Multiphysical optimization is particularly challenging when involving fluid–solid interactions with large deformations. While analytical approaches are commonly computational inexpensive but lack of the necessary accuracy for many applications, numerical simulations can provide higher accuracy but become very fast extremely costly. Experimental optimization approaches promise several benefits which can allow to overcome these issues in particular for application which bear complex multiphysics such as fluid–structure interactions. Here, we propose a method for an experimental optimization using genetic algorithms with a custom optimizer software directly coupled to a fully automatized experiment. Our application case is a biomimicking fish robot. The aim of the optimization is to determine the best swimming gaits for high propulsion performance in combination with low power consumption. The optimization involves genetic algorithms, more precise the NSGA-II algorithm and has been performed in still and running water. The results show a negligible impact of the investigated flow velocity. A subsequent spot analysis allows to derive some particular characteristics which leads to the recommendation to perform two different swimming gaits for cruising and for sprinting. Furthermore, we show that Exp-O techniques enable a massive reduction in the evaluation time for multiphysical optimization problems in realistic scenarios.

当涉及大变形的流固相互作用时，多物理优化尤其具有挑战性。虽然分析方法通常计算成本低廉，但缺乏许多应用所需的精度，但数值模拟可以提供更高的精度，但速度很快且成本高昂。实验优化方法具有多种好处，可以克服这些问题，特别是对于承受复杂多物理场（例如流固相互作用）的应用。在这里，我们提出了一种使用遗传算法进行实验优化的方法，其中自定义优化器软件直接耦合到全自动实验。我们的应用案例是仿生鱼机器人。优化的目的是确定最佳游泳步态，以实现高推进性能和低功耗。优化涉及遗传算法，更精确的 NSGA-II 算法，并已在静水和流水中进行。结果显示所研究的流速的影响可以忽略不计。随后的现场分析可以得出一些特定的特征，从而建议执行两种不同的游泳步态以进行巡航和冲刺。此外，我们还表明 Exp-O 技术可以大大减少现实场景中多物理优化问题的评估时间。