Document Type : Research Paper

Authors

1 M. Sc. Student of Mechanical Engineering, faculty of engineering, University of Tehran, Tehran, Iran

2 Department of Mechanical Engineering, faculty of engineering, University of Tehran, Tehran, Iran

3 Department of MAPNA Locomotive Research and Development, Karaj, Iran

Abstract

In today's design, system complexity and increasing demand for safer, more efficient and less costly systems have created new challenges in science and engineering. Locomotives are products which are designed according to market order and technical needs of customers. Accordingly, targets of companies, especially designers and manufacturers of locomotives, have always been on the path of progress and seek to offer products with higher technology than other competitors. Quality of body structures is based on indicators such as natural frequency, displacement, fatigue life and maximum stress. Natural frequency of various components of the system and their adaption to each other are important for avoiding the phenomenon of resonance. In this study, body structures of ER24 locomotive (Iran Safir Locomotive) was studied. A combination of imperialist competitive algorithm (ICA) and artificial neural network was proposed to find optimal weight of structures while natural frequencies were in the determined range. Optimization of locomotive's structure was performed with an emphasis on maintaining locomotive abilities in static and dynamic fields. The results indicated that use of optimization techniques in the design process was a powerful and effective tool for identifying and improving main dynamic characteristics of structures and also optimizing performance in stress, noise and vibration fields.

Keywords

Main Subjects

[1] A. Stribersky, S. Steidl, H. Müller and B. Rath, “On dynamic analyses of rail vehicles with electronically controlled suspensions”, Proc. of 14th IAVSD Int. Symposium, Ann Arbor, Usa, pp. 614-628, (1996).
[2] A. Stribersky, W. Rulka, H. Netter and A. Haigermoser, “Modeling and simulation of advanced rail vehicles”. Proc. of  8th IFAC/IFIP/IFORS Int. Symposium “Transportation Systems”, Chania, Greece, pp.476-481, (1997).
[3] A. Keymasi and M. Partovi, Static and Dynamic Analysis of G-16 Locomotive and study of possibility to optimize, Msc thesis, Iran University of Scince & Technology, Tehran, Iran, (2006).
[4] A. Subic and J. He, “Improving bus rollover design through modal analysis”, Intl  J. Crashworthiness, Vol. 2, No. 2, pp. 139-152, (1997).
[5] J. He and Z. F. Fu, Modal Analysis, first ed., Butterworth - Heinemann Publication, Oxford, pp.103-127, (2001).
[6] Nastran Advanced Dynamic Analysis User's Guide, MacNeal-Schwendler, Los Angeles, pp. 83-95, (2010).
[7] P. D. Wasserman, Advanced methods in neural computing, Van Nostrand Reinhold, New York, pp. 56-84, (1993).
[8] P. D. Wasserman, Neural computing theory and practice, Van Nostrand Reinhold, New York, pp. 78-96, (1989).
[9] M. Shahidi poor, “Optimization (Theory and Application)”, S. S. Rao, New age international, pp. 323–355, (1996).
[10] E. Atashpaz–Gargari and C. Lucas, “Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition”, “IEEE Congress on Evolutionary Computation”, Singapore, 4661-4667, (2007).
[11] C. Lucas, Z. Nasiri–Gheidari and F. Tootoonchian, “Application of an imperialist competitive algorithm to the design of a linear induction motor”, journal of Energy Convers Manage, Vol. 51, No. 7, pp. 1407-1411, (2010).
[12] M. Ghamami, M. S. Panahi, M. Rezaei and M. Mondali, “Modal analysis of ER24 locomotive and its Optimization by using genetic algorithms”, Proc. of 3th ICRARE Int. Seminar, Iran University of Scince & Technology, Tehran, Iran, (2013).
CAPTCHA Image