论文标题
使用神经网络的任意纵横比和攻击角度的椭圆圆柱体上的升力和阻力系数的敏感性分析
Sensitivity Analysis of Lift and Drag Coefficients for Flow over Elliptical Cylinders of Arbitrary Aspect Ratio and Angle of Attack using Neural Network
论文作者
论文摘要
虚张声势的流量具有多种工程应用，因此已经研究了数十年。在许多组件（例如汽车，飞机，建筑物）的设计中，升降和阻力系数实际上很重要。这些系数随着雷诺的数量和虚张声势的几何参数而差异很大。在这项研究中，我们分析了单个和串联椭圆圆柱体对圆柱纵横比的升力和阻力系数的敏感性，攻击角度，气缸分离和流动雷诺数的敏感性。使用蒙特 - 卡洛算法进行灵敏度分析需要多个功能评估，这与高保真计算模拟是不可行的。因此，我们使用计算流体动力学数据训练了多层感知神经网络（MLPNN），以有效地估算升力和阻力系数。还介绍了升力和阻力变化的线图作为管理参数的函数。目前的方法适用于研究其他各种虚张声势的身体配置。
Flow over bluff bodies has multiple engineering applications and thus, has been studied for decades. The lift and drag coefficients are practically important in the design of many components such as automobiles, aircrafts, buildings etc. These coefficients vary significantly with Reynolds number and geometric parameters of the bluff body. In this study, we have analyzed the sensitivity of lift and drag coefficients on single and tandem elliptic cylinders to cylinder aspect ratios, angles of attack, cylinder separation, and flow Reynolds number. Sensitivity analysis with Monte-Carlo algorithm requires several function evaluations, which is infeasible with high-fidelity computational simulations. We have therefore trained multilayer perceptron neural networks (MLPNN) using computational fluid dynamics data to estimate the lift and drag coefficients efficiently. Line plots of the variations in lift and drag as functions of the governing parameters are also presented. The present approach is applicable to study of various other bluff body configurations.
