Wedge suspensions are critical systems for three-piece bogies. This paper proposes a methodology to optimize wedge suspensions using white-box suspension models, dynamic simulations of railway vehicle systems, parallel multi-objective Particle Swarm Optimization (pMOPSO), and parallel multi-objective Genetic Algorithm (pMOGA). Two types of original wedge suspensions with three different toe angle configurations were modeled and compared. Four case studies were carried out to prove the feasibility of the optimization methodology. A series of optimized designs were identified using the Pareto Front technique. Demonstrative optimized designs were compared with the original designs. Results show that wedge suspensions with the toe-in configuration provide better dynamic performance for freight wagons. Significant reductions to the maximum wheel/rail contact forces can be achieved by the optimized designs. Linear speed-up was achieved by using the parallel computing technique.
Ahmadian M and White DL (1999) An experimental evaluation of the effect of rail vehicles truck suspensions on wheel-rail forces. In: Proceedings of the 1999 ASME/IEEE Joint Railroad Conference, Dallas, TX, pp. 144-150.
2.
ARTC (2014) Track geometry section 5 by Australian Rail Track Corporation Ltd.
3.
BallewBChanBJSanduC (2011) Multibody Dynamics modelling of the freight train bogie system. Vehicle System Dynamics49: 501–526.
4.
BullockRLCooleyDB (1978) Optimizing railroad freight car truck suspension systems having Coulomb damping. ASME Journal of Engineering for Industry100: 311–316.
5.
Cole C, Simson S, McClanachan M and Skerman D (2006) Evaluating the Performance of 3-piece Bogies on Short Defects and with Unequal Wheel Diameters. In: Conference on Railway Engineering, Melbourne, Australia, pp. 49-60.
6.
CQUHPC (2015) Central Queensland University, High Performance Computing. Available at: https://www.cqu.edu.au/hpc accessed 11 November 2015.
7.
EngelbrechtAP (2007) Computational Intelligence: An Introduction, 2nd edition. West Sussex, UK: John Wiley & Sons, Ltd.
8.
Gardner JF and Cusumano JP (1997) Dynamic models of friction wedge dampers. Proceedings of the 1997 IEEE/ASME Joint Rail Conference; 1997 March 18-20; Boston, MA: American Society of Mechanical Engineering; pp.65-69.
9.
HidalgoAde JalonJ (2015) Real-Time Dynamic Simulations of Large Road Vehicles Using Dense, Sparse, and Parallelization Techniques. ASME Journal of Computational and Nonlinear Dynamics10: 031005.
10.
IwnickiSDStichelSOrlovaAHechtM (2015) Dynamics of railway freight vehicles. Vehicle System Dynamics53: 995–1033.
11.
KimIde WeckO (2006) Adaptive Weighted Sum Method for Multiobjective Optimization. Structural and Multidisciplinary Optimization31: 105–116.
NegrutDSerbanRMazharHHeynT (2014) Parallel Computing in Multibody System Dynamics: Why, When and How. ASME Journal of Computational and Nonlinear Dynamics9: 041007.
14.
OrlovaABoronenkoY (2010) The influence of the condition of three-piece freight bogies on wheel flange wear: simulation and operation monitoring. Vehicle System Dynamics48: 37–53.
15.
RISSB (2009) Railway Rolling Stock- Dynamic Behaviour – Part 2: Freight Rolling Stock (AS 7509.2). Standards Australia & Rail Industry Safety & Standards Board.
16.
SpiryaginMColeCSunYQMcClanachanMSpiryaginVMcSweeneyT (2014) Design and simulation of rail vehicles, Boca Raton, FL: CRC Press.
17.
Spiryagin M, Wu Q and Cole C (2015) Wagon dynamics form a combination of faults and external forces. Paper presented at: 11th International Heavy Haul Association Conference, 21-24 June 2015, Perth, Australia.
18.
SteetsJChanBJSanduC (2010a) Multibody Dynamics approach to the modeling of friction wedge elements for freight train suspensions. I: Theory. Journal of Transportation Engineering136: 709–716.
19.
SteetsJChanBJSanduC (2010b) Multibody Dynamics of approach to the modeling of friction wedge elements for freight train suspensions. II: Applications. Journal of Transportation Engineering136: 717–726.
20.
SunYQColeCBoydP (2010) A numerical method using VAMPIRE modelling for prediction of turnout curve wheel-rail wear. Wear271: 482–491.
21.
SunYQColeC (2007) Comprehensive wagon-track modelling for simulation of three-piece bogie suspension dynamics. Proc Inst Mech Eng F Journal of Rail and Rapid Transit221: 905–917.
22.
Sun YQ and Cole C (2006) The effect of wedge friction conditions on the dynamic wheel-rail contact force on short wave length defects. Proceedings of Joint Rail Conference; 2006 April 4-6; Atlanta, GA: American Society of Mechanical Engineering; pp. 37–43.
23.
SunYQSimsonS (2007) Nonlinear three-dimensional wagon-track model for the investigation of rail corrugation initiation on curved track. Vehicle System Dynamics45: 113–132.
24.
Taillon AP (2001) Friction wedge design optimized for high warp friction moment and low damping force. US Patent No. US 6,269,752 B1. Date of Patent Aug. 7, 2001.
25.
Voss T, Beume N, Rudolph G and Igel C (2008) Scalarization versus Indicator-based Selection in Multi-Objective CMA Evolution Strategies. Proc. of the 2008 IEEE Congress on Evolutionary Computation (CEC 2008), pp. 3041–3048. IEEE Press: Piscataway, NJ, 2008.
26.
WuQColeCSpiryaginMSunYQ (2014) A review of dynamics modelling of friction wedge suspensions. Vehicle System Dynamics52: 1389–1415.
27.
WuQLuoSHLiCS (2012) Research of freight wagon shunting impact influenced by draft gear key parameters. Diesel Locomotives2: 5–8.
28.
WuQColeC (2015) Computing Schemes for Longitudinal Train Dynamics: Sequential, Parallel and Hybrid. ASME Journal of Computational and Nonlinear Dynamics10: 064502.
29.
WuQColeCSpiryaginM (2016) Parallel computing enables whole-trip train dynamics optimizations. ASME Journal of Computational and Nonlinear Dynamics11: 044503.
30.
YanZQMarkineVGuAJLiangQH (2014) Optimization of the dynamic properties of the ladder track system to control rail vibration using the multipoint approximation method. Journal of Vibration and Control20: 1967–1984.
