PROMOTING SCIENCE AND TECHNOLOGY BETWEEN INDIA AND THE U.S.

Rakesh K. Kapania

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Research Area

Aerospace & Ocean Engineering, Aerospace Engineering

Institution

University of Wisconsin-Madison

Finite Element Analysis of Nonlinear Analysis of Plates and Shells

While there are many shell finite elements available for shell structural analysis there are problems with using these elements for nonlinear analysis. The research is concerned with the development and application of high accuracy shell finite element geometrically nonlinear (large displacement, large rotation) analysis of plates and shells including buckling, postbuckling and nonlinear vibrations. Research also involves panel flutter and aerolastic tailoring of laminated beams and plates, and nonlinear transient analysis (including wave propagation) of laminated plates and shells, equivalent plate models for efficiently analyzing wing structures, and continuum models of built-up structures using neural networks.

Shape Sensitivity Analysis of Aerolastic Response

Research is concerned with determining the sensitivity of various aerolastic responses such as divergence, flutter, aerolastic lift distribution, and control effectiveness to small perturbations in the shape parameters of the wing. The wing parameters of interest are sweep, aspect ratio, taper ratio, wing surface area, and the root angle of attack. Emphasis is on determining these sensitivities using analytical methods as opposed to the finite difference method. Research also involves developing CFD/CSD interaction methodology.

Statistical Analysis of Structures

Deterministic analysis and design structures based on safety factors to allow for statistical variation is increasingly inadequate for modern structural design. The research effort is concerned with structures subject to random loadings such as wind or wave load and the calculation of the statistical properties of the nonlinear structural response.

Neural Networks and Genetic algorithm

This research involves using neural networks and genetic algorithms to solve inverse problems in structural health monitoring. Wavelets are being used to represent the signal.

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