlling the massive heat produced by high-performance AI processors has become more difficult due to the quick development of artificial intelligence (AI) and the exponential expansion of data centers. In addition to reducing processing efficiency, excessive heat endangers system dependability and energy sustainability. To mitigate this challenge, the present study investigates the application of ternary hybrid nanofluids (Tri-HNF: Al₂O₃-CuO-GNP/EG) as advanced coolants, comparing their performance with binary hybrid nanofluids (Di-HNF: Al₂O₃-CuO/EG) and mono nanofluids (NF: Al₂O₃/EG) in enhancing thermal regulation over a flat surface. The Collocation Chebyshev Method (CCM) and the Galerkin-weighted residual Method (GWRM), two separate numerical techniques included in Mathematica 11.3, were used to convert the governing nonlinear partial differential equations (PDEs) to ordinary differential equations (ODEs) and solve them numerically. The excellent agreement between the two methods demonstrates the consistency and accuracy of the numerical schemes. The effects of magnetic field, viscosity, suction, radiation, and heat generating factors on the flow and temperature fields were revealed using parametric experiments. Findings show that the Tri-HNF exhibits superior thermal performance due to the synergistic effects of Al₂O₃ (enhanced stability), CuO (excellent thermal conductivity), and graphene nanoplatelets (GNP) (exceptional heat transport efficiency). Compared to Di-HNF and NF, the Tri-HNF achieves higher Nusselt numbers, improved heat dispersion, and lower thermal resistance. In summary, this work demonstrates that Tri-HNF provide an efficient and reliable solution for cooling AI chips, microelectronics, and data centers, offering a pathway toward sustainable and thermally stable next-generation computing systems.