journal article·2021-08

In-plane dynamic response and multi-objective optimization of negative Poisson's ratio (NPR) honeycomb structures with sinusoidal curve

Fengxiang Xu, Kejiong Yu, Lin Hua

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Abstract

Abstract In order to efficiently evaluate and design honeycomb structures with excellent energy-absorbing ability, in-plane dynamic response and multi-objective optimization of negative Poisson's ratio (NPR) honeycomb structures with sinusoidal curve are performed in this work. By establishing the in-plane impact finite element model , the deformation modes and in-plane dynamic responses of the sinusoidal NPR honeycomb structures under different impact velocities are detailed studied. And the geometric parameters (e.g., the amplitude and period) of the sinusoidal curve that affect the energy absorption performance of the structure are analyzed. Combining the optimal latin hypercube design method with the double-points infilling criterion, the high-precision adaptive Kriging models of the peak crush force (PCF) and the specific energy absorption (SEA) with respect to the amplitude and period are constructed. And then, a multi-objective crashworthiness optimization is carried out by non-dominated sorting genetic algorithm (NSGA-II). The Pareto optimal solution set is gained and the final compromise solution is then obtained based on the fitness function. The optimized results show the PCF of the sinusoidal NPR honeycomb structure decreases by 30.53%, while the SEA increases by 38.55%. It indicates that the optimized sinusoidal honeycomb structure has better energy absorption performance than the conventional concave hexagonal honeycomb. The results can provide some important design guidance for other honeycomb energy-absorbing structures.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Cellular and Composite Structures · Automotive and Human Injury Biomechanics · Mechanical Engineering and Vibrations Research

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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