Enhancing heat dissipation and temperature uniformity of microchannel heat sinks using fractal gradient honeycomb-reverse Tesla valve configuration

This study introduces a fractal gradient honeycomb-reverse Tesla valve configuration (HC-RTV-GD) to enhance heat transfer and temperature uniformity in high-flux thermal management systems. Through comparative analysis with conventional Tesla valves and honeycomb structures, the HC-RTV-GD leverages hierarchical bifurcation and controlled turbulence generation to achieve superior heat transfer performance. At high Reynolds numbers, the design significantly reduces thermal resistance and suppresses maximum wall temperatures while flattening longitudinal temperature gradients, mitigating thermal stress risks. The improved temperature uniformity stems from gradient-driven flow redistribution, which minimizes stagnant zones and sustains coolant velocity in secondary channels. Thermal improvements come with hydraulic trade-offs: the gradient geometry amplifies flow resistance through localized vortices, elevating pressure drop and friction coefficients compared to conventional designs. Performance evaluation confirms the HC-RTV-GD’s viability exclusively in high-flux scenarios, where heat transfer gains outweigh pumping penalties. A neural network-enhanced optimization framework further identifies optimal coolant parameters, balancing thermal and hydraulic efficiency. The HC-RTV-GD advances cooling system design by strategically combining geometric complexity with turbulence control, prioritizing thermal uniformity in extreme heat flux environments.