Session: 20-17-01: Rising Stars of Mechanical Engineering

Paper Number: 172871

Smart Artificial Microswimmers Reveal Emergent Behavior in Finite Clusters 

Artificial microswimmers modeled after microorganisms hold great potential for biomedical applications such as minimally invasive surgery and targeted drug delivery. Most of these applications require using multiple microswimmers due to the complexity of tasks and the need for sufficient mass transfer. Existing models studying the emergent behavior of microswimmer clusters typically focus on either pair-wise hydrodynamic interactions or adopt continuum approaches, which often omit important dynamics that govern the collective behavior of finite clusters. However, both hydrodynamic interactions and fluid rheology significantly influence not only the viscous response of individual swimmers but also that of the cluster as a whole. This complexity is further compounded by the non-Newtonian properties of many bodily fluids, making it difficult to extrapolate cluster behavior using models that consider only one of these effects in isolation. Elucidating the physics of swimming in finite clusters within Newtonian and complex fluids is therefore essential for advancing microswimmer-based biomedical systems. This project aims to develop a comprehensive understanding of the emergent behavior of finite microswimmer clusters under realistic conditions. By integrating experimental methods, computational fluid dynamics (CFD), and machine learning, we will evaluate the limits of current modeling approaches and uncover the dominant fluid dynamic mechanisms in environments that closely mimic biological conditions.

We will develop and utilize fully autonomous clusters of Smart Artificial Microswimmers (SAMs) to investigate emergent motion patterns at low Reynolds numbers in both Newtonian and non-Newtonian fluids. These patterns will be analyzed using CFD models validated against experimental data. High-resolution imaging and flow visualization through particle image velocimetry (PIV), combined with simulation-derived fields such as viscous stress, pressure, viscosity, swimming efficiency, and velocity, will allow us to thoroughly investigate the fluid-structure interactions at play. Our study will examine microswimmer clusters with varying populations and packing densities, performing prescribed gaits in controlled formations to systematically characterize the resulting dynamics. We will also conduct experiments using SAMs with machine learning-based autonomy, where gaits are not externally prescribed. Comparing the behavior of these autonomous swimmers with AI-coupled simulations will allow us to evaluate the ability of learning-based models to predict and reproduce complex emergent behavior. Through the integration of experimental observations and computational tools, this project will reveal how swimmer autonomy, cluster structure, and fluid properties combine to shape collective dynamics. These insights will inform the future design and deployment of microswimmer systems in real biomedical environments and open new directions in control, sensing, and navigation in complex media.

Presenting Author: Ebru Demir Lehigh University

Presenting Author Biography: Ebru Demir is an Assistant Professor of Mechanical Engineering and Mechanics in Lehigh University’s P.C. Rossin College of Engineering and Applied Science. Her research bridges thermo/fluids, biomedical engineering, and robotics, with a focus on artificial microswimmers for biomedical applications. Her research approach combines numerical simulations and experiments to investigate locomotion in complex fluids and biological environments, advancing applications such as targeted drug delivery, diagnostics, and waste elimination.

Demir’s interdisciplinary research leverages fluid mechanics/dynamics and machine learning techniques to address challenges in robotics, biomedical engineering, and advanced manufacturing. She investigates locomotion across multiple scales, from bioinspired artificial microswimmers to scaled-up intelligent robots, and contributes to flow-based medical device development through her experience in experimental design, simulations, and rapid prototyping.

Demir received both her BS and PhD in Mechatronics Engineering from Sabanci University (Istanbul, Turkey). She completed a postdoctoral research fellowship in the Department of Mechanical Engineering at Santa Clara University before joining Lehigh.

Authors: