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There are many challenges facing the aerospace industry that can be addressed with new concepts, technologies, and materials. However, current design methods make it difficult to include these new ideas early in the design of aircraft. This is especially true in the structures discipline, which often uses weight-based methods based upon statistical regressions of historical data. A way to address this is to use physics-based structural analysis and design to create more detailed structural data. Thus, the overall research objective of this dissertation is to develop a physics-based structural analysis method to incorporate new concepts, technologies, and materials into the conceptual design phase. The design space of physics-based structural design problem is characterized as highly multimodal with numerous discontinuities; thus, a large number of alternatives must be explored. Current physics-based structural design methods tend to use high fidelity modeling and analysis tools that are computationally expensive. This dissertation proposes a modeling & simulation environment based on classical structural analysis methods. Using classical structural analysis will enable increased exploration of the design space by reducing the overall run time necessary to evaluate one alternative. The use of physics-based structural optimization using classical structural analysis is tested through experimentation. First the underlying hypotheses are tested in a canonical example by comparing different optimization algorithms ability to locate a global optimum identified through design space exploration. Then the proposed method is compared to a method based on higher fidelity finite element analysis as well as a method based on weight-based empirical data to validate the overall research objective.

A three dimensional wing design tool with analysis functions has been developed with embedded Euler/Navier-Stokes flow solver and a three dimensional hyperbolic grid generator. A graphical user interface has been constructed using PYTHON script language and the tool was enhanced with pre-processing and post-processing capabilities. Analysis and design procedures are demonstrated with automatic grid generation, automatic series solution and automatic graphs and reports generation.

This investigation sought first to determine the feasibility of generating a surrogate model of the interference drag between nacelles and wing-fuselage systems suitable for the inclusion in a multidisciplinary design optimization (MDO) framework. The target aircraft was a single-aisle, transonic aircraft with a freestream Mach number of 0.8 at 35,000 feet and a design lift coefficient of 0.5. Using an MDO framework is necessary for placing the nacelle because of the competing objectives of the disciplines involved in aircraft design including structures, acoustics, and aerodynamics. A secondary goal was to determine what tools are necessary for accurately capturing interference drag effects on the system. This research used both Euler computational fluid dynamics (CFD) with a coupled viscous drag estimation tool and Reynolds Averaged Navier-Stokes (RANS) CFD to estimate the system drag. The initial trade space exploration that varied the nacelle location across a baseline airframe configuration was completed with the Euler solver, and it showed that appreciable overlap between the wing and nacelle led to large increases in interference drag. A follow-on study was conducted with RANS CFD where the wing shape was tailored for each unique nacelle position. In comparing the results of the Euler and the RANS CFD, it was determined that RANS is required to accurately capture the flow features. Euler solvers can create artifacts due to the lack of viscous effects within the model. Wing tailoring is necessary because of the sensitivity of transonic flows to geometric changes and the addition of neighboring components, such as a nacelle. The research showed that for above and aft wing locations, a nacelle can overlap the trailing edge without incurring a drag penalty. Nacelles placed in the conventional location, forward and beneath the wing, displayed low interference drag effects, as the nacelle had a small and local impact on the wing's aerodynamics. Given the high cost of computing a RANS solution with wing tailoring, and the large design space for nacelle locations, building a surrogate model for interference drag was found to be prohibitive at this time. As the cost of computing and mesh generation decreases, collecting the data for building a surrogate model may become tractable. / Doctor of Philosophy / Engine placement on an aircraft is dependent on multiple disciplines. Engine placement affects the noise of the aircraft because the wing can shield or reflect the engine noise. Engine placement impacts the structural loads of an aircraft, with some positions requiring more reinforcement that adds to the cost and weight of the aircraft. Aerodynamically, the engine placement impacts the vehicle's drag. Taken together, the only means of trading the different disciplines' needs is through a multidisciplinary design optimization (MDO) framework. The challenge of MDO frameworks is that they require numerous solutions to effectively explore the trade space. Thus, MDO frameworks employ fast, low-order tools to compute hundreds or thousands of different combinations of features. A common approach to make running MDO analysis feasible is to develop surrogate models of the key considerations. Current aerodynamic drag build-ups for aircraft do not consider the interference drag associated with engine placement. The first goal of this research was to determine the feasibility of generating a surrogate model for inclusion in an MDO framework. In order to collect the data required for the surrogate, appropriate tools to capture the interference drag are required. Building a surrogate requires a large number of samples, thus the aerodynamic solver must be fast, robust, and accurate. An Euler (inviscid) computational fluid dynamics (CFD) was used do explore the engine placement design space to test the feasibility of building the surrogate model. The target aircraft was a single-aisle, transonic aircraft with a freestream Mach number of 0.8, flying at an altitude of 35,000 feet and a design lift coefficient of 0.5. The initial vehicle used a baseline wing, and the engine placement was varied across the wing span and fuselage. The results showed that the conventional location, where the engine is forward and beneath the wing, had the a modestly beneficial interference drag, though positions near the trailing edge and above the wing also showed neutral interference drag. In general, if the engine overlapped the wing, the interference drag increased dramatically. A follow-on study used Reynolds Averaged Navier-Stokes (RANS) CFD to investigate seven engine placements above and aft of the wing. Each of these positions had the wing tailored such that the wing performance would be typical of a good transonic wing. The results showed that with wing tailoring, a moderate amount of overlap between the wing and nacelle results in reduced or neutral interference drag. This is in contrast with the baseline wing results that showed moderate overlap led to large increases in interference drag. The results from this research suggest that building a surrogate model of interference drag for transonic aircraft is not feasible given today's computational resources. In order to accurately model the interference drag, one must use a RANS CFD solver and tailor the wing. These requirements increase the cost of evaluating an engine position such that collecting enough for a surrogate model is prohibitively expensive. As computational speeds increase, and the ability to automate CFD mesh generation becomes less time intensive, the feasibility may increase. Using an Euler solver is insufficient because of the lack of viscous effects in the flow. The lack of a boundary layer leads to artifacts appearing in the flow when the nacelle and wing are in close proximity.

Multidisciplinary Design Optimization (MDO) is an area in engineering design which has been growing rapidly in terms of applications in the last few decades, aircraft design being no exception to that. The application of MDO to aircraft and more specifically, wing design, presents many challenges, since disciplines like aerodynamics and structures have to be combined and interact. The level to which this interaction is implemented depends only on how much one is willing to pay in terms of computational cost. The objective of the current work is therefore to develop a simplified MDO tool, suitable for the preliminary design of aircraft wings. At the same time, versatility in the definition of optimization problems (in terms of design variables, constraints and objective function) is given great attention. At the same time, modularity will ensure that this framework is upgradeable with higher-fidelity and/or more capable modules. The disciplines that were chosen for interaction were aerodynamics and structures/ aeroelasticity, though more data can be extracted from their results in order to perform other types of analyses. The aerodynamics module employs a Vortex Lattice code developed specifically for the current implementation of the tool. The structural module is based on Equivalent Plate model theory. The fluid structure interaction is simply one-way, wherein the aerodynamics loads are passed on to the structural analyzer for computation of the static deformation. Semi-empirical relations are then used to estimate the flutter speed. The optimizer, which controls the activity of the other modules, makes use of a gradient based algorithm (Sequential Quadratic Programming) to search for a local minimum of a user defined objective function. Among the myriad of MDO strategies available, two are chosen to exemplify the modularity of the tool developed: Multidiscipline Feasible (MDF) and Sequential Optimization (SO), and their results are compared. Several case studies are analyzed to cover a broad spectrum of the capabilities of the framework. Because user interaction is of prime concern in design optimization, a graphical interface (GUI) of the tool is presented. Its advantages in terms of the set up of optimization problems and post-processing of results are made clear. In conclusion, some topics for future work regarding the expansion and improvement of the features of the application are noted.

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.