Navin S. Rao

Bio: Navin S. Rao is an academic researcher from Mississippi State University. The author has contributed to research in topics: Phase velocity & Specific modulus. The author has an hindex of 1, co-authored 2 publications receiving 83 citations.

Inversion of composite material elastic constants from ultrasonic bulk wave phase velocity data using genetic algorithms

Abstract: In this paper, efforts on the reconstruction of material stiffness properties of unidirectional fiber-reinforced composites from obliquely incident ultrasonic bulk wave data, employing an inverse technique based on genetic algorithms, is described. Computer-generated ultrasonic phase velocity data, as a function of the angle of refraction in both symmetry and nonsymmetry through the thickness planes of unidirectional composites were used as the input to the genetic algorithm. A simple genetic algorithm, with optimal parameters chosen from the literature and numerical analysis (reported here), was implemented for the reconstruction. The inversion using this novel technique was found to be extremely promising in the characterization of the material stiffness properties. Stability to noise in the input phase velocity data set was also investigated. Advantages and disadvantages of the genetic reconstruction technique over conventional methods are also discussed.

Genetic Algorithms for Inversion of Ultrasonic Data for the Elastic Constants Measurement in Orthotropic Material Systems

Abstract: Fiber reinforced composite materials have been used in many structural applications varying from swimming pool diving boards to advanced aerospace components. The primary advantage of composites includes a high stiffness to weight ratio which in the past has come at an increased cost. Continued improvements in the development of cost effective manufacturing methods and development of low cost fibers and resin materials have increased the use of composites in infra-structural applications such as buildings, and bridges. As fiber reinforced composites become more widely used, the need for a reliable method to nondestructively characterize the material stiffness properties and identify material defects is becoming critical for ensuring a reliable level of performance.

Genetic algorithms in materials design and processing

Abstract: Genetic algorithms (GAs) are biologically inspired computing techniques, which tend to mimic the basic Darwinian concepts of natural selection. They are highly robust and efficient for most engineering optimising studies. Although a late entrant in the materials arena, GAs based studies are increasingly making their presence felt in many different aspects of this discipline. In recent times, GAs have been successfully used in numerous problems in the areas of atomistic material design, alloy design, polymer processing, powder compaction and sintering, ferrous production metallurgy, continuous casting, metal rolling, metal cutting, welding, and so on. The present review attempts to present the state of the art in this area. It includes three broad sections given as: fundamentals of genetic algorithms, genetic algorithms in materials design, and genetic algorithms in materials processing. The first section provides the reader with the basic concepts and the intricacies associated with this novel tec...