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

Abstract

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<Re<5000). The design objectives have been redefined as dimensionless parameters to assess heat dissipation, pressure drop, and heat sink weight. These modified objectives encompass thermal performance (TP), thermo-hydraulic performance (THP), and thermo-volumetric performance (TVP). The modeling process showed that both stepwise mixed selection (SMS) and GMDH-NN techniques exhibited comparable performance in most modeling scenarios. Nevertheless, the SMS approach demonstrated more reliability in modeling diverse objectives. Furthermore, the optimization results demonstrated that the optimal size of the perforations is strongly dependent on their shapes. In PFHSs with square perforations, approximately 54% of the Pareto points had a φ-value greater than 0.45. Meanwhile, in PFHSs based on circular perforations, more than 50% of the optimal points have φ less than 0.4. The MCDM-based analysis on various real-world scenarios indicated that using PFHSs with square-shaped perforations with Reynolds numbers around 2000 and considering a wide range of perforations' sizes could result in optimal designs.