Performance analysis and multi-objective optimization of bionic dendritic furcal energy-absorbing structures for trains

Abstract

Rail vehicles offer the characteristics of large carrying capacity and high running speed. Train accidents usually result in substantial personal casualties and economic losses. Energy-absorbing structures can efficiently and quickly absorb and disperse the energy generated during collisions. In this paper, the performances with different cross-sections and partings of the bionic dendritic furcal structure (BDFS) inspired by the furcal branch and bifurcation structure of neurons are analyzed and compared by numerical simulation. The simplified super folding element theory and experiments verify that the finite element models are effective. A hybrid multi-stage optimization decision system, i.e., multi-criteria decision-making (MCDM)- multi-objective optimization (MOP)- MCDM, which integrates the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, the multiple objective particle swarm optimization-crowding distance (MOPSO-CD) evolutionary algorithm and repetitive VIKOR method, is proposed to find the optimal structure and obtain the optimal parameter alternative of the structure. Comparisons and analyses of experiments and simulations are carried out to verify the effectiveness of the proposed method. The results show that BDFS with six furcal ribs and 3rd-order parting display the best comprehensive crashworthiness under the working condition of 10 m/s specified in EN15227 standard. When the single target requirement is considered, specific energy absorption (SEA) can be increased by 16.18%, peak crushing force (PCF) can be decreased by 67.07%, and undulation of the load-carrying capacity (ULC) can be decreased by 14.02%. This study provides an effective tool for the analysis and optimization of energy-absorbing structures.