Hyper-Efficient Microfluidic Cooling System Optimization via Dynamic Algorithm Selection and Bayesian Calibration (DASC-BC)

**Abstract:** This paper proposes a novel framework, Dynamic Algorithm Selection and Bayesian Calibration (DASC-BC), for optimizing microfluidic cooling systems in high-power density electronics. Leveraging a multi-layered evaluation pipeline, the system dynamically selects and optimizes amongst diverse computational fluid dynamics (CFD) and machine learning algorithms to achieve unprecedented cooling efficiency and reliability. By integrating a human-AI feedback loop and Bayesian calibration, DASC-BC enables continuous self-improvement and robust performance across varied operational conditions, demonstrating a significant advancement towards scalable and commercially viable microfluidic thermal management solutions. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*