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Complex systems like combat vehicles contain numerous subsystems and components. Therefore, simultaneous consideration of hierarchy of a system, subsystems, and components is necessary for optimization. Multidisciplinary design optimization techniques have been researched to design the complex system. However most of these techniques premise integration process of total system which requires great time and cost. To reduce time and cost for the integration process, we introduce a target cascading technique that optimizes complex hierarchy system with several subproblems of each subsystem and component. Another challenge is to improve the firing accuracy of combat vehicle under various uncertainties. Robust design is therefore necessary to improve the firing accuracy of combat vehicles. To utilize these two concepts in optimization process, statistical information of firing angle is used as linking variables for problem formulation of robust target cascading. Furthermore, analysis of variance, surrogate modeling and statistical approach evaluating firing accuracy are employed to enhance efficiency of optimization. Finally, optimum design of a combat vehicle is achieved by using robust target cascading while improving firing accuracy. [ABSTRACT FROM AUTHOR]

• Developing a complex vehicle model including tire, suspension, and firing system. • Uncertainty definition for significant parameters selected by analysis of variance. • Constructing the surrogate models using an effective adaptive sampling method. • Prediction of uncertainty propagation by using surrogate model-based Monte Carlo simulation. • Obtaining a joint probability of correctness for integrated vehicle performance. The design uncertainties of vehicles cause variation of the vehicle performance. This variation increases with the complexity of the vehicle; e.g., it is greater for heavy-duty vehicles than for passenger cars. This paper presents an efficient uncertainty quantification method based on uncertainty definition, propagation, and certification, with regard to the integrated performance of a heavy-duty vehicle. For the uncertainty definition of the design parameters, an analysis of variance is performed to select the parameters with the greatest effect on the performance, and various probability density functions are employed for these parameters. To predict the precise uncertainty propagation of the vehicle performance and reflect the design uncertainty in the real-world, a full vehicle model is constructed. Additionally, a Monte Carlo simulation (MCS) with surrogate models is performed to assess the efficiency and accuracy of the performance estimation. To efficiently develop the surrogate models, an adaptive-sampling method is used to reduce the required amount of sampling data. For certification of the required performance, the joint probability of correctness for the integrated performance is suggested for practical application, and a comparison of the probability results between the surrogate and dynamic vehicle models indicates the accuracy of MCS with a surrogate model. [ABSTRACT FROM AUTHOR]

Highlights • An integrated interface is developed to focus on concept design via Maple software. • Outlier analysis, screening, kriging model are adopted to the integrated interface. • Symbolic computation is implemented to reduce computational cost. • Design optimization of combat vehicle system is applied to the integrated interface. • Optimization time is reduced by the integrated interface and symbolic computation. Abstract A complex system is composed of several subsystems and numerous lower level components. It is inefficient to design the complex system at the system level by using high-fidelity models. A system level approach using a language-based model is required to design such a highly complex system before implementing a detailed design. However, as commercial process integration and design optimization software packages focus on detailed design, which uses the high-fidelity models, it is difficult to perform the design optimization of a highly complex system, such as a combat vehicle. Moreover, as commercial optimization software packages use numerical computation, the gradient calculation cost can increase, and the matrix computation process can be inefficient in terms of optimization time. Therefore, in this study, an integrated interface for efficient design optimization was developed to focus on concept design using a MODELICA language-based model. Additionally, the design variable screening by using analysis of variance, surrogate modeling through sequential design of experiments, and symbolic computation were used to solve the aforementioned problems. These were applied to the design optimization of the combat vehicle system to demonstrate the effectiveness of the integrated interface and symbolic computation. In conclusion, a concept design utilizing a MODELICA language-based model was achieved, and the optimization time achieved by symbolic computation was largely reduced in comparison to the optimization time achieved by numerical computation. [ABSTRACT FROM AUTHOR]