Damage characterization of embedded defects in composites using a hybrid thermography, computational, and artificial neural networks approach.

This work presents a hybrid thermography, computational, and Artificial Neural Networks (ANN) approach to characterize beneath the surface defects in composites. Computational simulations are created to model thermography experiments carried out on composite plates with controlled damage in the form of drilled holes. The computational models are then extended to create hypothetical composite component geometries of plates and pipes with embedded defects of varying sizes and shapes.

Computational design and development of high-performance polymer-composites as new encapsulant material for concentrated PV modules.

A computational design methodology is reported to propose a high-performance composite for backside encapsulation of concentrated photovoltaic (CPV) systems for enhanced module life and electrical power. Initially, potential polymer composite systems that are expected to provide the target properties, such as thermal conductivity, coefficient of thermal expansion, and long-term shear modulus are proposed using in-house built design codes. These codes are based on differential effective medium theory and mean-field homogenization, which lead to the selection of matrix, filler, volume fractions, and type of particulates.

Design and Development of Hybrid AlO Based Composites with Toughening and Self-Lubricating Second-Phase Inclusions.

Polycrystalline ceramics, such as alumina (AlO), are brittle and they generally wear by fracture mechanism, which limits their potential in tribological applications. In the present work, computational design tools are used to develop hybrid AlO composites reinforced with best combinations of toughening and self-lubricating second-phase particles for cutting tool inserts in dry machining applications. A mean-field homogenization approach and J-integral based fracture toughness models are employed to predict the effective structural properties (such as elastic modulus and fracture toughness) and related to the intrinsic attributes of second- phase inclusions in AlO matrix.

Experimental and Numerical Investigations on the Mechanical Characteristics of Carbon Fiber Sensors.

Carbon fiber-based materials possess excellent mechanical properties and show linear piezoresistive behavior, which make them good candidate materials for strain measurements. They have the potential to be used as sensors for various applications such as damage detection, stress analysis and monitoring of manufacturing processes and quality. In this paper, carbon fiber sensors are prepared to perform reliable strain measurements.

Generalized Effective Medium Theory for Particulate Nanocomposite Materials.

The thermal conductivity of particulate nanocomposites is strongly dependent on the size, shape, orientation and dispersion uniformity of the inclusions. To correctly estimate the effective thermal conductivity of the nanocomposite, all these factors should be included in the prediction model. In this paper, the formulation of a generalized effective medium theory for the determination of the effective thermal conductivity of particulate nanocomposites with multiple inclusions is presented.