Numerical investigation of elliptical ribbed manifold microchannel heat sink and multi-objective optimization utilizing machine learning

Key Points

• The research aims to optimize the thermal performance of microchannel heat sinks using a combination of advanced computational techniques and machine learning.
• Coupled computational fluid dynamics (CFD) with artificial neural networks (ANN) and multi-objective genetic algorithm (NSGA-II).
• Derivation of new empirical correlations for Nusselt number and friction factor.
• Conducted vortex and boundary layer analyses to reveal flow and heat transfer mechanisms.
• The new design achieved a reduced temperature and pressure drop, enhancing overall efficiency.
• Empirical correlations improved predictive capabilities for Nusselt number and friction factor.
• Entropy generation analysis indicated reduced thermodynamic irreversibility in optimized designs.