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Because of rising complexity during the automotive product development process, the number of disciplines to be concerned has been significantly increased. Multidisciplinary design optimization (MDO) methodology, which provides an opportunity to integrate each discipline and conduct compromise searching process, is investigated and introduced to achieve the best compromise solution for the automotive industry. To make a better application of MDO, the suitable coupling strategy of different disciplines and efficient optimization techniques for automotive design are studied in this article. Firstly, considering the characteristics of automotive load cases which include many shared variables but rare coupling variables, a multilevel MDO coupling strategy based on enhanced collaborative optimization (ECO) is studied to improve the computational efficiency of MDO problems.

Lightweight vehicle design has become an imperative in today's automotive industry. And it is a difficult task, which usually involves non-obvious decisions beyond the designer's intuition. In practice, optimization through finite element simulation is prohibitively inappropriate due to massive computational cost. As a consequence, approximation method is extensively used. In this paper, lightweight design of front side rail through high strength steel is performed. And the advantages of weighted average surrogate (WAS) for approximating the crashworthiness responses in frontal crash are also discussed. It shows the strategy of using WAS is effective, with great potential applications for vehicle crashworthiness approximation and lightweight design.

It has been the core problem in the automotive industry to realize vehicle lightweight, fuel economy and environmental protection. Weight reduction of body structure is playing a rather important role in lightening of a full vehicle. This paper presents the study, from the point of view of safety, of the structural lightweight of the front side rail and radiator support of a new multi-purpose vehicle (MPV). Frontal crash simulation is performed and comparison is shown with original crashworthiness performance of full vehicle, in deformed shape of parts, force response, original absorbed energy of full vehicle and deceleration of a key point in A-pillar. The study verifies the feasibility of this lightweight scheme, which is an exploration in the study on structural lightweight of auto-body and may offer a referential experience for the future work. Achieved weight reductions of the front side rail and radiator support are 9.11% and 22.88%, respectively.

In this paper, a lightweight automotive prototype using alternative materials and gauge thickness was studied using numerical method. NVH performance was the main target to be verified in this study. The frequency response function (FRF) analysis of inner sound pressure was performed by associating FEM (finite element method) with BEM (boundary element method). Detailedly, in order to get the dynamic behavior of the auto-body structure, a frequency response function (FRF) of the body structure was calculated from 1Hz to 150Hz using FEM. Afterwards, the pressure response of the interior acoustic domain was solved by BEM. In order to reveal the structural panels that contributed most significantly to the interior sound pressure, the panel acoustic contribution analysis (PACA) was performed. Finally, the most contributing panel was located. The method used here was timesaving and helpful for the following structure optimization.

Particle swarm optimization (PSO) is a relatively new stochastic optimization algorithm and has gained much attention in recent years because of its fast convergence speed and strong optimization ability. However, PSO suffers from premature convergence problem for quick losing of diversity. That is to say, if no particle discovers a new superiority position than its previous best location, PSO algorithm will fall into stagnation and output local optimum result. In order to improve the diversity of basic PSO, design of experiment technique is used to initialize the particle swarm in consideration of its space-filling property which guarantees covering the design space comprehensively. And the optimization procedure of PSO is divided into two stages, optimization stage and improving stage. In the optimization stage, the basic PSO initialized by Optimal Latin hypercube technique is conducted.