Railway Air Brake Model and Parallel Computing Scheme

Abstract

This paper developed a detailed fluid dynamics model and a parallel computing scheme for air brake systems on long freight trains. The model consists of subsystem models for pipes, locomotive brake valves, and wagon brake valves. A new efficient hose connection boundary condition that considers pressure loss across the connection was developed. Simulations with 150 sets of wagon brake systems were conducted and validated against experimental data; the simulated results and measured results reached an agreement with the maximum difference of 15%; all important air brake system features were well simulated. Computing time was compared for simulations with and without parallel computing. The computing time for the conventional sequential computing scheme was about 6.7 times slower than real-time. Parallel computing using four computing cores decreased the computing time by 70%. Real-time simulations were achieved by parallel computing using eight computer cores.

Train braking simulation with wheel-rail adhesion model

“…A number of different railway air brake models have been reported in the literature [18][19][20][21][22][23][24][25]. The air brake model used in this paper is an empirical model based on measured data as shown in Figure 1.…”

Parallel computing of wheel-rail contact

“…The parallel computing was enabled using the OpenMP 32 technique. Other options such as Message Passing Interface 34 , 35 may also be used. The main changes to the original code are: (1) creating identification vectors for all force couplings in the model and marking the parallelised couplings as a certain number; (2) creating address vectors to store the addresses for all parallelised force couplings; and (3) using the OpenMP Do function to execute the computing of parallelised force couplings in parallel.…”

Parallel computing in railway research

“…More information regarding MPI can be accessed via the online book of Barney [34]. In railway research, the MPI has found many applications [7,[11][12][13][21][22][23]31,[35][36][37][38][39][40][41][42][43]; it is one of the most widely used parallel computing enabling techniques. In the authors' opinion, MPI is very suitable for parallel co-simulations and parallel optimisations, as presented in References [31] and [42] respectively, since they have very independent parallelised computing tasks.…”

“…In addition to the FEA related studies, Hoang et al [19,24] have used parallel DEA to simulate railway ballast. Parallel CFD has been used to analyse vehicle response subject to cross-wind [18], the entry flow issue of railway tunnels [27], catenary aerodynamics [78] and air brake modelling [41].…”