Combining artificial intelligence and computational fluid dynamics for optimal design of laterally perforated finned heat sinks

The efficient design of heat sinks is a severe challenge in thermo-fluid engineering. A creative and innovative way is applying lateral perforations to parallel finned heat sinks. The significance of achieving an optimal design for perforated finned heat sinks (PFHSs) has inspired the present authors to introduce a novel hybrid designing approach that combines computational fluid dynamics (CFD), machine learning (ML), multi-objective optimization (MOO), and multi-criteria decision-making (MCDM). The design variables considered include the size (0.25<φ<0.5) and shape (square, circular, and hexagonal) of the perforations, as well as the airflow Reynolds number (2000