journal article·2026-05-14

Tunable deformation behavior and penetration performance of the adjustable-similarity-ratio nested folded honeycomb sandwich panels under low-velocity impact

Shouji Zhao, Wu Yan, Xu Zhang, Di Zhang, Zhenqing Wang

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Abstract

Honeycomb sandwich panels are widely used in aerospace, automotive, and marine applications. To meet the demands of complex loading conditions and specialized application scenarios requiring flexibly tunable advanced structures, an adjustable-similarity-ratio nested folded honeycomb core was developed by integrating the adjustable similarity ratio nested hierarchical and folding design strategies. Numerical simulations were conducted to investigate the low-velocity impact response of sandwich panels with cores of varying geometric parameters under different impact velocities. The local crash efficiency (LCE) and energy balance models were employed to evaluate deformation resistance, while specific penetration energy was analyzed. The new structure can ensure that the external dimensions remain unchanged, and the mechanical properties can be altered by changing the nesting similarity ratio. Analysis demonstrates that the nested folded honeycomb sandwich panels can achieve 25.5% and 85.6% enhancements in resistance to local deformation and global stiffness, respectively, compared to traditional honeycomb sandwich panels. At high impact velocity, structures with vertical folding and high nesting similarity ratios demonstrated the maximum specific penetration energy, being 1.076 times that of nested honeycomb and 1.248 times that of traditional honeycomb structures, respectively. The deformation process and energy absorption characteristics of the nested folded honeycomb structure provide valuable guidance for the design of new energy-absorbing materials.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Cellular and Composite Structures · Composite Structure Analysis and Optimization · Automotive and Human Injury Biomechanics

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

Affiliations

Harbin Engineering University

Funding

National Natural Science Foundation of China

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