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Volume 2017, Article ID 1686230, 14 pages
Research Article

Decomposition-Assisted Computational Technique Based on Surrogate Modeling for Real-Time Simulations

Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 Johor, Malaysia

Correspondence should be addressed to Nariman Fouladinejad; ym.mtu.evil@3namiranf

Received 31 July 2016; Revised 13 January 2017; Accepted 12 February 2017; Published 6 March 2017

Academic Editor: Francisco Gordillo

Copyright © 2017 Nariman Fouladinejad et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The development of complex simulation systems is extremely costly as it requires high computational capability and expensive hardware. As cost is one of the main issues in developing simulation components, achieving real-time simulation is challenging and it often leads to intensive computational burdens. Overcoming the computational burden in a multidisciplinary simulation system that has several subsystems is essential in producing inexpensive real-time simulation. In this paper, a surrogate-based computational framework was proposed to reduce the computational cost in a high-dimensional model while maintaining accurate simulation results. Several well-known metamodeling techniques were used in creating a global surrogate model. Decomposition approaches were also used to simplify the complexities of the system and to guide the surrogate modeling processes. In addition, a case study was provided to validate the proposed approach. A surrogate-based vehicle dynamic model (SBVDM) was developed to reduce computational delay in a real-time driving simulator. The results showed that the developed surrogate-based model was able to significantly reduce the computing costs, unlike the expensive computational model. The response time in surrogate-based simulation was considerably faster than the conventional model. Therefore, the proposed framework can be used in developing low-cost simulation systems while yielding high fidelity and fast computational output.