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Title page for ETD etd-11172015-150542

Type of Document Dissertation
Author Bogdanor, Michael James
URN etd-11172015-150542
Title Failure Prediction of Fiber Reinforced Composites Using Reduced Order Multiscale Models
Degree PhD
Department Civil Engineering
Advisory Committee
Advisor Name Title
Caglar Oskay Committee Chair
Douglas Adams Committee Member
Haoxiang Luo Committee Member
P.K. Basu Committee Member
Sankaran Mahadevan Committee Member
Stephen Clay Committee Member
  • multiscale mechanics
  • composites
  • uncertainty quantification
  • progressive damage
  • blind prediction
Date of Defense 2015-11-02
Availability unrestricted
Fiber reinforced polymer (FRP) composites present a significant opportunity for increas-

ing performance and energy efficiency in a number of technology sectors, most notably the

automotive and aerospace industries. In order to reduce the development costs for FRP ma-

terials, accurate and efficient predictive methods are required which capture the evolution

of damage at the heterogeneous microscale. The goal of this dissertation is to advance the

state of the art in the failure prediction of FRP composites through new multiscale methods

both for the mechanical response and propagation of uncertainty in the material. The contin-

ued development of the eigen-deformation based homogenization method with reduced order

models (EHM) is presented, including a new approach to address the tension-compression

stiffness anisotropy in the fiber direction and a novel parameter weighting method to capture

the disparate damage evolution under uniaxial and shear loading. A blind prediction study

of laminated IM7/977-3 composites using the improved EHM approach is presented for three

composite layups ([0,45,90,-45]2S, [30,60,90,-60,-30]2S, and [60,0,-60]3S) under static tension

and compression and tension-tension fatigue with open hole and unnotched configurations.

Additionally, Bayesian parameter calibration is implemented within the EHM framework to

quantify uncertainty in the composite and is utilized to predict the probabilistic behavior of

laminated composite specimens subject to strain rate-dependent effects.

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