Characterization of Nanocomposites ─ Technology and Industrial Applications

These days advanced multi-scale hybrid materials are being produced in the industry, studied by the universities, and used in several applications. Unlike macro materials, it is difficult to obtain nano material properties due to the scales. For designers to perform any finite element analysis or durability and damage tolerance analysis, it is important to have knowledge of these properties.

This book is unique as it provides a multi-scale, multi-physics, and statistical analysis combined with multi-scale progressive failure analysis approach. The combination gives a very powerful tool for minimizing tests, improving accuracy, and understanding the effect of statistical nature in materials in addition to the mechanics of the advanced multi-scale material all the way to failure. The book focuses on methodology details backed with comparison of predictions with test data for various types of static, fatigue, dynamic, and crack growth problems.

These days advanced multi-scale hybrid materials are being produced in the industry, studied by the universities, and used in several applications. Unlike macro materials, it is difficult to obtain physical, mechanical, electrical, and thermal properties of nano materials due to the scales. For designers to perform any finite element analysis or durability and damage tolerance analysis, it is important to have knowledge of these properties.

The scope of the book is limited to the discussion of material characterization methodology for constituents of advanced hybrid multi-scale composites by means of reverse-engineering method using test data from macro/continuum level properties to micro and nano level. The book goes further in showing how these constituent reverse-engineered properties are used to predict macro-level continuum properties combining all nano- (Mori-Tanaka formulation) and micro-level (micro-mechanics [modified rule-of-mixture]) analytical models, statistical formulation, and finite element analysis in conjunction with multi-scale progressive failure analysis. The book compares the predictions with test data and is shown to cover static, explicit, and fracture mechanics types of loading conditions.

Frank Abdi is the chief scientist of AlphaSTAR Corporation. He has over 35 years’ experience in computer-based modeling and software development for a range of applications associated with advanced composite materials and structures, durability and damage tolerance, and aircraft certification. Before founding ASC, he worked at Boeing/Rockwell Aerospace advanced program. He has published more than 200 journal articles and conference papers. Dr. Abdi received a BS and MS in mechanical engineering from the University of Michigan (1974-1975) and a PhD in mechanical engineering from the University of Southern California (1980). He currently serves as adjunct professor at UCLA and as visiting professor at Imperial College London. He is the recipient of several awards, including NASA Software of the Year (1999), R&D 100 (2000, 2015), US Senate Tibbets Award (2001), and NASA Columbia Accident Investigation Award (2003).

Mohit Garg is a material and structural research engineer at AlphaSTAR Corporation. His research interests are focused mainly on modeling of thermosets, thermoplastics, and nano-, micro-, and macro-level mechanics, including the theoretical background of continuum mechanics. He has to his credit several publications on the failure assessment of composite material systems of various configurations, the material systems ranging from nanoparticles, polymers, and metals to two- and three-dimensional composite and hybrid systems. He is equally capable of performing and improving fracture mechanics–based analyses subjected to static, fatigue, and impact-type problems. Garg received his BSc in aerospace engineering from the University of Texas at Austin (2003) and his MSc in mechanical engineering from Massachusetts Institute of Technology (2005). His research projects have been accepted recently for publication in well-known journals.