Simulation of Turbulent Flow around a Generic HighSpeed Train using Hybrid Models of RANS Numerical Method with Machine Learning
Abstract
In the present paper, an aerodynamic investigation of a highspeed train is performed. In the first section of this article, a generic highspeed train against a turbulent flow is simulated, numerically. The ReynoldsAveraged NavierStokes (RANS) equations combined with the turbulence model are applied to solve incompressible turbulent flow around a highspeed 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 datadriven 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.
 Publication:

arXiv eprints
 Pub Date:
 December 2019
 arXiv:
 arXiv:2001.01569
 Bibcode:
 2020arXiv200101569H
 Keywords:

 Physics  Fluid Dynamics;
 Computer Science  Machine Learning;
 Statistics  Machine Learning
 EPrint:
 43 pages, 25 figures, 9 tables