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Safety and Reliability Analysis of Composites Under Low Velocity Impact

[+] Author Affiliations
Shivdayal Patel

National Institute of Technology Delhi, New Delhi, India

Suhail Ahmad

Indian Institute of Technology Delhi, New Delhi, India

Manander Singh

GGSIP University, New Delhi, India

Paper No. OMAE2016-54582, pp. V003T02A006; 9 pages
  • ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering
  • Volume 3: Structures, Safety and Reliability
  • Busan, South Korea, June 19–24, 2016
  • Conference Sponsors: Ocean, Offshore and Arctic Engineering Division
  • ISBN: 978-0-7918-4994-1
  • Copyright © 2016 by ASME


Low velocity impact on composite plates is studied taking material properties and initial velocity as random parameters. Graphite fiber reinforced composite plates are susceptible to damage due to impact by foreign objects and in plane loading. In order to assess the safe load carrying capacity and the probability of failure under impact, dynamic analysis of composite plate subjected to low velocity impact is carried out. Finite element method is used to study impact. During impact, the in-plane damage modes such as matrix cracking, fiber failure and shear cracking are modeled using a failure criterion. The out of plane de-lamination is modeled using cohesive surfaces. The uncertainties associated with the system properties due to the inherent scatter in the geometric and material properties and input loads are modeled in a probabilistic fashion. Random parameters represent various characteristics appearing in the limit state function. The probabilistic analysis and reliability prediction of the system is carried out using Gaussian response surface method and validity of method for the present problem is establish using Monte Carlo simulation (MCS) procedure. Sensitivity analysis of the probability of failure with respect to random parameters considered is an important study for design optimization. The safety level qualification is achieved in terms of reliability level targeted. The mean and standard deviations of random variables show an appreciable influence on the probabilistic failure. Systematic changes in the input parameters are governed by the probabilistic sensitivity tools to achieve target reliability.

Copyright © 2016 by ASME



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