Fan Zijie

Multi-Objective Collaborative Optimization Based on Evolutionary Algorithms

This paper proposes a novel multi-objective collaborative optimization (MOCO) approach based on multi-objective evolutionary algorithms for complex systems with multiple disciplines and objectives, especially for those systems in which most of the disciplinary variables are shared. The shared variables will conflict when the disciplinary optimizers are implemented concurrently. In order to avoid the confliction, the shared variables are treated as fixed parameters at the discipline level in most of the MOCO approaches. But in this paper, a coordinator is introduced to handle the confliction, which allocates more design freedom and independence to the disciplinary optimizers. A numerical example is solved, and the results are discussed.

Experimental studies on the axial crash behavior of aluminum foam-filled hat sections

Drop hammer tests were carried out to study the axial crash behavior of aluminum foam-filled hat sections. First, the axial crash tests of the empty hat sections, aluminum foam and the aluminum foam-filled hat sections were carried out; then, based upon the test results, the axial crash behavior of the aluminum foam-filled hat sections were analyzed. It was found that aluminum foam filling can increase the energy absorption capacities of the hat sections. Compared with the non-filled structures, aluminum foam-filled structures were much more stable and needed less mass to absorb the specified energy.