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


Koç G., ALBAYRAK K., SERT C.

6th European Congress on Computational Methods in Applied Sciences and Engineering, ECCOMAS 2012, Vienna, Austria, 10 - 14 September 2012, pp.3405-3416 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • City: Vienna
  • Country: Austria
  • Page Numbers: pp.3405-3416

Abstract

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.