Positional impacts of partial wall translations on hybrid nanofluid flow in porous media: Real Coded Genetic Algorithm (RCGA)

Positions of partially translating walls are highly effective for enhancing heat transfer. The present study intends to appraise the positional effects of partially translating cold walls of a bottom-heated enclosure on thermo-fluid phenomena and associated thermal characterization of Cu-Al2O3/water hybrid nanofluid packed porous matrix imposing an external magnetizing field. The cold walls are located either partly length or over the entire length of the sidewalls; the topmost wall is insulated. This novel configuration explores the enhanced heat transfer due to partially translating cold walls in lieu of whole wall motion under similar parametric conditions. Furthermore, the assessment of the different positions of the partially translating walls is also incorporated. The finite volume-based CFD code is developed to solve the governing equations. The thermo-fluid behaviors are analyzed systematically in an orderly fashion of parametric studies for a variety of relevant parameters like Richardson number (Ri), Reynolds number (Re), Darcy number (Da), Hartmann number (Ha), hybrid nanoparticles volume fractions (phi), and the directions and positions of partially translating cold walls. The results show distinct local behaviors allied with better heat exchanges compared to the completely cold sidewall motion. The implementation of partially translating cold walls located in the bottom-bottom position might be a good choice for achieving enhanced heat transportation. Moreover, the thermo-fluid flow phenomenon is greatly influenced by the intensity of the applied magnetic field. It has been also revealed that the addition of hybrid nanoparticles is beneficial for certain concentrations only that can attend an enhanced heat transfer. The downward translational speed with a bottom-bottom position is the superior option for accomplishing better heat exchange similar to 89.08%. Adopting the Real Coded Genetic Algorithm (RCGA), optimal heat transfer under a wide range of pertinent parameters is scrutinized for the upward and downward directions of translation.

Automated summary

This study evaluates the effects of partially translating cold walls on thermo-fluid phenomena and heat transfer performance in a bottom-heated enclosure. The results show that partially translating cold walls, especially in the bottom position, enhance heat transfer significantly. The study also highlights the influence of various parameters such as hybrid nanoparticles volume fractions and the direction of translation on thermo-fluid behaviors.