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.
翻译:研究Najafi-Golestanian三肢游泳运动员的流体动力学和溶液迁移问题,研究的还有Reynolds 编号定为零,P='eclet numbers(Pe)介于0.06至60之间。采用的方法是以FENICS图书馆为基础的有限元素代码对问题进行数字模拟。对于执行最佳软体运动的游泳运动员来说,我们报告Sherwood编号是Pe在同质液中的函数,并证实除非Pe大大大于10,否则通过游泳在溶液流中不会取得多大的收益。我们还认为游泳者是在具有集中度梯度的液体中运动的学习剂。Q-学习过程的结果表明,学习流动(以迁移作为奖励)比学习化工税(以溶液通量增加作为奖励)要容易得多。染色税问题,即使在低 Pe,环境也使得学习更加困难。此外,学习困难随着P\'缩数的增加,学习困难也严重增加。结果表明,当自然和人工游泳需要克服向化学的迁移时,自然和人工运动需要克服挑战。