Utilization of neural networks for simulating vehicle induced air velocity in underground tunnels

Date

2012-12-01

Author

Koç, G.
Albayrak, Kahraman
Sert, Cüneyt

Metadata

Show full item record

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

Item Usage Stats

193
views

0
downloads

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 that have influence on the vehicle induced flow characteristics are used together to obtain non-dimensional input and target parameters. First input parameter is the major head loss coefficient of tunnel, fL/D. Blockage ratio, A Train/ATunnel and train aspect ratio, (L/D) Train are selected to be nondimensional input parameters to represent the system geometry. As the final input parameter, skin friction coefficient of the train, Csf; drag coefficient of the train, CD; frontal area of the train, ATrain and lateral area of the train, ALateral are combined into a single overall drag coefficient based on the train frontal area. Non-dimensional VAir/VTrain speed ratio is selected to be the only target parameter.

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84871634760&origin=inward
https://hdl.handle.net/11511/89097

Collections

Graduate School of Natural and Applied Sciences, Conference / Seminar

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 ...
Prediction of the maximum air velocities created by metro trains using an artificial neural network approach KOC, Gencer; Sert, Cüneyt; Albayrak, Kahraman (2014-09-01) 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 wi...
Experimental Evaluation of Panel-Method-Based Path Planning for eVTOL in A Scaled Urban Environment Ünal, Zeynep; Bronz, Murat; Yavrucuk, İlkay (2022-01-01) In this study, previously proposed panel method based path planning for electric vertical take off and landing vehicles in urban environments is tested in a high fidelity simulation environment and with real-life drones in an indoor flight arena. Panel method is a numerical tool, borrowed form fluid dynamics domain, that can generate collision free paths for multiple vehicles in environments with arbitrarily shaped obstacles while guaranteeing obstacle avoidance and convergence to global minima with little ...
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...
Modeling of ground-borne vibration from underground railway systems Sarıgöl, Melih; Çalışkan, Mehmet; Department of Mechanical Engineering (2007) Ground-borne vibration from underground rail vehicles is studied analytically. A previously developed model by J.A.Forrest and H.E.M.Hunt is modified to account for different track and vehicle models. The tunnel is modeled as infinite cylindrical shell surrounded by viscoelastic soil. The track is coupled to the tunnel with supports of complex stiffness. The rails, which are modeled as infinite Euler beams, are supported by discrete sleepers with regular spacing, and railpads with complex stiffness. A modif...

Citation Formats

G. Koç, K. Albayrak, and C. Sert, “Utilization of neural networks for simulating vehicle induced air velocity in underground tunnels,” 2012, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84871634760&origin=inward.