Increasing costs for space shuttle missions translate to smaller, lighter, and more flexible satellites that maintain or improve current dynamic requirements. This is especially true for optical systems and surfaces. Lightweight, inflatable structures, otherwise known as gossamer structures, are smaller, lighter, and more flexible than current satellite technology. Unfortunately, little research has been performed investigating cost effective and feasible methods of dynamic analysis and control of these structures due to their inherent, non-linear dynamic properties. Gossamer spacecraft have the potential of introducing lenses and membrane arrays in orbit on the order of 25 m in diameter. With such huge structures in space, imaging resolution and communication transmissibility will correspondingly increase in orders of magnitude. A daunting problem facing gossamer spacecraft is their highly flexible nature. Previous attempts at ground testing have produced only localized deformation of the structure's skin rather than excitation of the global (entire structure's) modes. Unfortunately, the global modes are necessary for model parameter verification. The motivation of this research is to find an effective and repeatable methodology for obtaining the dynamic response characteristics of a flexible, inflatable structure. By obtaining the dynamic response characteristics, a suitable control technique may be developed to effectively control the structure's vibration. Smart materials can be used for both active dynamic analysis as well as active control. In particular, piezoelectric materials, which demonstrate electro-mechanical coupling, are able to sense vibration and consequently can be integrated into a control scheme to reduce such vibration. Using smart materials to develop a vibration analysis and control algorithm for a gossamer space structure will fulfill the current requirements of space satellite systems. Smart materials will help spawn the next generation of space satellite technology. / Master of Science

High precision inflatable surfaces were introduced when NASA created the ECHO 1 Balloon in 1960. The experiment proved that inflatable structures were a feasible alternative to their rigid counterparts for high precision applications. Today inflatable structures are being used in aviation and aerospace applications and the benefits of using such structures are being recognized. Inflatable structures used in high precision structures require the inflatable surfaces to have controllable and predictable geometries. Many applications such as solar sails and radar reflectors require the surface of such structures to have a uniform surfaces as such surfaces improve the efficiency of the structure. In the study presented, tests were conducted to determine which combination of factors affect surface flatness on a triangular test article. Factors tested include, three boundary conditions, two force loadings, and two fabric orientations. In total, twelve tests were conducted and results showed that which force loading and fabric orientations used greatly affected the Root Mean Square (RMS) of the surface. It was determined that using the triangular clamp along with 00 fabric orientation and high force loading provided the best results.

The aim of this research is to calibrate conductive polymer nanocomposite materials for large strain sensing and develop a structural health monitoring algorithm for gossamer structures by using nanocomposites as strain sensors. Any health monitoring system works on the principle of sensing the response (strain, acceleration etc.) of the structure to an external excitation and analyzing the response to find out the location and the extent of the damage in the structure. A sensor network, a mathematical model of the structure, and a damage detection algorithm are necessary components of a structural health monitoring system. In normal operating conditions, a gossamer structure can experience normal strain as high as 50%. But presently available sensors can measure strain up to 10% only, as traditional strain sensor materials do not show low elastic modulus and high electrical conductivity simultaneously. Conductive polymer nanocomposite which can be stretched like rubber (up to 200%) and has high electrical conductivity (sheet resistance 100 Ohm/sq.) can be a possible large strain sensor material. But these materials show hysteresis and relaxation in the variation of electrical properties with mechanical strain. It makes the calibration of these materials difficult. We have carried out experiments on conductive polymer nanocomposite sensors to study the variation of electrical resistance with time dependent strain. Two mathematical models, based on the modified fractional calculus and the Preisach approaches, have been developed to model the variation of electrical resistance with strain in a conductive polymer. After that, a compensator based on a modified Preisach model has been developed. The compensator removes the effect of hysteresis and relaxation from the output (electrical resistance) obtained from the conductive polymer nanocomposite sensor. This helps in calibrating the material for its use in large strain sensing. Efficiency of both the mathematical models and the compensator has been shown by comparison of their results with the experimental data. A prestressed square membrane has been considered as an example structure for structural health monitoring. Finite element analysis using ABAQUS has been carried out to determine the response of the membrane to an uniform transverse dynamic pressure for different damage conditions. A neuro-fuzzy system has been designed to solve the inverse problem of detecting damages in the structure from the strain history sensed at different points of the structure by a sensor that may have a significant hysteresis. Damage feature index vector determined by wavelet analysis of the strain history at different points of the structure are taken by the neuro-fuzzy system as input. The neuro-fuzzy system detects the location and extent of the damage from the damage feature index vector by using some fuzzy rules. Rules associated with the fuzzy system are determined by a neural network training algorithm using a training dataset, containing a set of known input and output (damage feature index vectors, location and extent of damage for different damage conditions). This model is validated by using the sets of input-output other than those which were used to train the neural network. / Ph. D.