The main objective of this research is to develop an innovative multi-fidelity multi-disciplinary design, analysis and optimization suite that integrates certain solution generation codes and newly developed innovative tools to improve the overall optimization process. The research performed herein is divided into two parts: (1) the development of an MDAO framework by integration of variable fidelity physics-based computational codes, and (2) enhancements to such a framework by incorporating innovative features extending its robustness. The first part of this dissertation describes the development of a conceptual Multi-Fidelity Multi-Strategy and Multi-Disciplinary Design Optimization Environment (M3 DOE), in context of aircraft wing optimization. M3 DOE provides the user a capability to optimize configurations with a choice of (i) the level of fidelity desired, (ii) the use of a single-step or multi-step optimization strategy, and (iii) combination of a series of structural and aerodynamic analyses. The modularity of M3 DOE allows it to be a part of other inclusive optimization frameworks. The M3 DOE is demonstrated within the context of shape and sizing optimization of the wing of a Generic Business Jet aircraft. Two different optimization objectives, viz. dry weight minimization, and cruise range maximization are studied by conducting one low-fidelity and two high-fidelity optimization runs to demonstrate the application scope of M3 DOE. The second part of this dissertation describes the development of an innovative hybrid optimization framework that extends the robustness of M3 DOE by employing a proper orthogonal decomposition-based design-space order reduction scheme combined with the evolutionary algorithm technique. The POD method of extracting dominant modes from an ensemble of candidate configurations is used for the design-space order reduction. The snapshot of candidate population is updated iteratively using evolutionary algorithm technique of fitness-driven retention. This strategy capitalizes on the advantages of evolutionary algorithm as well as POD-based reduced order modeling, while overcoming the shortcomings inherent with these techniques. When linked with M3 DOE, this strategy offers a computationally efficient methodology for problems with high level of complexity and a challenging design-space. This newly developed framework is demonstrated for its robustness on a non-conventional supersonic tailless air vehicle wing shape optimization problem. / Ph. D.

Fully morphing wings allow the active change of the wing surface contours/wing configuration in flight enabling the optimum wing design for various flight regimes. These wing shape deformations are obtained by using smart actuators, which requires that the wing structure be flexible enough to morph under applied actuator loads and at the same time be fully capable of holding the aerodynamic loads. The study of such wing surface deformation requires an aeroelastic analysis since there is an active structural deformation under an applied aerodynamic field. Herein, a 2-D wing section, that is, an airfoil is considered. Modeling a variable geometry airfoil is performed using B-spline expansions. B-spline representation is also favorable towards optimization and provides a methodology to design curves based on discrete polygon points. The energy required for deforming the airfoil contour needs to be minimized. One of the methodologies adopted to minimize this actuation energy is to use the aerodynamic load itself for wing deformation. Another approach is to treat the airfoil deformation as a Multi Disciplinary Optimization (MDO) problem wherein the actuation energy needs to be minimized subject to certain constraints. The structural analysis is performed using commercial finite element software. The aerodynamic model is initiated from viscous-inviscid interaction codes and later developed from commercial Computational Fluid Dynamics (CFD) codes. Various modeling levels are investigated to determine the design requirements on morphing airfoils for enhanced aircraft maneuverability. / Singapore-MIT Alliance (SMA)

Multi-disciplinary ship synthesis models and multi-objective optimization techniques are increasingly being used in ship design. Multi-disciplinary models allow designers to break away from the traditional design spiral approach and focus on searching the design space for the best overall design instead of the best discipline-specific design. Complex design problems such as these often have high levels of uncertainty associated with them, and since most optimization algorithms tend to push solutions to constraint boundaries, the calculated "best" solution might be infeasible if there are minor uncertainties related to the model or problem definition. Consequently, there is a need to address uncertainty in optimization problems to produce effective and reliable results. This thesis focuses on adding a third objective, uncertainty, to the effectiveness and cost objectives already present in a multi-disciplinary ship synthesis model. Uncertainty is quantified using a "confidence of success" (CoS) calculation based on the mean value method. CoS is the probability that a design will satisfy all constraints and meet performance objectives. This work proves that the CoS concept can be applied to synthesis models to estimate uncertainty early in the design process. Multiple sources of uncertainty are realistically quantified and represented in the model in order to investigate their relative importance to the overall uncertainty. This work also presents methods to encourage a uniform distribution of points across the Pareto front. With a well defined front, designs can be selected and refined using a gradient based optimization algorithm to optimize a single objective while holding the others fixed. / Master of Science

This study focuses on the external configuration design of a tactical missile based on maximizing flight range while minimizing the radar signature which is a crucial performance parameter for survivability. It is known that shaping of a missile according to aerodynamic performance may have significant negative effects on the radar cross section. Thus, the impact of the geometry changes on the aerodynamic performance and the radar cross section is investigated. Suggorage models for the flight range, control effectiveness and the radar cross section (RCS) at an X band frequency are established by employing Genetic Algorithm. Accuracies of surrogate models are discussed in terms of statistical parameters. Seventeen geometrical parameters are considered as the design variables. Optimum combinations for the design variables are sought such that flight range is maximized while the radar cross section is minimized. The multi objective optimization problem is solved by imposing the static stability margin as a hard nonlinear constraint. Weighted sum approach is utilized to compare results with known missile configurations. Weights for flight range and Radar Cross Section are varied to obtain Pareto optimal solutions.

The primary research objective was to develop a methodology to support conceptual design of the Hybrid Wing Body (HWB) configuration. The absence of a horizontal tail imposes new stability and control requirements on the planform, and therefore requiring greater emphasis on control authority assessment than is typical for conceptual design. This required investigations into three primary areas of research. The first was to develop a method for designing an appropriate amount of redundancy. This was motivated widely varying numbers of trailing edge elevons in the HWB literature, and inadequate explanations for these early design decisions. The method identifies stakeholders, metrics of interest, and synthesizes these metrics using the Breguet range equation for system level comparison of control surface layouts. The second area of research was the development trim analysis methods that could accommodate redundant control surfaces, for which conventional methods performed poorly. A new measure of control authority was developed for vehicles with redundant controls. This is accomplished using concepts from the control allocation literature such as the attainable moment subset and the direct allocation method. The result is a continuous measure of remaining control authority suitable for use during HWB sizing and optimization. The final research area integrated performance and control authority to create a HWB sizing environment, and investigations into how to use it for design space exploration and vehicle optimization complete the methodology. The Monte Carlo Simulation method is used to map the design space, identify good designs for optimization, and to develop design heuristics. Finally, HWB optimization experiments were performed to discover best practices for conceptual design.

Automated solid modeling, integrated with computational fluid dynamics (CFD) and optimization of a 3D jet turbine engine has never been accomplished. This is due mainly to the computational power required, and the lack of associative parametric modeling tools and techniques necessary to adjust and optimize the design. As an example, the fluid domain of a simple household fan with three blades may contain 500,000 elements per blade passage. Therefore, a complete turbine engine that includes many stages, with sets of thirty or more blades each, will have hundreds of millions of elements. The fluid domains associated with each blade creates a nearly incomprehensible challenge. One method of organizing and passing geometric and non-geometric data is through the utilization of knowledge based engineering (KBE). The focus of this thesis will be the development of a set of techniques utilizing KBE principles to analyze an assembly which includes multiple fluid domains. This comprehensive system will be referred to as the Parametric Optimization Design System (PODS).