Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Simulation of Turbulent Flow around a Generic High-Speed Train Using Hybrid Models of RANS Numerical Method with Machine Learning

Version 1 : Received: 26 December 2019 / Approved: 26 December 2019 / Online: 26 December 2019 (05:23:14 CET)

How to cite: Hajipour, A.; Mirabdolah Lavasani, A.; Eftekhari Yazdi, M.; Mosavi, A.; Shamshirband, S.; Chau, K. Simulation of Turbulent Flow around a Generic High-Speed Train Using Hybrid Models of RANS Numerical Method with Machine Learning. Preprints 2019, 2019120351 (doi: 10.20944/preprints201912.0351.v1). Hajipour, A.; Mirabdolah Lavasani, A.; Eftekhari Yazdi, M.; Mosavi, A.; Shamshirband, S.; Chau, K. Simulation of Turbulent Flow around a Generic High-Speed Train Using Hybrid Models of RANS Numerical Method with Machine Learning. Preprints 2019, 2019120351 (doi: 10.20944/preprints201912.0351.v1).

Abstract

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 SST 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.

Subject Areas

machine learning; aerodynamics; high-speed train; hybrid machine learning; Prediction Turbulence model; deep learning

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