  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.
