潜入一个简单的鳗状游泳者的敏感性
Diving into a simple anguilliform swimmer's sensitivity
摘要：
水上运动的计算模型范围从2D中的个体适度简单的游泳者到复杂的3D个体游泳者，再到试图解析集体行为动力学的复杂的多游泳者模型。这些模型中的每一个都包含模型输出固有地依赖于的大量模型输入参数，即，各种游泳性能指标。本文针对一个理想化的、简单的二维鳗形游动模型，研究了游动性能对参数的敏感性。这里考虑的游泳者通过动态地改变其身体曲率向前传播，类似于\textit{C. elegan}的运动。参数的敏感性进行了探讨，相对于流体尺度（雷诺数，Re），冲程（波动）频率，以及运动学参数控制的速度和加速度的每一个upstroke和downstroke。总共进行了5000次流体-结构相互作用模拟，每次模拟都使用通过Sobol序列选择的唯一参数组合。因此，使用Sobol敏感性分析进行全局敏感性分析。结果表明，游泳运动员的成绩是最敏感的变化，其中风频率。通过将整个3D参数空间的性能指标投影到特定的2D子空间上，发现了游泳性能的趋势，并确定了游泳效率的Pareto最优前沿。这一工作是对文献[1]中同一模型的参数探索的自然推广。
英文摘要：
Computational models of aquatic locomotion range from individual modest simple swimmers in 2D to sophisticated 3D individual swimmers to complex multi-swimmer models that attempt to parse collective behavioral dynamics. Each of these models contain a multitude of model input parameters to which the model outputs are inherently dependent, i.e., various swimming performance metrics. In this work, the swimming performance's sensitivity to parameters is investigated for an idealized, simple anguilliform swimming model in 2D. The swimmer considered here propagates forward by dynamically varying its body curvature, similar to motion of a \textit{C. elegan}. The parameter sensitivities were explored with respect to the fluid scale (Reynolds number, Re), stroke (undulation) frequency, as well as a kinematic parameter controlling the velocity and acceleration of each upstroke and downstroke. In total, 5000 fluid-structure interaction simulations were performed, each with a unique parameter combination selected via a Sobol sequence. Thus, global sensitivity analysis was performed using Sobol sensitivity analysis. Results indicate that the swimmer's performance is most sensitive to variation in its stroke frequency. Trends in swimming performance were discovered by projecting the performance metrics from the overall 3D parameter space onto particular 2D subspaces and Pareto-like optimal fronts were identified for swimming efficiency. This work is a natural extension of the parameter explorations of the same model from [1].
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
