There is an increased need for spacecraft to quickly and efficiently de-orbit themselves as the amount of debris in orbit around Earth grows. Defunct spacecraft pose a significant threat to the LEO environment due to their risk of fragmentation. If these spacecraft are de-orbited at the end of their useful life their risk to future spacecraft is greatly lessened. A proposed method of efficiently de-orbiting spacecraft is to use an inflatable thin-film envelope to increase the body's area to mass ratio and thusly shortening its orbital lifetime. The system and analysis presented in this project is sized for use on a CubeSat as they are an effective utility as a technology demonstration platform. Analysis has been performed to characterize the orbital dynamics of high area to mass ratio spacecraft as well as the leak rate of such an inflatable device in a vacuum environment. Results show that a 1U CubeSat can be de-orbited using a 1.7 meter diameter spherical device in just under one year while using 0.7 grams of inflating gas, this is compared to over 25 years without any method of post-mission disposal.

Optical membranes are currently pursued for their ability to replace the conventional mirrors that are used to correct wave front aberration and space-based telescopes. Among some of the many benefits of using optical membranes, is their ability to considerably reduce the weight of the structure. As a secondary effect, the cost of transportation, which is of great interest in space applications, is reduced as well. Given the low density of these thin-film membranes, the lower end dynamics play a greater significant role than their rigid plate-like counterparts in achieving functional mirrors. Space-based mirrors are subjected to a series of disturbances. Among those encountered are thermal radiation, debris impact, and slewing maneuvers. Thus, dynamic control is essential for the adequate performance of thin-film membrane mirrors. With this in mind, the work described herein aims to improve the performance of optical membranes with an innovative, acoustical control approach to suppress vibration of optical membranes backed by an air cavity. This is achieved by using a centralized acoustic source in the cavity as the method of actuation. The acoustic actuation is of great interest since it does not mass load the membrane in the conventional way, as most methods of actuation would. To achieve this end goal, two structural-acoustic coupled models are developed to describe the dynamics of a pressurized optical membrane system. This is done through an impedance based modeling approach where the subsystems are modeled individually, and then coupled at the interface. The control of the membrane is implemented using a positive position feedback approach. The theory is also extended to positive velocity and positive acceleration feedback. Three experiments are carried out to validate the models previously mentioned. Successful implementation of a control experiment is also accomplished leading to considerable attenuations in the coupled membrane's dynamics. / Ph. D.

The incorporation of inflatable structures into aerospace systems can produce significant advantages in stowed volume to mechanical effectiveness and overall weight. Many applications of these ultra-lightweight systems are designed to precisely control internal or external surfaces, or both, to achieve desired performance. The modeling of these structures becomes complex due to the material nonlinearities inherent to the majority of construction materials used in inflatable structures. Furthermore, accurately modeling the response and behavior of the interfacing boundaries that are common to many inflatable systems will lead to better understanding of the entire class of structures. The research presented involved using nonlinear finite element simulations correlated with photogrammetry testing to develop a procedure for defining material properties for commercially available polyurethane-coated woven nylon fabric, which is representative of coated materials that have been proven materials for use in many inflatable systems. Further, the new material model was used to design and develop an inflatable pathfinder system which employs only internal pressure to control an assembly of internal membranes. This canonical inflatable system will be used for exploration and development of general understanding of efficient design methodology and analysis of future systems. Canonical structures are incorporated into the design of the phased pathfinder system to allow for more universal insight. Nonlinear finite element simulations were performed to evaluate the effect of various boundary conditions, loading configurations, and material orientations on the geometric precision of geometries representing typical internal/external surfaces commonly incorporated into inflatable pathfinder system. The response of the inflatable system to possible damage was also studied using nonlinear finite element simulations. Development of a correlated material model for analysis of the inflatable pathfinder system has improved the efficiency of design and analysis techniques of future inflatable structures.

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.