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A platform for research: civil engineering, architecture and urbanism
Multidisciplinary Design Optimization of Driving Axle Housing Using Sparse Grid Approach
In order to improve the engineering performance of lightweight design on the driving axle housing, lightweight, structural mechanics, fatigue strength and dynamics are applied in the multidisciplinary design optimization. Firstly, finite element model of driving axle housing was established and its accuracy was verified through bench tests. Secondly, driving axle housing system was divided into multiple sub-discipline systems and design variables of multidisciplinary lightweight design were determined, in order to solve the problems of large amount of data transmission and complex calculation, sparse grid approach was used to establish high accuracy approximate model of each discipline. Lastly, mass of driving axle housing and difference values of first six order modal frequencies before and after lightweight design were optimized through Non-dominated Sorted Genetic Algorithm-II, the Pareto optimal solution set was obtained. In optimization results, masses of driving axle housing are all decreased compared to the initial design, meanwhile, the dynamic performance, structural static intensity and fatigue life are all ensured.
Multidisciplinary Design Optimization of Driving Axle Housing Using Sparse Grid Approach
In order to improve the engineering performance of lightweight design on the driving axle housing, lightweight, structural mechanics, fatigue strength and dynamics are applied in the multidisciplinary design optimization. Firstly, finite element model of driving axle housing was established and its accuracy was verified through bench tests. Secondly, driving axle housing system was divided into multiple sub-discipline systems and design variables of multidisciplinary lightweight design were determined, in order to solve the problems of large amount of data transmission and complex calculation, sparse grid approach was used to establish high accuracy approximate model of each discipline. Lastly, mass of driving axle housing and difference values of first six order modal frequencies before and after lightweight design were optimized through Non-dominated Sorted Genetic Algorithm-II, the Pareto optimal solution set was obtained. In optimization results, masses of driving axle housing are all decreased compared to the initial design, meanwhile, the dynamic performance, structural static intensity and fatigue life are all ensured.
Multidisciplinary Design Optimization of Driving Axle Housing Using Sparse Grid Approach
Ma, Shi-Lei (author) / Li, Fang-Yi (author) / He, Yang (author) / Xu, Qing-Zhong (author)
2012
5 Seiten
Conference paper
English
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