This article presents a hybrid IBM/CGM scheme for the study of two-dimensional orbital regimes of flexible and inextensible filaments in low-Reynolds number shear flows. The study includes a parametric analysis of the mechanical response for a range of the filament length, bending rigidity and shear-strain rate. The results are compared to theoretical estimates and previous numerical and experimental analyses. These results are subsequently used to develop a prediction model using Artificial Neural Networks to effectively forecast the orbital regime of a filament immersed in shear flow.

中文翻译：

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使用浸入边界/粗粒度方法和人工神经网络对二维粘性剪切流中不可拉伸长丝进行数值模拟和预测建模

本文提出了一种混合 IBM/CGM 方案，用于研究低雷诺数剪切流中柔性和不可拉伸细丝的二维轨道状态。该研究包括对一系列长丝长度、弯曲刚度和剪切应变率的机械响应进行参数分析。将结果与理论估计和以前的数值和实验分析进行比较。这些结果随后用于开发使用人工神经网络的预测模型，以有效地预测浸入剪切流中的灯丝的轨道状态。