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Abstract
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This study presents a numerical investigation of entropy generation in a microchannel heat sink with various dimpled surface geometries using a biologically synthesized water–silver nanofluid. A two-phase mixture model was employed to analyze six geometries (spherical, elliptical, egg-shaped, and super-spherical) for Reynolds numbers between 500 and 2000 and nanoparticle volume fractions of 0–1%. The results reveal that increasing the Reynolds number enhances convective heat transfer, reducing the average CPU temperature from 295.94 K to 294.62 K in the plain channel, while adding 1% Ag nanoparticles yields a further temperature drop of up to 0.08 K (≈0.03%). Thermal entropy generation decreases substantially with nanoparticle loading – by approximately 35% as nanoparticle volume fraction increases from 0 to 1% – confirming the nanofluid’s role in improving thermal conduction and reducing temperature gradients. In contrast, frictional entropy generation rises sharply with Reynolds number but can be reduced by up to 12.4% through nanofluid addition at Re = 2000. Among all tested geometries, the egg-spherical (Case C2) configuration achieved the best thermodynamic performance, with the lowest total entropy generation (1.375 × 10− 5 W/K) and the most uniform temperature distribution, corresponding to about 6.8% lower total entropy than the plain channel. The super-spherical (Case D) geometry minimized viscous losses, while spherical dimples (Case A) provided stronger heat transfer enhancement at the expense of higher frictional entropy. These findings offer quantitative guidelines for optimizing microchannel heat sink geometries and nanofluid formulations toward energy-efficient electronic cooling.
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