journal article·2017-11

Impact and close-in blast response of auxetic honeycomb-cored sandwich panels: Experimental tests and numerical simulations

Chang Qi, Alex Remennikov, Lian-Zheng Pei, Shu Yang, Zhi-Hang Yu, Tuan D. Ngo

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Abstract

Abstract Protecting building, critical infrastructure and military vehicles from Improvised Explosive Devices (IEDs) has become a critical task. This study aims to examine the performance of a new protective system utilizing auxetic honeycomb-cored sandwich panels for mitigation of shock loads from close-in and contact detonations of high explosives. Both field blast tests and drop weight tests were performed using the proposed sandwiches as a shield for concrete panels in combination with conventional steel protective plates. The combined shield was found to be effective in protecting reinforced concrete structures against severe impact and close-in blast loadings. The honeycomb core with re-entrant hexagonal cells shows evident auxetic characteristics which contribute substantially to outstanding force mitigation and blast-resistance performances of such sandwich panels. Numerical simulations showed good agreement with the experimental results. The proposed auxetic panels were found to perform better than conventional honeycomb panels of the same size, areal density and material. Both were found to boost the energy absorption of the monolithic steel plate by a factor of 2.5 by changing its deformation pattern under close-in blast loading. In addition, a combination of the steel plate and an auxetic sandwich panel has aerial specific energy absorption ( ASEA ) higher than either of them, showing great potential for the development of lightweight blast protection of civil, mining, military, nuclear infrastructure and vehicles.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Cellular and Composite Structures · Automotive and Human Injury Biomechanics · Structural Response to Dynamic Loads

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

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