The evaluation of an in-flight airfoil model requires extensive analysis of a variety of structural systems. Determining the safety of the design is a unique task dependant on the aircraft, flight environment, and physical requirements of the airfoil. With some areas of aerodynamic research choosing to utilize flight testing over wind tunnels the need to design and certify safe and reliable designs is a necessity. Commercially available codes have routinely demonstrated an ability to simulate complex systems. The union of three-dimensional design software with finite element programs, such as SolidWorks and COSMOSWorks, allows for a streamlined approach to the iterative task of design and simulation. The iterative process is essential to the safety analysis of the system. Results from finite-element analysis are used to determine material selection and component dimensions. These changes, in turn, produce different stress profiles, which will affect other components. The unique case presented in this study outlines the process required to certify a large swept-wing model mounted to a Cessna O-2 aircraft. The process studies the affect of aerodynamic loading on the hard-point structure inside the wing, as well as the model mounting structure, and support strut. The process does not end when numerical simulations indicate that each system is safe. Following numerical work, a series of static tests are used to verify that no unforeseen failures will occur. Although the process is tailored to one specific example, it outlines an approach that could be applied to any test platform. A different model may create a physically different system, but the safety analysis would remain the same.

The drive toward robust systems design, especially with respect to system affordablility throughout the system life-cycle, has led to the development of several advanced design methods. While these methods have been extremely successful in satisfying the needs for which they have been developed, they inherently leave a critical area unaddressed. None of them fully considers the effect of requirements on the selection of solution systems. The goal of all of current modern design methodologies is to bring knowledge forward in the design process to the regions where more design freedom is available and design changes cost less. Therefore, it seems reasonable to consider the point in the design process where the greatest restrictions are placed on the final design, the point in which the system level requirements are set. Historically the requirements have been treated as something handed down from above. However, neither the customer nor the solution provider completely understood all of the options that are available in the broader requirements space. If a method were developed that provided the ability to understand the full scope of the requirements space, it would allow for a better comparison of potential solution systems with respect to both the current and potential future requirements. The key to a requirements conscious method is to treat requirements differently from the traditional approach. The method proposed herein is known as Requirements Controlled Design (RCD). By treating the requirements as a set of variables that control the behavior of the system, instead of variables that only define the response of the system, it is possible to determine a-priori what portions of the requirements space that any given system is capable of satisfying. Additionally, it should be possible to identify which systems can satisfy a given set of requirements and the locations where a small change in one or more requirements poses a significant risk to a design program. This thesis puts forth the theory and methodology to enable RCD, and details and validates a specific method called the Modified Strength Pareto Evolutionary Algorithm (MSPEA).

In recent decades, complexity in aerospace programs has been increasing, leading to large budget and schedule overruns. Many of the risks of complex system development can be attributed to the inadequacy of linear methods when applied to nonlinear domains, i.e., oversimplification in a program amplifies the amount of risk produced when a system behaves unexpectedly. Effectively managing complexity involves responding to the various sources of complexity, whether it appears in the objective behavior of the system itself or in the subjective behavior of the people developing it. Thus, the engineering of complex systems requires nonlinear modeling methods of the system as well as nonlinear processes for developing the system. Much effort tends to be focused on addressing the objective sources of complexity and less is given to understanding and responding to the subjective sources of complexity. This present study examines how facilitating creativity in aerospace system development can serve as a potential strategy for managing complexity. Creativity is a kind of psychological process that integrates linear and nonlinear modes of thinking, and therefore systems engineering processes that reflect the creative process could reduce the risks of complexity. There are three primary results of this work: a novel application of creativity research to aerospace engineering processes; the most comprehensive published review of existing research on creativity in aerospace known to-date; and the proposal of two new systems engineering methods for facilitating creativity to manage complexity. These two new methods designed to improve the Waterfall methodology are as follows: the formation of a Parallel Systems Engineering group that functions analogously to how linear and nonlinear information are coordinated in creativity; and a conceptual model wherein aerospace programs are treated as a series of interdependent creative processes, which can be used to trace the propagation of complexity through various phases of system development.

A Bayesian adaptive sampling method is developed for highly coupled multidisciplinary design problems. The method addresses a major challenge in aerospace design: exploration of a design space with computationally expensive analysis tools such as computational fluid dynamics (CFD) or finite element analysis. With a limited analysis budget, it is often impossible to optimize directly or to explore a design space with off-line design of experiments (DoE) and surrogate models. This difficulty is magnified in multidisciplinary problems with feedbacks between disciplines because each design point may require iterative analyses to converge on a compatible solution between different disciplines. Bayesian Collaborative Sampling (BCS) is a bi-level architecture for adaptive sampling that simulataneously - concentrates disciplinary analyses in regions of a design space that are favorable to a system-level objective - guides analyses to regions where interdisciplinary coupling variables are probably compatible BCS uses Bayesian models and sequential sampling techniques along with elements of the collaborative optimization (CO) architecture for multidisciplinary optimization. The method is tested with the aero-structural design of a glider wing and the aero-propulsion design of a turbojet engine nacelle.

Formulations of optimization under uncertainty (OUU) commonly used in aerospace design—those based on treating statistical moments of the quantity of interest (QOI) as separate objectives—can result in stochastically dominated designs. A stochastically dominated design is undesirable, because it is less likely than another design to achieve a QOI at least as good as a given value, for any given value. As a remedy to this limitation for the multi-objective formulation of moments, a novel OUU formulation is proposed—dominance optimization. This formulation seeks a set of solutions and makes use of global optimizers, so is useful for early stages of the design process when exploration of design space is important. Similarly, to address this limitation for the single-objective formulation of moments (combining moments via a weighted sum), a second novel formulation is proposed—horsetail matching. This formulation can make use of gradient- based local optimizers, so is useful for later stages of the design process when exploitation of a region of design space is important. Additionally, horsetail matching extends straightforwardly to different representations of uncertainty, and is flexible enough to emulate several existing OUU formulations. Existing multi-fidelity methods for OUU are not compatible with these novel formulations, so one such method—information reuse—is generalized to be compatible with these and other formulations. The proposed formulations, along with generalized information reuse, are compared to their most comparable equivalent in the current state-of-the-art on practical design problems: transonic aerofoil design, coupled aero-structural wing design, high-fidelity 3D wing design, and acoustic horn shape design. Finally, the two novel formulations are combined in a two-step design process, which is used to obtain a robust design in a challenging version of the acoustic horn design problem. Dominance optimization is given half the computational budget for exploration; then horsetail matching is given the other half for exploitation. Using exactly the same computational budget as a moment-based approach, the design obtained using the novel formulations is 95% more likely to achieve a better QOI than the best value achievable by the moment-based design.