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Efficient method to solve large-scale eigenvalue problem in vibration is presented. NVH (Noise, Vibration and Harshness) performance is an important quality measure of vehicles. Therefore, the reduction of vibration is one of the key considerations of new automatic transmission design. In addition, reduction of product design time is another important requirement. Computer Aided Engineering (CAE) is becoming a more important methodology to reduce product design time. However, computational time of eigenvalue problem takes long. We propose parallel eigenvalue computation (Sakurai-Sugiura method) for large-scale eigenvalue problems. This method has a good parallel scalability according to a hierarchical structure of the method. As the demonstrative problem, we consider large-scale computation of eigenvalue problem for AISIN AW FWD automatic transmission.

This study implemented multi-objective design exploration for an automatic transmission casing using genetic algorithm (GA) and data mining techniques. In general, real-world design problems are requested to be solved by considering all relevant disciplines simultaneously, which is called multidisciplinary design optimization (MDO). We often face the difficulty in balancing these different disciplines to satisfy required performance, such as durability, stiffness, noise, vibration, harshness, mountability, weight, and manufacturability considered in this study. In addition, we often have to improve development efficiency for shortening of the development period. Therefore, MDO has been widely implemented for taking improved design candidates according to required specifications and making a decision to fix conceptual design in an early stage. MDO is usually considered as the multi-objective optimization which aims to obtain the set of Pareto-optimal solutions.