More electric aircraft systems, high power avionics, and a reduction in heat sink capacity have placed a larger emphasis on correctly satisfying aircraft thermal management requirements during conceptual design. Thermal management systems must be capable of dealing with these rising heat loads, while simultaneously meeting mission performance. Since all subsystem power and cooling requirements are ultimately traced back to the engine, the growing interactions between the propulsion and thermal management systems are becoming more significant. As a result, it is necessary to consider their integrated performance during the conceptual design of the aircraft gas turbine engine cycle to ensure that thermal requirements are met. This can be accomplished by using thermodynamic modeling and simulation to investigate the subsystem interactions while conducting the necessary design trades to establish the engine cycle. As the foundation for this research, a parsimonious, transparent thermodynamic model of propulsion and thermal management systems performance was created with a focus on capturing the physics that have the largest impact on propulsion design choices. A key aspect of this approach is the incorporation of physics-based formulations involving the concurrent usage of the first and second laws of thermodynamics to achieve a clearer view of the component-level losses. This is facilitated by the direct prediction of the exergy destruction distribution throughout the integrated system and the resulting quantification of available work losses over the time history of the mission. The characterization of the thermodynamic irreversibility distribution helps give the designer an absolute and consistent view of the tradeoffs associated with the design of the system. Consequently, this leads directly to the question of the optimal allocation of irreversibility across each of the components. An irreversibility allocation approach based on the economic concept of resource allocation is demonstrated for a canonical propulsion and thermal management systems architecture. By posing the problem in economic terms, exergy destruction is treated as a true common currency to barter for improved efficiency, cost, and performance. This then enables the propulsion systems designer to better fulfill system-level requirements and to create a system more robust to future requirements.

The surveillance of geographic borders and critical infrastructures using limited sensor capability has always been a challenging task in many homeland security applications. While geographic borders may be very long and may go through isolated areas, critical assets may be large and numerous and may be located in highly populated areas. As a result, it is virtually impossible to secure each and every mile of border around the country, and each and every critical infrastructure inside the country. Most often, a compromise must be made between the percentage of border or critical asset covered by surveillance systems and the induced cost. Although threats to homeland security can be conceived to take place in many forms, those regarding illegal penetration of the air, land, and maritime domains under the cover of day-to-day activities have been identified to be of particular interest. For instance, the proliferation of drug smuggling, illegal immigration, international organized crime, resource exploitation, and more recently, modern piracy, require the strengthening of land border and maritime awareness and increasingly complex and challenging national security environments. The complexity and challenges associated to the above mission and to the protection of the homeland may explain why a methodology enabling the design and optimization of distributed detection systems architectures, able to provide accurate scanning of the air, land, and maritime domains, in a specific geographic and climatic environment, is a capital concern for the defense and protection community. This thesis proposes a methodology aimed at addressing the aforementioned gaps and challenges. The methodology particularly reformulates the problem in clear terms so as to facilitate the subsequent modeling and simulation of potential operational scenarios. The needs and challenges involved in the proposed study are investigated and a detailed description of a multidisciplinary strategy for the design and optimization of detection architectures in terms of detection performance and cost is provided. This implies the creation of a framework for the modeling and simulation of notional scenarios, as well as the development of improved methods for accurate optimization of detection architectures. More precisely, the present thesis describes a new approach to determining detection architectures able to provide effective coverage of a given geographical environment at a minimum cost, by optimizing the appropriate number, types, and locations of surveillance and detection systems. The objective of the optimization is twofold. First, given the topography of the terrain under study, several promising locations are determined for each sensor system based on the percentage of terrain it is covering. Second, architectures of sensor systems able to effectively cover large percentages of the terrain at minimal costs are determined by optimizing the number, types and locations of each detection system in the architecture. To do so, a modified Genetic Algorithm and a modified Particle Swarm Optimization are investigated and their ability to provide consistent results is compared. Ultimately, the modified Particle Swarm Optimization algorithm is used to obtain a Pareto frontier of detection architectures able to satisfy varying customer preferences on coverage performance and related cost.

An approach was developed which allows for design studies of commercial aircraft using physics-based noise analysis methods while retaining the ability to perform the rapid tradeoff and risk analysis studies needed at the conceptual design stage. A prototype integrated analysis process was created for computing the total aircraft EPNL at the Federal Aviation Regulations Part 36 certification measurement locations using physics-based methods for fan rotor-stator interaction tones and jet mixing noise. The analysis process was then used in combination with design of experiments to create response surface equations (RSEs) for the engine and aircraft performance metrics, geometric constraints and takeoff and landing noise levels. In addition, Monte Carlo analysis was used to assess the expected variability of the metrics under the influence of uncertainty, and to determine how the variability is affected by the choice of engine cycle. Finally, the RSEs were used to conduct a series of proof-of-concept conceptual-level design studies demonstrating the utility of the approach. The study found that a key advantage to using physics-based analysis during conceptual design lies in the ability to assess the benefits of new technologies as a function of the design to which they are applied. The greatest difficulty in implementing the physics-based analysis proved to be the generation of design geometry at a sufficient level of detail for high-fidelity analysis.

This thesis presents a method for streamlining the process of obtaining and interpreting quantitative data for the purpose of creating a low-fidelity modeling and simulation environment. By providing a more efficient means for obtaining such information, quantitative analyses become much more practical for decision-making in the very early stages of design, where traditionally, quants are viewed as too expensive and cumbersome for concept evaluation. The method developed to address this need uses a Systematic Process for Adaptive Concept Exploration (SPACE). In the SPACE method, design space exploration occurs in a sequential fashion; as data is acquired, the sampling scheme adapts to the specific problem at hand. Previously gathered data is used to make inferences about the nature of the problem so that future samples can be taken from the more interesting portions of the design space. Furthermore, the SPACE method identifies those analyses that have significant impacts on the relationships being modeled, so that effort can be focused on acquiring only the most pertinent information. The results show that the combination of a tailored data set, and an informed model structure work together to provide a meaningful quantitative representation of the system while relying on only a small amount of resources to generate that information. In comparison to more traditional modeling and simulation approaches, the SPACE method provides a more accurate representation of the system using fewer resources to generate that representation. For this reason, the SPACE method acts as an enabler for decision making in the very early design stages, where the desire is to base design decisions on quantitative information while not wasting valuable resources obtaining unnecessary high fidelity information about all the candidate solutions. Thus, the approach enables concept selection to be based on parametric, quantitative data so that informed, unbiased decisions can be made.

The scale of engineering problems has sharply increased over the last twenty years. Larger coupled systems, increasing complexity, and limited resources create a need for methods that automatically decompose problems into manageable sub-problems by discovering and leveraging problem structure. The ability to learn the coupling (inter-dependence) structure and reorganize the original problem could lead to large reductions in the time to analyze complex problems. Such decomposition methods could also provide engineering insight on the fundamental physics driving problem solution. This work forwards the current state of the art in engineering decomposition through the application of techniques originally developed within computer science and information theory. The work describes the current state of automatic problem decomposition in engineering and utilizes several promising ideas to advance the state of the practice. Mutual information is a novel metric for data dependence and works on both continuous and discrete data. Mutual information can measure both the linear and non-linear dependence between variables without the limitations of linear dependence measured through covariance. Mutual information is also able to handle data that does not have derivative information, unlike other metrics that require it. The value of mutual information to engineering design work is demonstrated on a planetary entry problem. This study utilizes a novel tool developed in this work for planetary entry system synthesis. A graphical method, force-based clustering, is used to discover related sub-graph structure as a function of problem structure and links ranked by their mutual information. This method does not require the stochastic use of neural networks and could be used with any link ranking method currently utilized in the field. Application of this method is demonstrated on a large, coupled low-thrust trajectory problem. Mutual information also serves as the basis for an alternative global optimizer, called MIMIC, which is unrelated to Genetic Algorithms. Advancement to the current practice demonstrates the use of MIMIC as a global method that explicitly models problem structure with mutual information, providing an alternate method for globally searching multi-modal domains. By leveraging discovered problem inter-dependencies, MIMIC may be appropriate for highly coupled problems or those with large function evaluation cost. This work introduces a useful addition to the MIMIC algorithm that enables its use on continuous input variables. By leveraging automatic decision tree generation methods from Machine Learning and a set of randomly generated test problems, decision trees for which method to apply are also created, quantifying decomposition performance over a large region of the design space.

Traditionally, the design of complex aerospace systems requires iteration among segregated disciplines such as aerodynamic modeling and trajectory optimization. Multidisciplinary design optimization algorithms have been developed to efficiently orchestrate the interaction among these disciplines during the design process. For example, vehicle capability is generally obtained through sequential iteration among vehicle shape, aerodynamic performance, and trajectory optimization routines in which aerodynamic performance is obtained from large pre-computed tables that are a function of angle of attack, sideslip, and flight conditions. This numerical approach segregates advancements in vehicle shape design from advancements in trajectory optimization. This investigation advances the state-of-the-art in conceptual hypersonic aerodynamic analysis and trajectory optimization by removing the source of iteration between aerodynamic and trajectory analyses and capitalizing on fundamental linkages across hypersonic solutions. Analytic aerodynamic relations, like those derived in this investigation, are possible in any flow regime in which the flowfield can be accurately described analytically. These relations eliminate the large aerodynamic tables that contribute to the segregation of disciplinary advancements. Within the limits of Newtonian flow theory, many of the analytic expressions derived in this investigation provide exact solutions that eliminate the computational error of approximate methods widely used today while simultaneously improving computational performance. To address the mathematical limit of analytic solutions, additional relations are developed that fundamentally alter the manner in which Newtonian aerodynamics are calculated. The resulting aerodynamic expressions provide an analytic mapping of vehicle shape to trajectory performance. This analytic mapping collapses the traditional, segregated design environment into a single, unified, mathematical framework which enables fast, specialized trajectory optimization methods to be extended to also include vehicle shape. A rapid trajectory optimization methodology suitable for this new, mathematically integrated design environment is also developed by relying on the continuation of solutions found via indirect methods. Examples demonstrate that families of optimal hypersonic trajectories can be quickly constructed for varying trajectory parameters, vehicle shapes, atmospheric properties, and gravity models to support design space exploration, trade studies, and vehicle requirements definition. These results validate the hypothesis that many hypersonic trajectory solutions are connected through fast indirect optimization methods. The extension of this trajectory optimization methodology to include vehicle shape through the development of analytic hypersonic aerodynamic relations enables the construction of a unified mathematical framework to perform rapid, simultaneous hypersonic aerodynamic and trajectory optimization. Performance comparisons relative to state-of-the-art methodologies illustrate the computational advantages of this new, unified design environment.

Engineering design may be viewed as a decision making process that supports design tradeoffs. The designer makes decisions based on information available and engineering judgment. The designer determines the direction in which the design must proceed, the procedures that need to be adopted, and develops a strategy to perform successive decisions. The design is only as good as the decisions made, which is in turn dependent on the information available. Information is time and process dependent. This thesis work focuses on developing a coherent bottom-up framework and methodology to improve information transfer and decision making while designing complex systems. The rotorcraft drive system is used as a test system for this methodology. The traditional serial design approach required the information from one discipline and/or process in order to proceed with the subsequent design phase. The Systems Engineering (SE) implementation of Concurrent Engineering (CE) and Integrated Product and Process Development (IPPD) processes tries to alleviate this problem by allowing design processes to be performed in parallel and collaboratively. The biggest challenge in implementing Concurrent Engineering is the availability of information when dealing with complex systems such as aerospace systems. The information is often incomplete, with large amounts of uncertainties around the requirements, constraints and system objectives. As complexity increases, the design process starts trending back towards a serial design approach. The gap in information can be overcome by either “softening” the requirements to be adaptable to variation in information or to delay the decision. Delayed decisions lead to expensive modifications and longer product design lifecycle. Digitization of IPPD tools for complex system enables the system to be more adaptable to changing requirements. Design can proceed with “soft” information and decisions adapted as information becomes available even at early stages. The advent of modern day computing has made digitization and automation possible and feasible in engineering. Automation has demonstrated superior capability in design cycle efficiency [1]. When a digitized framework is enhanced through automation, design can be made adaptable without the requirement for human interaction. This can increase productivity, and reduce design time and associated cost. An important aspect in making digitization feasible is having the availability of parameterized Computer Aided Design (CAD) geometry [2]. The CAD geometry gives the design a physical form that can interact with other disciplines and geometries. Central common CAD database allows other disciplines to access information and extract requirements; this feature is of immense importance while performing systems syntheses. Through database management using a Product Lifecycle Management (PLM) system, Integrated Product Teams (IPTs) can exchange information between disciplines and develop new designs more efficiently by collaborating more and from far [3]. This thesis focuses on the challenges associated with automation and digitization of design. Making more information available earlier goes jointly with making the design adaptable to new information. Using digitized sizing, synthesis, cost analysis and integration, the drive system design is brought in to early design. With modularity as the objective, information transfer is made streamlined through the use of a software integration suite. Using parametric CAD tools, a novel ‘Fully-Relational Design’ framework is developed where geometry and design are adaptable to related geometry and requirement changes. During conceptual and preliminary design stages, the airframe goes through many stages of modifications and refinement; these changes affect the sub-system requirements and its design optimum. A fully-relational design framework takes this into account to create interfaces between disciplines. A novel aspect of the fully-relational design methodology is to include geometry, spacing and volume requirements in the system design process. Enabling fully-relational design has certain challenges, requiring suitable optimization and analysis automation. Also it is important to ensure that the process does not get overly complicated. So the method is required to possess the capability to intelligently propagate change. There is a need for suitable optimization techniques to approach gear train type design problems, where the design variables are discrete in nature and the values a variables can assume is a result of cascading effects of other variables. A heuristic optimization method is developed to analyze this multimodal problem. Experiments are setup to study constraint dependencies, constraint-handling penalty methods, algorithm tuning factors and innovative techniques to improve the performance of the algorithm. Inclusion of higher fidelity analysis in early design is an important element of this research. Higher fidelity analyses such as nonlinear contact Finite Element Analysis (FEA) are useful in defining true implied stresses and developing rating modification factors. The use of Topology Optimization (TO) using Finite Element Methods (FEM) is proposed here to study excess material removal in the gear web region.

Aluminum Cross-Car Beam is significantly lighter than the conventional steel counterpart and presents superior energy absorption characteristics. The challenge is however, its considerably higher cost, rendering it difficult for the aluminum one to compete in the automotive market. In this work, using material distribution techniques and stochastic optimization, a Multidisciplinary Design Optimization procedure is developed to optimize an existing Cross-Car Beam model with respect to the cost. Topology, Topography, and gauge optimizations are employed in the development of the optimization disciplines. Based on a qualitative cost assessment, penalty functions are designed to penalize costly designs. Noise-Vibration-Harshness (NVH) performance is the key constraint of the optimization. To fulfill this requirement, natural frequencies are obtained using modal analysis. Undesirable designs with respect to the NVH criteria are gradually eliminated from the optimization cycles. The new design is verified by static loading scenario and evaluated in terms of the cost saving it offers.

Aluminum Cross-Car Beam is significantly lighter than the conventional steel counterpart and presents superior energy absorption characteristics. The challenge is however, its considerably higher cost, rendering it difficult for the aluminum one to compete in the automotive market. In this work, using material distribution techniques and stochastic optimization, a Multidisciplinary Design Optimization procedure is developed to optimize an existing Cross-Car Beam model with respect to the cost. Topology, Topography, and gauge optimizations are employed in the development of the optimization disciplines. Based on a qualitative cost assessment, penalty functions are designed to penalize costly designs. Noise-Vibration-Harshness (NVH) performance is the key constraint of the optimization. To fulfill this requirement, natural frequencies are obtained using modal analysis. Undesirable designs with respect to the NVH criteria are gradually eliminated from the optimization cycles. The new design is verified by static loading scenario and evaluated in terms of the cost saving it offers.

Improvements in performance, reliability and durability as well as reductions in production costs, remain critical prerequisites for the commercialization of proton exchange membrane fuel cells. In this thesis, a computational framework for fuel cell analysis and optimization is presented as an innovative alternative to the time consuming trial-and-error process currently used for fuel cell design. The framework is based on a two-dimensional through-the-channel isothermal, isobaric and single phase membrane electrode assembly (MEA) model. The model input parameters are the manufacturing parameters used to build the MEA: platinum loading, platinum to carbon ratio, electrolyte content and gas diffusion layer porosity. The governing equations of the fuel cell model are solved using Netwon's algorithm and an adaptive finite element method in order to achieve quadratic convergence and a mesh independent solution respectively. The analysis module is used to solve two optimization problems: i) maximize performance; and, ii) maximize performance while minimizing the production cost of the MEA. To solve these problems a gradient-based optimization algorithm is used in conjunction with analytical sensitivities. The presented computational framework is the first attempt in the literature to combine highly efficient analysis and optimization methods to perform optimization in order to tackle large-scale problems. The framework presented is capable of solving a complete MEA optimization problem with state-of-the-art electrode models in approximately 30 minutes. The optimization results show that it is possible to achieve Pt-specific power density for the optimized MEAs of 0.422 $g_{Pt}/kW$. This value is extremely close to the target of 0.4 $g_{Pt}/kW$ for large-scale implementation and demonstrate the potential of using numerical optimization for fuel cell design.