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Chemoreception and chemotaxis of a three-sphere swimmer
Machine Learning · Submitted 5 May 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The results demonstrate the challenges that natural and artificial swimmers need to overcome to migrate efficiently when exposed to chemical inhomogeneities.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | 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. | contribution 0.76 | 0.73 | 0.04 | asserted 0.93 |
| s1 | The adopted method is the numerical simulation of the problem with a finite element code based upon the FEniCS library. | method 1.00 | 0.81 | 0.02 | asserted 0.99 |
| s2 | 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. | result 1.00 | 0.94 | 0.25 | asserted 0.99 |
| s3 | We also consider the swimmer as an learning agent moving inside a fluid that has a concentration gradient. | contribution 0.93 | 0.72 | 0.03 | asserted 0.96 |
| s4 | 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). | result 1.00 | 0.94 | 0.28 | asserted 1.00 |
| s5 | The chemotaxis problem, even at low Pe, has a varying environment that renders learning more difficult. | interpretation 0.64 | 0.80 | 0.11 | asserted 1.00 |
| s6 | Further, the learning difficulty increases severely with the P\'eclet number. | result 0.99 | 0.91 | 0.15 | asserted 1.00 |
| s7 | The results demonstrate the challenges that natural and artificial swimmers need to overcome to migrate efficiently when exposed to chemical inhomogeneities. | interpretation 1.00 | 0.67 | 0.54 | asserted 1.00 |
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Original abstract
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.
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