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Chemoreception and chemotaxis of a three-sphere swimmer

arXiv 2205.02678 ↗First posted 5 May 2022cs.LG nlin.AO physics.flu-dyncited by 0

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Abstract (arXiv)

The coupled problem of hydrodynamics and solute transport for the Najafi-Golestanian three-sphere swimmer is studied, with the Reynolds number set to zero and P'eclet numbers (Pe) ranging from 0.06 to 60. The adopted method is the numerical simulation of the problem with a finite element code based upon the FEniCS library. For the swimmer executing the optimal locomotion gait, we report the Sherwood number as a function of Pe in homogeneous fluids and confirm that little gain in solute flux is achieved by swimming unless Pe is significantly larger than 10. We also consider the swimmer as an learning agent moving inside a fluid that has a concentration gradient. The outcomes of Q-learning processes show that learning locomotion (with the displacement as reward) is significantly easier than learning chemotaxis (with the increase of solute flux as reward). The chemotaxis problem, even at low Pe, has a varying environment that renders learning more difficult. Further, the learning difficulty increases severely with the P'eclet number. The results demonstrate the challenges that natural and artificial swimmers need to overcome to migrate efficiently when exposed to chemical inhomogeneities.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 49 references that are in the map. First posted on arXiv 5 May 2022.

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References · 3

  1. 2019A Theoretical Analysis of Deep Q-Learningcited by 215
  2. 2019Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learningcited by 114
  3. 2019Machine learning strategies for path-planning microswimmers in turbulent flowscited by 14

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