This thesis presents a comprehensive study on the optimal design of hybrid pin-fin microchannel heatsinks for integrated microsystems using hybrid neural network-based multi-objective optimization techniques. The work addresses the increasing demand for efficient thermal management in microelectronic devices, where high heat fluxes and compact form factors necessitate advanced cooling solutions. By integrating hybrid pin-fin structures within microchannels, the research aims to enhance both thermal and hydrodynamic performance, overcoming limitations of conventional microchannel heat sinks that often face trade-offs between heat transfer efficiency and pressure drop.
Hybrid Neural Network-Based Optimization: The thesis introduces a hybrid neural network framework to perform multi-objective optimization, balancing competing objectives such as maximizing heat transfer (Nusselt number) and minimizing pressure drop. This approach enables the identification of optimal design parameters for the heatsink geometry, including pin-fin arrangement, size, and channel configuration.
Novel Hybrid Pin-Fin Microchannel Designs: The research explores innovative heatsink architectures that combine the benefits of pin-fin arrays and microchannels. These hybrid designs are shown to significantly improve the surface area for heat transfer while mitigating the adverse effects of increased flow resistance, a common drawback in traditional pin-fin or microchannel-only solutions.
Comprehensive Performance Evaluation: The thesis provides a detailed analysis of the thermal and hydrodynamic behavior of the proposed heatsinks. Numerical simulations and theoretical modeling are employed to assess the impact of geometric parameters on performance metrics. The findings demonstrate that carefully optimized hybrid pin-fin microchannel heatsinks can achieve substantial reductions in maximum device temperature and pressure drop, leading to enhanced cooling efficiency and reliability for integrated microsystems.
The outcomes of this research have significant implications for the design of next-generation cooling solutions in microelectronics, particularly in applications where space and energy efficiency are critical. The hybrid neural network-based optimization methodology offers a powerful tool for engineers to systematically explore complex design spaces and achieve balanced performance improvements. The demonstrated enhancements in thermal management directly contribute to the reliability, longevity, and operational stability of high-performance microchips and microsystems. Furthermore, the thesis lays the groundwork for future investigations into advanced heatsink topologies, such as those incorporating triply periodic minimal surfaces or novel lattice structures, which promise even greater gains in cooling performance. Overall, this work advances the state of the art in microchannel heat sink design and provides a robust foundation for further research and practical implementation in the field of electronic thermal management.