标题： 涡流引起的阻力预测对于非均匀来流中的圆柱体 显示英文标题

标题： Vortex-Induced Drag Forecast for Cylinder in Non-uniform Inflow

摘要： 在本信中，开发了一种基于物理的数据驱动策略，用于预测非均匀流入条件下圆柱体上的涡激阻力 - 这是在中等雷诺数工程应用中的常见问题。传统基于压力信号的模型由于复杂的涡旋动力学与非均匀流入的耦合而存在局限性。为了解决这个问题，建立了一种改进的全连接神经网络（FCNN）架构，该架构将上游速度测量值（作为流入校准）与基于压力信号的输入相结合，以提高预测能力（R^2 ~ 0 到 0.75）。在雷诺数 Re = 4000 下进行了直接数值模拟（DNS）以进行模型训练和验证。进行了迭代优化，以得出压力传感器布置和上游位置速度分量的优化输入配置。优化后的模型在预测一个时间单位未来时间窗口内的高振幅阻力系数波动（C_d=0.2 - 1.2）时，获得了 R^2 得分为 0.75。观察到模型性能与优化后的压力信号输入之间存在指数关系，并通过这种比例关系解释了稀疏分布但优化的传感器的预测能力。优化的传感器布置对应于流动分离动力学在涡激阻力生成中起主导作用的物理机制。这项工作推进了机器学习在流体-结构相互作用系统中的应用，为在现实工程条件下的湍流统计预测提供了一种可扩展的策略。 显示英文摘要

摘要： In this letter, a physics-based data-driven strategy is developed to predict vortex-induced drag on a circular cylinder under non-uniform inflow conditions - a prevalent issue for engineering applications at moderate Reynolds numbers. Traditional pressure-signal-based models exhibit limitations due to complex vortex dynamics coupled with non-uniform inflow. To address this issue, a modified fully connected neural network (FCNN) architecture is established that integrates upstream velocity measurements (serving as an inflow calibration) with pressure-signal-based inputs to enhance predictive capability (R^2 ~ 0 to 0.75). Direct numerical simulations (DNS) at Reynolds number Re = 4000 are implemented for model training and validation. Iterative optimizations are conducted to derive optimized input configurations of pressure sensor placements and velocity components at upstream locations. The optimized model achieves an R^2 score of 0.75 in forecasting high-amplitude drag coefficient fluctuations (C_d=0.2 - 1.2) within a future time window of one time unit. An exponential scaling between model performance and optimized pressure signal inputs is observed, and the predictive capability of sparsely distributed but optimized sensors is interpreted by the scaling. The optimized sensor placements correspond to the physical mechanism that the flow separation dynamics play a governing role in vortex-induced drag generation. This work advances machine learning applications in fluid-structure interaction systems, offering a scalable strategy for forecasting statistics in turbulent flows under real-world engineering conditions.