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

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

152
views

0
downloads

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

Suggestions

OpenMETU
Core

Utilization of neural networks for simulation of vehicle induced flow in tunnel systems Koç, Gencer; Albayrak, Kahraman; Sert, Cüneyt; Department of Mechanical Engineering (2012) Air velocities induced by underground vehicles in complex metro systems are obtained using artificial neural networks. Complex tunnel shaft-systems with any number of tunnels and shafts and with most of the practically possible geometries encountered in underground structures can be simulated with the proposed method. A single neural network, of type feed-forward back propagation, with a single hidden layer is trained for modelling a single tunnel segment. Train and tunnel parameters that have influence on ...
Use of artificial neural networks for the prediction of time-dependent air speed variation in metro stations KOC, Gencer; Sert, Cüneyt; Albayrak, Kahraman (2018-09-01) In this study, the time-dependent, induced air speeds at critical sections of underground metro stations are assessed using a novel one-dimensional data-driven approach. For this purpose, three artificial neural networks are used, each trained for the most basic configuration of a single train moving in a single tunnel. The first two are trained to provide the maximum and time averaged values of the induced air speeds while the train is moving inside the approach tunnel or the station. The third one is used...
Analysis of weakly ionized hypersonic flow Pişkin, Tuğba; Eyi, Sinan; Department of Aerospace Engineering (2015) The knowledge of physical phenomena and numerical computation skills are required to simulate flow field around the re-entry capsule due to extreme conditions resulted by high speed and high energy of capsule. The re-entries of spacecraft occur generally at high hypersonic speed where reactions take place actively, and where plasma field around geometry as well as radiation from the surface affect characteristics of the flow field. In these conditions, ideal gas assumption fails; thus, real gas assumption i...
Mathematical Modeling of the NOTAR Antitorque System for Flight Simulation Yavrucuk, İlkay; Uzol, Oğuz (2013-04-01) In this paper, a mathematical model of a helicopter NO TAil Rotor (NOTAR) antitorque system is developed for real-time flight simulations. The model consists of the circulation control tail boom, direct jet thruster, and the vertical stabilizers. The airflow inside the tail boom is modeled by dividing the flow into aerodynamic control volumes. The model features a bladeelement-type approach for modeling the mass flow through the axial fan blades as well as aerodynamic mass and momentum conservation calculat...
Utilization of neural networks for simulating vehicle induced air velocity in underground tunnels Koç, G.; Albayrak, Kahraman; Sert, Cüneyt (2012-12-01) Air velocities induced by underground vehicles in metro tunnels equipped with ventilation shafts are obtained using artificial neural networks. Complex tunnel shaft-systems with any number of tunnels and shafts and with most of the practically possible geometries encountered in underground structures can be simulated with the proposed method. A single neural network, of type feed-forward back propagation, with a single hidden layer is trained for modeling a single tunnel segment. Train and tunnel parameters...

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.