论文标题
使用机器学习的数值方法的混合模型围绕通用高速列车的湍流模拟
Simulation of Turbulent Flow around a Generic High-Speed Train using Hybrid Models of RANS Numerical Method with Machine Learning
论文作者
论文摘要
在本文中，对高速列车进行了空气动力学研究。在本文的第一部分中，通过数值模拟了针对湍流的通用高速列车。使用雷诺平均的Navier-Stokes（RANS）方程与湍流模型相结合，用于求解高速列车周围不可压缩的湍流。在某些典型的风向上，流动结构，速度和压力轮廓和流线是该模拟的最重要结果。指定和讨论最大值和最小值。同样，评估了火车表面上某些临界点的压力系数。在下文中，将风向影响空气动力学关键参数，因为在上述风向上的阻力，升力和侧力会影响并进行比较。此外，估计速度变化（50、60、70、80和90 m/s）的影响，并在上述流动参数和空气动力学参数上进行比较。在本文的第二部分中，应用了各种数据驱动的方法，包括基因表达编程（GEP），高斯过程回归（GPR）和随机森林（RF），用于预测输出参数。因此，预测并使用统计参数对上述风向和速度的最小和最小压力系数以及最大压力系数进行了比较。获得的结果表明，在风向的所有系数和自由流速度的大多数系数上，RF提供了最准确的预测。最后，可能建议将RF用于预测空气动力学系数。
In the present paper, an aerodynamic investigation of a high-speed train is performed. In the first section of this article, a generic high-speed train against a turbulent flow is simulated, numerically. The Reynolds-Averaged Navier-Stokes (RANS) equations combined with the turbulence model are applied to solve incompressible turbulent flow around a high-speed train. Flow structure, velocity and pressure contours and streamlines at some typical wind directions are the most important results of this simulation. The maximum and minimum values are specified and discussed. Also, the pressure coefficient for some critical points on the train surface is evaluated. In the following, the wind direction influence the aerodynamic key parameters as drag, lift, and side forces at the mentioned wind directions are analyzed and compared. Moreover, the effects of velocity changes (50, 60, 70, 80 and 90 m/s) are estimated and compared on the above flow and aerodynamic parameters. In the second section of the paper, various data-driven methods including Gene Expression Programming (GEP), Gaussian Process Regression (GPR), and random forest (RF), are applied for predicting output parameters. So, drag, lift, and side forces and also minimum and a maximum of pressure coefficients for mentioned wind directions and velocity are predicted and compared using statistical parameters. Obtained results indicated that RF in all coefficients of wind direction and most coefficients of free stream velocity provided the most accurate predictions. As a conclusion, RF may be recommended for the prediction of aerodynamic coefficients.
