Parametric Shape Optimization of Flagellated Micro-Swimmers Using Bayesian Techniques

TL;DR

This paper introduces a novel Bayesian optimization framework combined with Free-Form Deformation to optimize the shape of helical micro-swimmers, achieving improved speed and efficiency for various swimmer configurations.

Contribution

It presents an innovative shape optimization method for micro-swimmers using Bayesian techniques and deformation, enabling discovery of new optimal swimmer geometries.

Findings

Identified new optimal shapes for micro-swimmers.

Achieved enhanced average speed and efficiency.

Compared optimized shapes with biological counterparts.

Abstract

Understanding and optimizing the design of helical micro-swimmers is crucial for advancing their application in various fields. This study presents an innovative approach combining Free-Form Deformation with Bayesian Optimization to enhance the shape of these swimmers. Our method facilitates the computation of generic swimmer shapes that achieve optimal average speed and efficiency. Applied to both monoflagellated and biflagellated swimmers, our optimization framework has led to the identification of new optimal shapes. These shapes are compared with biological counterparts, highlighting a diverse range of swimmers, including both pushers and pullers.