A Comparison of the Numerical Simulation and Experimental Investigation of the Flow Around a High Speed Train

Paradot, Nicolas; Talotte, Corinne; Garem, H.; Delville, J.; Bonnet, Jean-Paul

doi:10.1115/fedsm2002-31430

Cited by 5 publications

(3 citation statements)

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“…Reynolds-Averaged Navier-Stokes (RANS) has been one of the most popular computational techniques used by researchers in recent times. It has been used very frequently to study the aerodynamics of a high-speed train in an open environment and has been proven as an accurate method [22,23]. There have been a few studies on the motion of trains through constrained spaces using RANS.…”

Section: Studies On Airflow Around High-speed Systemsmentioning

confidence: 99%

Mitigating the Piston Effect in High-Speed Hyperloop Transportation: A Study on the Use of Aerofoils

Bose

Viswanathan

2021

Energies

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The Hyperloop is a concept for the high-speed ground transportation of passengers traveling in pods at transonic speeds in a partially evacuated tube. It consists of a low-pressure tube with capsules traveling at both low and high speeds throughout the length of the tube. When a high-speed system travels through a low-pressure tube with a constrained diameter such as in the case of the Hyperloop, it becomes an aerodynamically challenging problem. Airflow tends to get choked at the constrained areas around the pod, creating a high-pressure region at the front of the pod, a phenomenon referred to as the “piston effect.” Papers exploring potential solutions for the piston effect are scarce. In this study, using the Reynolds-Average Navier–Stokes (RANS) technique for three-dimensional computational analysis, the aerodynamic performance of a Hyperloop pod inside a vacuum tube is studied. Further, aerofoil-shaped fins are added to the aeroshell as a potential way to mitigate the piston effect. The results show that the addition of fins helps in reducing the drag and eddy currents while providing a positive lift to the pod. Further, these fins are found to be effective in reducing the pressure build-up at the front of the pod.

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Section: Studies On Airflow Around High-speed Systemsmentioning

confidence: 99%

Mitigating the Piston Effect in High-Speed Hyperloop Transportation: A Study on the Use of Aerofoils

Bose

Viswanathan

2021

Energies

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“…In 2002, an experimental and numerical investigation of air flow around a high-speed train was done by Paradot et al [3]. In this research, a high-speed train at French railways among a turbulent air flow was simulated, numerically using the commercial code Star CD.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigation of Wall Function Effects on Aerodynamic Characteristics of Turbulent Flow Around a Simplified High-Speed Train

Hajipour¹,

Lavasani²,

Yazdi³

2021

IJHT

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Modelling of turbulence is a vital issue for flow forecasting which is of great interest for most of engineering applications like flow over planes, movement of pollutants and some industrial processes. Originally, via solving the government equations (Navier-Stokes equations) the flow field can be simulated. With developing PCs and high-performance computers, implementing of Navier-Stokes equations for numerical simulation is increasing. In this research, the effects of some wall functions on aerodynamic and turbulence behavior of air flow around a simplified high-speed train via OpenFOAM software are numerically investigated. In the following, first, the effects of some default and common wall functions of OpenFOAM on the flow and aerodynamic key parameters are analyzed and then, a relatively new wall function called “Enhanced Wall Function” was implemented from ANSYS FLUENT into OpenFOAM and improvement for comprehensive simulation. Variations of flow key parameters such as velocity, pressure distribution and aerodynamic significant components and parameters such as lift, drag and side coefficients under the influence of wall functions changes are illustrated. The results could be used for obtaining more accurate analysis of aerodynamic characteristics of fluid flow around high-speed trains.

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“…A comparison between numerical and experimental simulation of air flow over a high-speed train was investigated (Paradot, 2002). A SNCF (French high-speed train) with high Reynolds number (almost 10 9 ) was used for aerodynamic analysis.…”

Section: Introductionmentioning

confidence: 99%

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

Hajipour¹,

Lavasani²,

Yazdi³

et al. 2019

Preprint

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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 of θ = 0˚, 30˚, 45˚ and 60, 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

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A Comparison of the Numerical Simulation and Experimental Investigation of the Flow Around a High Speed Train

Cited by 5 publications

References 0 publications

Mitigating the Piston Effect in High-Speed Hyperloop Transportation: A Study on the Use of Aerofoils

Mitigating the Piston Effect in High-Speed Hyperloop Transportation: A Study on the Use of Aerofoils

Investigation of Wall Function Effects on Aerodynamic Characteristics of Turbulent Flow Around a Simplified High-Speed Train

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

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