Department

Department of Mechanical Engineering

First Advisor

Dr. Ray Fertig

Description

Composites are formed from two or more constituents each contributing to the final properties for improving chemical and mechanical behavior. Composites are widely used in different industries such as aerospace and wind energy. Fiber reinforced polymers (FRPs) are the most common types of composites due to their high strength to weigh ratio and their ability to be tailored to meet various application needs. The focus of this research is on unidirectional fiber composites. Experimental results have shown that larger composite samples have lower strength, this size dependent behavior of composites is the so called, size effect. This can be explained by the fact that as larger samples have more defects therefore, there is a larger variability in the properties contributing to the lower observed strength. Conventional analysis of composite materials typically focuses upon the average mechanical properties. While average values can predict the mean behavior, but size effect cannot be predicted as such models do not permit variability. To have an accurate prediction of composite strength, material variability has to be considered through stochastic modeling of microstructures. The presented research focuses on capturing size effect by accounting for fiber volume fraction variability throughout the specimen. The variability in fiber volume fraction was determined by the image analysis of actual micrographs. In our model, first, the stiffness and strength were determined for each fiber volume fraction and then randomly assigned to each element based on Monte Carlo method. The results of this research highlight the advancement of linking microstructural variability to composites structural behavior.

Comments

Oral Presentation, Wyoming NASA Grant Consortium

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Capturing Size Effect in FRPs Employing Stochastic Finite Element Simulations

Composites are formed from two or more constituents each contributing to the final properties for improving chemical and mechanical behavior. Composites are widely used in different industries such as aerospace and wind energy. Fiber reinforced polymers (FRPs) are the most common types of composites due to their high strength to weigh ratio and their ability to be tailored to meet various application needs. The focus of this research is on unidirectional fiber composites. Experimental results have shown that larger composite samples have lower strength, this size dependent behavior of composites is the so called, size effect. This can be explained by the fact that as larger samples have more defects therefore, there is a larger variability in the properties contributing to the lower observed strength. Conventional analysis of composite materials typically focuses upon the average mechanical properties. While average values can predict the mean behavior, but size effect cannot be predicted as such models do not permit variability. To have an accurate prediction of composite strength, material variability has to be considered through stochastic modeling of microstructures. The presented research focuses on capturing size effect by accounting for fiber volume fraction variability throughout the specimen. The variability in fiber volume fraction was determined by the image analysis of actual micrographs. In our model, first, the stiffness and strength were determined for each fiber volume fraction and then randomly assigned to each element based on Monte Carlo method. The results of this research highlight the advancement of linking microstructural variability to composites structural behavior.