Prediction of the maximum air velocities created by metro trains using an artificial neural network approach

Date

2014-09-01

Author

KOC, Gencer
Sert, Cüneyt
Albayrak, Kahraman

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The maximum air velocity created by a moving train inside a tunnel is obtained using an artificial neural network approach. A neural network model is developed to represent a single train travelling in a single tunnel. A set of non-dimensional groups, which are known to influence the induced flow characteristics, is used for the training of the neural network. Various test runs are compared with the results of the authoritative software, Subway Environmental Simulation. The presence of ventilation shafts within a tunnel is included in the model by defining an aerodynamically equivalent single tunnel using major head loss characteristics of different parts of the system. This approach eliminated the requirement to train the neural network for a large number of possible tunnel/shaft configurations.

Subject Keywords

Piston effect, Vehicle-induced flow, Artificial neural network, Underground transportation

URI

https://hdl.handle.net/11511/32231

Journal

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART F-JOURNAL OF RAIL AND RAPID TRANSIT

DOI

https://doi.org/10.1177/0954409713488100

Collections

Graduate School of Natural and Applied Sciences, Article

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Citation Formats

G. KOC, C. Sert, and K. Albayrak, “Prediction of the maximum air velocities created by metro trains using an artificial neural network approach,” PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART F-JOURNAL OF RAIL AND RAPID TRANSIT, pp. 759–767, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/32231.