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Littell, JustinThe Experimental and Analytical Characterization of the Macromechanical Response for Triaxial Braided Composite Materials
Doctor of Philosophy, University of Akron, 2008, Civil Engineering

Increasingly, carbon composite structures are being used in aerospace applications. Due to their high strength, high stiffness and low weight properties, they are good candidates for replacing many aerospace structures currently made out of aluminum or steel. Recently, many of the aircraft engine manufacturers have been developing new commercial jet engines which will use composite fan cases. Instead of using traditional composite layup techniques, these new fan cases will use a triaxially braided pattern, which improves case performance. The impact characteristics of composite materials for jet engine fan cases applications have been an important research topic, because federal regulations require that an engine case must be able to contain a blade and blade fragments during an engine blade out event. Once the impact characteristics of these triaxial braided materials are known, computer models can be developed to simulate a jet engine blade out event, thus reducing cost and time for development of these composite jet engine cases. The two main problems that have arisen in this area of research are that the material properties for these materials have not been fully determined, and computationally efficient computer models, which incorporate much of the micro-scale deformation and failure mechanisms, are not available.

This research addressed some of the deficiencies present in previous research regarding these triaxial braided composite materials. This research developed new techniques to accurately quantify the material properties of the triaxial braided composite materials. New test methods were developed for the composite constituent, the polymer resin, and representative composite coupons. These methods expanded previous research by using novel specimen designs along with using a non-contact measuring system which was also capable of identifying and quantifying many of the micro-scale failure mechanisms present in the materials. Finally, using the data gathered, a new hybrid micro-macromechanical computer model was created to simulate the behavior of these composite material systems under static and ballistic impact loading using the test data acquired. It also quantified how the fiber/matrix interface affected material response under static and impact loading.

The results showed that the test methods were capable of accurately quantifying the polymer resin under a variety of strain rates and temperature for three loading conditions, which is a constituent in the composite material. The resin strength and stiffness data showed a clear strain rate and temperature dependence. The data also showed the hydrostatic stress effects and hysteresis, all of which can be used by researchers developing composite constitutive models for the resins. The results for the composite data showed noticeable differences in strength, failure strain and stiffness in the different material systems presented. The investigations into the micro-scale failure mechanisms provided insights into the nature of the different material systems behaviors. Finally, the developed computer model predicted composite static strength and stiffness to within 10% of the gathered test data. It also agreed with the composite impact data, where available.

Committee:

Wieslaw Binienda, PhD (Advisor)

Subjects:

Aerospace Materials; Civil Engineering; Engineering; Mechanical Engineering; Mechanics; Polymers

Keywords:

Composite Materials; Materials Testing; Optical Measurement; Photogrammetry; Computer Modelling; Impact Simulation

Anderson, Jerone S.A Study of Nutrient Dynamics in Old Woman Creek Using Artificial Neural Networks and Bayesian Belief Networks
Master of Science (MS), Ohio University, 2009, Industrial and Systems Engineering (Engineering and Technology)
The Old Woman Creek National Estuary is studied in this project to evaluate effective modelling techniques for predicting Net Ecosystem Metabolism (NEM). NEM is modelled using artificial neural networks, Bayesian belief networks, and a hybrid model. A variety of data preprocessing techniques are considered prior to model development. The effects of discretization on model development are considered and discrete data is ultimately used to produce models which classify NEM into three ranges based on inputs with information significance. Artificial neural networks are found to be the most accurate for classification while Bayesian belief networks are found to provide a better framework for dynamically predicting NEM as inputs are changed.

Committee:

Gary R. Weckman, PhD (Advisor); David Millie, PhD (Committee Member); Kevin Berisso, PhD (Committee Member); Diana Schwerha, PhD (Committee Member)

Subjects:

Ecology; Engineering; Environmental Engineering; Industrial Engineering

Keywords:

BBN; ANN; ecology; NEM; Bayesian Belief Networks; Artificial Neural Networks; computer modelling