2020
DOI: 10.1007/s40430-020-02405-3
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Practicability of metallic hybrid systems in enhancing the energy absorption capacity: expansion and buckling mechanisms

Abstract: In this paper, the efficiency of a new hybrid design of energy absorber systems is investigated to improve the capability of the system in absorbing the kinetic energy of vehicles in case of a crash. This setup implements two well-known energy absorption mechanisms simultaneously to enhance the energy dissipation capacity of the system while occupying a limited constant space. The system consists of two individual mechanisms, expansion of a circular tube accompanied by buckling of two inner tubes, which dissip… Show more

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Cited by 4 publications
(3 citation statements)
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“…4. Responses of LF model at 21 training points and response of HF model at randomly selected 10 training points (number 1,4,5,6,7,10,11,13,14,18 shown in Table 3 and Table 4) are used. After generation of multi-fidelity model, the same process as high-fidelity optimization is followed.…”
Section: Multi-fidelity Optimization Resultsmentioning
confidence: 99%
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“…4. Responses of LF model at 21 training points and response of HF model at randomly selected 10 training points (number 1,4,5,6,7,10,11,13,14,18 shown in Table 3 and Table 4) are used. After generation of multi-fidelity model, the same process as high-fidelity optimization is followed.…”
Section: Multi-fidelity Optimization Resultsmentioning
confidence: 99%
“…Quadratic response surface model can be expressed as [13] (13) where ŷ(x) is the response prediction, L is the size of input vector x and b 0 , b i , b ii , b ij are the response surface parameters to be determined using linear regression. In order to construct multi-fidelity surrogate models, all data of LF model and data of HF model at randomly selected 10 training points are used (training points number 1,4,5,6,7,10,11,13,14,18 given in Tables 3 and 4 ). After determination of low-fidelity scale factor ( ) and the discrepancy function ( (x)), multi-fidelity surrogate model is constructed as it is explained in Sect.…”
Section: Accuracy Of Surrogate Modelsmentioning
confidence: 99%
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