The permanent magnet motors have become essential parts of modern motor drives recently because need for high efficiency and accurate dynamic performance arose in the industry. Some of the advantages they possess over other types of electric motors include higher torque density, higher efficiency due to absence of losses caused by field excitation, almost unity power factor, and almost maintenance free construction. With increasing need for specialized PM motors for different purposes and areas, much effort has also gone to design methodologies. In this thesis a design model is developed for surface PM motors. This model is used with an available optimization algorithm for the optimized design of a PM motor. Special attention is paid to measurement of parameters of a sample PM motor. As a result of this study, an effective analytical model with a proven accuracy by measurement results is developed and applied in a design process of a surface PM motor. Parametric and performance results of analytical model and tests have been presented comparatively. A prototype motor has been realized and tested.

This thesis introduces a design optimization methodology that is based on minimizing the Cost of Energy (COE) of a Horizontal Axis Wind Turbine (HAWT) that is to be operated at a specific wind site. In the design methodology for the calculation of the Cost of Energy, the Annual Energy Production (AEP) model to calculate the total energy generated by a unit wind turbine throughout a year and the total cost of that turbine are used. The AEP is calculated using the Blade Element Momentum (BEM) theory for wind turbine power and the Weibull distribution for the wind speed characteristics of selected wind sites. For the blade profile sections, either the S809 airfoil profile for all spanwise locations is used or NREL S-series airfoil families, which have different airfoil profiles for different spanwise sections, are used,. Lift and drag coefficients of these airfoils are obtained by performing computational fluid dynamics analyses. In sample design optimization studies, three different wind sites that have different wind speed characteristics are selected. Three scenarios are generated to present the effect of the airfoil shape as well as the turbine power. For each scenario, design optimizations of the reference wind turbines for the selected wind sites are performed the Cost of Energy and Annual Energy Production values are compared.

Unmanned Aerial Vehicles (UAVs) are the most dynamic growth sector of the aerospace industry today. The need to provide persistent intelligence, surveillance, and reconnaissance for military operations is driving the planned acquisition of over 5,000 UAVs over the next five years. The most pressing need is for quiet, small UAVs with endurance beyond what is capable with advanced batteries or small internal combustion propulsion systems. Fuel cell systems demonstrate high efficiency, high specific energy, low noise, low temperature operation, modularity, and rapid refuelability making them a promising enabler of the small, quiet, and persistent UAVs that military planners are seeking. Despite the perceived benefits, the actual near-term performance of fuel cell powered UAVs is unknown. Until the auto industry began spending billions of dollars in research, fuel cell systems were too heavy for useful flight applications. However, the last decade has seen rapid development with fuel cell gravimetric and volumetric power density nearly doubling every 2-3 years. As a result, a few design studies and demonstrator aircraft have appeared, but overall the design methodology and vehicles are still in their infancy. The design of fuel cell aircraft poses many challenges. Fuel cells differ fundamentally from combustion based propulsion in how they generate power and interact with other aircraft subsystems. As a result, traditional multidisciplinary analysis (MDA) codes are inappropriate. Building new MDAs is difficult since fuel cells are rapidly changing in design, and various competitive architectures exist for balance of plant, hydrogen storage, and all electric aircraft subsystems. In addition, fuel cell design and performance data is closely protected which makes validation difficult and uncertainty significant. Finally, low specific power and high volumes compared to traditional combustion based propulsion result in more highly constrained design spaces that are problematic for design space exploration. To begin addressing the current gaps in fuel cell aircraft development, a methodology has been developed to explore and characterize the near-term performance of fuel cell powered UAVs. The first step of the methodology is the development of a valid MDA. This is accomplished by using propagated uncertainty estimates to guide the decomposition of a MDA into key contributing analyses (CAs) that can be individually refined and validated to increase the overall accuracy of the MDA. To assist in MDA development, a flexible framework for simultaneously solving the CAs is specified. This enables the MDA to be easily adapted to changes in technology and the changes in data that occur throughout a design process. Various CAs that model a polymer electrolyte membrane fuel cell (PEMFC) UAV are developed, validated, and shown to be in agreement with hardware-in-the-loop simulations of a fully developed fuel cell propulsion system. After creating a valid MDA, the final step of the methodology is the synthesis of the MDA with an uncertainty propagation analysis, an optimization routine, and a chance constrained problem formulation. This synthesis allows an efficient calculation of the probabilistic constraint boundaries and Pareto frontiers that will govern the design space and influence design decisions relating to optimization and uncertainty mitigation. A key element of the methodology is uncertainty propagation. The methodology uses Systems Sensitivity Analysis (SSA) to estimate the uncertainty of key performance metrics due to uncertainties in design variables and uncertainties in the accuracy of the CAs. A summary of SSA is provided and key rules for properly decomposing a MDA for use with SSA are provided. Verification of SSA uncertainty estimates via Monte Carlo simulations is provided for both an example problem as well as a detailed MDA of a fuel cell UAV. Implementation of the methodology was performed on a small fuel cell UAV designed to carry a 2.2 kg payload with 24 hours of endurance. Uncertainty distributions for both design variables and the CAs were estimated based on experimental results and were found to dominate the design space. To reduce uncertainty and test the flexibility of the MDA framework, CAs were replaced with either empirical, or semi-empirical relationships during the optimization process. The final design was validated via a hardware-in-the loop simulation. Finally, the fuel cell UAV probabilistic design space was studied. A graphical representation of the design space was generated and the optima due to deterministic and probabilistic constraints were identified. The methodology was used to identify Pareto frontiers of the design space which were shown on contour plots of the design space. Unanticipated discontinuities of the Pareto fronts were observed as different constraints became active providing useful information on which to base design and development decisions.

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.

Modeling and simulation of granular matter has important applications in both natural science and industry. One widely used method is the discrete element method (DEM). It can be used for simulating granular matter in the gaseous, liquid as well as solid regime whereas alternative methods are in general applicable to only one. Discrete element analysis of large systems is, however, limited by long computational time. A number of solutions to radically improve the computational efficiency of DEM simulations are developed and analysed. These include treating the material as a nonsmooth dynamical system and methods for reducing the computational effort for solving the complementarity problem that arise from implicit treatment of the contact laws. This allow for large time-step integration and ultimately more and faster simulation studies or analysis of more complex systems. Acceleration methods that can reduce the computational complexity and degrees of freedom have been invented. These solutions are investigated in numerical experiments, validated using experimental data and applied for design exploration of iron ore pelletising systems. / <p>This work has been generously supported by Algoryx Simulation, LKAB (dnr 223-</p><p>2442-09), Umeå University and VINNOVA (2014-01901).</p>

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.