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Adaptive crash energy absorber based on a granular jamming mechanism

Bartkowski Piotr, Bukowiecki Hubert, Gawiński Franciszek, Zalewski Robert

Bulletin of the Polish Academy of Sciences. Technical Sciences

2022

Vol. 70, nr 1

art. no. e139002

The following discussion concerns the use of innovative smart materials called vacuum-packed particles (VPPs) as active energy absorbers. VPP, also known as a granular jamming system, is a structure composed of granular media contained within an elastomer coating. By changing the vacuum pressure inside the coating, it is possible to control the mechanical properties of the structure. VPPs have many applications, e.g. in medicine, robotics, and vibration damping. No attempts have yet been made to use VPPs to absorb the energy of a collision, although, given their properties, this could very well be an interesting application. In the first part of the paper, the general concept of the absorber is presented. Then a prototype and the empirical tests conducted are precisely described. The middle part of the paper considers the basic properties of VPP and modeling methodology. A proposal for a constitutive equation is presented, and a numerical simulation using LS-Dyna was performed. In the final section, the concept of a smart parking post is presented.

Parameter identification of Bouc-Wen model for vacuum packed particles based on genetic algorithm

Bartkowski Piotr, Zalewski Robert, Chodkiewicz Paweł

Archives of Civil and Mechanical Engineering

2019

Vol. 19, no. 2

322--333

This paper investigates cylindrical samples made of vacuum packed particles. Such structures are composed of granular media placed in a hermetic encapsulation where, in the final stage, a partial vacuum is generated. The main advantage of such a structure is that the underpressure value makes it possible to control the global physical properties of granular systems. Materials with various grains are analyzed in the paper. A modified Bouc-Wen hysteresis model is adopted to describe the nonlinear properties of the tested specimens. To identify the model parameters, a genetic algorithm is applied. The proposed model is found to be in good agreement with the experimental data.