Machine Learning-Based Computational Framework for Microfluidic Device Design and Simulation

Abstract

To overcome the limits of traditional microfluidic design, this study provides a unique framework that combines machine learning with CFD simulations to expedite development and increase performance. By utilizing Random Forest algorithms, we developed a predictive model that analyzes a vast dataset derived from CFD simulations, capturing the complex fluid behavior within microfluidic systems. The integration of machine learning enables the prediction of key performance metrics, significantly reducing the time and computational resources traditionally required for design optimization. This method not only enhances the accuracy of device performance predictions but also offers a scalable framework for testing various design parameters efficiently. The results highlight the potential of machine learning to improve the precision and speed of microfluidic device development, making it a valuable tool for industries like biotechnology, medical diagnostics, and environmental monitoring. By addressing the challenges in simulating and optimizing microfluidic flow, this study provides a foundation for future innovations in lab-on-a-chip technologies, paving the way for more cost-effective and customizable devices. The proposed method offers a promising solution to accelerate research and development in microfluidics, with wide-reaching applications in diverse scientific and industrial fields.