Report (M.S.)--Virginia Polytechnic Institute and State University, 1994. / Abstract. Includes bibliographical references (leaves 40-41). Also available via the Internet.

Aeronautical structures, such as aircraft or missiles, are usually highly sophisticated systems often subjected to random vibration environment. Thus, in various design, development, and production stages, laboratory random vibration testing of sampled solid rocket motors on electromagnetic or hydraulic shakers are routinely performed as an important experiment-oriented quality control strategy. Nevertheless, it is crucial to understand the dynamic structural behavior of these layered viscoelastic cylinders such as solid rocket motors under random vibration tests analytically. In this study, a methodology has been developed to deal with the random vibration of a general class of composite structures with frequency-dependent viscoelastic material properties as represented by the example of solid rocket motors. The method combines the finite element method, structural dynamics, strain energy approach, and random vibration analysis concepts. The method is a more powerful technique capable of treating sophisticated random vibration problems with complicated geometry, nonhomogeneous materials, and frequency-dependent stiffness and damping properties. Before the random vibration analysis could proceed, a microcomputer-based dynamic mechanical analyzer system was used together with time-temperature superposition principle to obtain the frequency-dependent dynamic viscoelastic properties of the solid propellant. The strain energy approach has been used to calculate the frequency-dependent equivalent viscoelastic damping which is in turn judiciously represented by a combination of viscous damping and structural damping to accommodate this frequency dependent material property. Modal analysis data together with half power band width calculated at each natural frequency are highly useful guides in the harmonic analysis to achieve computational efficiency. On one hand, the technique used in this study has a hybrid taste in the sense that it makes use of best features and capabilities of both modal analysis and harmonic analysis to achieve the goal of random vibration analysis in addition to the power of finite element technique. The displacement, acceleration and stress power spectra have been obtained for significant points on the rocket model together with their root mean square values. These data can be used for various analyses, testing, design, and other purposes as discussed in later sections of this study. / Ph. D.

This thesis proposes an advanced architecting methodology. This methodology allows for the sizing and optimization of aircraft system architecture concepts and the establishment of subsystem development strategies. The process is implemented by an architecting team composed of subsystem experts and architects. The methodology organizes the architecture definition using the SysML language. Using meta-modeling techniques, this definition is translated into an analysis model which automatically integrates subsystem analyses in a fashion that represents the specific architecture concept described by the team. The resulting analysis automatically sizes the subsystems composing it, synthesizes their information to derive architecture-level performance and explores the architecture internal trade-offs. This process is facilitated using the Coordinated Optimization method proposed in this dissertation. This method proposes a multi-level optimization setup. An architecture-level optimizer orchestrates the subsystem sizing optimizations in order to optimize the aircraft as whole. The methodologies proposed in this thesis are tested and demonstrated on a proof of concept based on the exploration of turbo-electric propulsion aircraft concepts.

This dissertation progressively addresses research problems related to the trajectory optimization for fuel cell powered UAVs, from propulsion system model development, to optimal trajectory analyses and optimal trajectory applications. A dynamic model of a fuel cell powered UAV propulsion system is derived by combining a fuel cell system dynamic model, an electric motor dynamic model, and a propeller performance model. The influence of the fuel cell system dynamics on the optimal trajectories of a fuel cell powered UAV is investigated in two phases. In the first phase, the optimal trajectories of a fuel cell powered configuration and that of a conventional gas powered configuration are compared for point-to-point trajectory optimization problems with different performance index functions. In the second phase, the influence of the fuel cell system parameters on the optimal fuel consumption cost of the minimum fuel point-to-point optimal trajectories is investigated. This dissertation also presents two applications for the minimum fuel point-to-point optimal trajectories of a fuel cell powered UAV: three-dimensional minimum fuel route planning and path generation, and fuel cell system size optimization with respect to a UAV mission.

In the last half century, the aerospace industry has seen a dramatic paradigm shift from a focus on performance-at-any-cost to product economics and value. The steady increase in product requirements, complexity and global competition has driven aircraft manufacturers to seek broad portfolios of advanced technologies. The development costs and cycle times of these technologies vary widely, and the resulting design environment is one where decisions must be made under substantial uncertainty. Modeling and simulation have recently become the standard practice for addressing these issues; detailed simulations and explorations of candidate future states of these systems help reduce a complex design problem into a comprehensible, manageable form where decision factors are prioritized. While there are still fundamental criticisms about using modeling and simulation, the emerging challenge becomes ``How do you best configure uncertainty analyses and the information they produce to address real world problems?” One such analysis approach was developed in this thesis by structuring the input, models, and output to answer questions about the risk and economic impact of technology decisions in future aircraft programs. Unlike other methods, this method placed emphasis on the uncertainty in the cumulative cashflow space as the integrator of economic viability. From this perspective, it then focused on exploration of the design and technology space to tailor the business case and its associated risk in the cash flow dimension. The methodology is called CASSANDRA and is intended to be executed by a program manager of a manufacturer working of the development of future concepts. The program manager has the ability to control design elements as well as the new technology allocation on that aircraft. She is also responsible for the elicitation of the uncertainty in those dimensions within control as well as the external scenarios (that are out of program control). The methodology was applied on a future single-aisle 150 passenger aircraft design. The overall methodology is compared to existing approaches and is shown to identify more economically robust design decisions under a set of at-risk program scenarios. Additionally, a set of metrics in the uncertain cumulative cashflow space were developed to assist the methodology user in the identification, evaluation, and selection of design and technology. These metrics are compared to alternate approaches and are shown to better identify risk efficient design and technology selections. At the modeling level, an approach is given to estimate the production quantity based on an enhanced Overall Evaluation Criterion method that captures the competitive advantage of the aircraft design. This model was needed as the assumption of production quantity is highly influential to the business case risk. Finally, the research explored the capacity to generate risk mitigation strategies in to two analysis configurations: when available data and simulation capacity are abundant, and when they are sparse or incomplete. The first configuration leverages structured filtration of Monte Carlo simulation results. The allocation of design and technology risk is then identified on the Pareto Frontier. The second configuration identifies the direction of robust risk mitigation based on the available data and limited simulation ability. It leverages a linearized approximation of the cashflow metrics and identifies the direction of allocation using the Jacobian matrix and its inversion.