Utilization of neural networks for simulation of vehicle induced flow in tunnel systems

Download

index.pdf

Date

2012

Author

Koç, Gencer

Metadata

Show full item record

Item Usage Stats

195
views

121
downloads

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 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, (L/D)_Tunnel. Blockage ratio A_Train/A_Tunnel and train aspect ratio (D/L)_Train are selected to be non-dimensional input parameters to represent the system geometry. As the final input parameter, skin friction coefficient of the train, f_Train drag coefficient of the train, C_D; frontal area of the train, A_Train and lateral area of the train, A_Lateral are combined into a single overall drag coefficient based on the train frontal area. Non-dimensional V_Air/V_Train speed ratio is selected to be the target parameter. Using maximum air velocity predicted by the trained neural network together with non dimensional system parameters and time, an additional neural network is trained for predicting the deceleration of air in case of train stoppage within the tunnel system and departure of the train from the system. A simulation tool for predicting time dependent velocity profile of air in metro systems is developed with the trained neural networks.

Subject Keywords

Unsteady flow (Aerodynamics)., Unsteady viscous flow., Subway tunnels., Neural networks (Computer science)., Structured neural networks.

URI

http://etd.lib.metu.edu.tr/upload/12614975/index.pdf
https://hdl.handle.net/11511/22042

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

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...
Modelling, simulation and testing of artificial neural network augmented kalman filter for INS/GPS and magnetometer integration Yıldız, Doğan; Konukseven, Erhan İlhan; Nalbantoğlu, Volkan; Department of Mechanical Engineering (2016) The objective of this thesis is to investigate a hybrid Artificial Intelligence/ Kalman Filter (AI/KF) system to determine 3D attitude, velocity and position of a vehicle in challenging GPS environment. In navigation problem, the aim is to determine the position and velocity of the host vehicle from initial conditions. By using Inertial Measurement Unit (IMU), it is possible to calculate position and velocity with an error. In other words, during the integration stage of the IMU measurement, errors will be ...
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...
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...
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...

Citation Formats

G. Koç, “Utilization of neural networks for simulation of vehicle induced flow in tunnel systems,” Ph.D. - Doctoral Program, Middle East Technical University, 2012.