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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.

Driving simulators are practical simulation tools in studying vehicle behavior and driver reaction in a safe and controllable condition. The development of a real time driving simulator evolves into complex highly integrated and interdependent systems that require vast amount of computer memory and computational time. This paper provides a study of employing approximation techniques in optimizing the computationally expensive simulation systems. Using the approximation techniques, a surrogate model can be constructed and used in the lieu of original codes. It can obviate the computational cost of highly integrated systems. A variety of approximation techniques can be used to simplify multidisciplinary simulations. In this paper, some well-known approximation techniques were reviewed including design of experiments, polynomial response surfaces, Kriging models and neural networks. A thorough review and study of various types of approximation techniques were made to construct efficient surrogate models for simulation subsystems. A surrogate assisted driving simulator (SADS) framework is then proposed that can significantly reduce the computational burden and achieve reasonable accuracy.

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