@ARTICLE{Zarei, author = {Zarei, Hamid Reza and Rezaei, Mohammad and Soveity, Salem and }, title = {Numerical simulation of shear thickening fluid impregnated polypropylene fabric and comparing with experimental results}, volume = {17}, number = {2}, abstract ={Recently shear thickening fluids (STF) are applied more and more to improve the penetration resistance of fabrics. In this research, at first, the performance of the neat and STF impregnated fabric subjected to the impact of 8.7 mm diameter steel spherical projectile is investigated experimentally. Then, the numerical analysis is done to study the effective parameters such as fabric density, static and dynamic coefficients of friction between yarns and between projectile and fabric, boundary conditions and number of layers of fabric by using commercial tool LS-DYNA software. Previous studies expressed that the major factor that improves the energy absorption capacity of STF impregnated fabrics is the friction between the impact projectile, fabric, and yarns within the fabric, however here the investigations showed that in addition to the friction, the mass of added STF is effective in the results. Increasing the mass of the fabric by adding STF, is considered as the increasing density of the fabric. Empirical investigations showed that STF-impregnated fabrics exhibited a significant enhancement in penetration resistance performance as compared to neat fabric such that the projectile penetration subjected to the fabric with 44% wt STF decreased 63% compared to neat fabric. The simulation results showed that, if the STF effects just assign to increased friction, the projectile penetration decreased 43% compared to neat fabric. But if in addition to friction, the mass of the STF is considered as the effective parameter, the penetration decreased 58% which have good agreement with experimental data. }, URL = {http://mme.modares.ac.ir/article-15-1617-en.html}, eprint = {http://mme.modares.ac.ir/article-15-1617-en.pdf}, journal = {Modares Mechanical Engineering}, doi = {}, year = {2017} }