Abstract

This presentation aims to highlight the synergy between experimental studies, Computational Fluid Dynamics (CFD) simulations, and Artificial Intelligence (AI) in developing high-fidelity, real-time digital twins that optimize and enhance the precision and performance of microfluidic systems. Digital twins are virtual replicas of physical systems that enable improved decision-making and performance optimization. These systems are particularly useful in complex scenarios where physical testing is costly or impractical. Digital twins allow for the exploration of ‘what-if’ scenarios and the prediction of outcomes under varying conditions, thus offering a comprehensive tool for modern engineering challenges. The impacts of effective digital twins are substantial, ranging from cost reduction and quality improvement to faster time-to-market, product differentiation, and enhanced product reliability.