We are in an era of massive spending cuts in educational institutions, aerospace companies and governmental entities. Educational institutions are pursuing more training for less money, aerospace companies are reducing the cost of gaining ight heritage and the government is cutting budgets and their response times. Organizations are accomplishing this improved efficiency by moving away from large-scale satellite projects and developing pico and nanosatellites following the CubeSat specifications. One of the major challenges of developing satellites to the standard CubeSat mission requirements is meeting the exceedingly tight power, data and communication constraints. A MATLAB toolbox was created to assist the CubeSat community with understanding these restrictions, optimizing their systems, increasing mission success and decreasing the time building to these initial requirements. The Toolbox incorporated the lessons learned from the past nine years of CubeSats' successes and Analytical Graphics, Inc. (AGI)'s Satellite Tool Kit (STK). The CubeSat Mission Planning Toolbox (CMPT) provides graphical representations of the important requirements a systems engineer needs to plan their mission. This includes requirements for data storage, ground station facilities, orbital parameters, and power. CMPT also allows for a comparison of broadcast (BC) downlinking to Ground Station Initiated (GSI) downlinking for payload data using federated ground station networks. Ultimately, this tool saves time and money for the CubeSat systems engineer

As engineering technologies continue to grow and improve, the complexities in the engineering models which utilize these technologies also increase. This seemingly endless cycle of increased computational power and demand has sparked the need to create representative models, or metamodels, which accurately reflect these complex design spaces in a computationally efficient manner. As research into metamodeling and using advanced metamodeling techniques continues, it is important to remember design engineers who need to use these advancements. Even experienced engineers may not be well versed in the material and mathematical background that is currently required to generate and fully comprehend advanced complex metamodels. A metamodeling environment which utilizes an advanced metamodeling technique known as Gaussian Process is being developed to help bridge the gap that is currently growing between the research community and design engineers. This tool allows users to easily create, modify, query, and visually/numerically assess the quality of metamodels for a broad spectrum of design challenges.

<p> The optimization study of space frames has been considered in two aspects in this project work. The first was to develop a suitable optimization technique for a nonlinear programming problem including equality constraints, without any particular reference to structural optimization. The necessacity for the above requirement was due to the fact that almost all existing methods on optimization have some limitation. The second object of this study was to set up the necessary equations for the constraints on stress and on frequency for the structural model used, and then to use the developed technique to optimize the structural model for minimum weight. </p> <p> A simple and effective strategy, which is a combination of direct search and linear approximate programming is believed to have been developed for optimization of simple nonlinear type equations. </p> <p> The analysis of the space structure and the study of structural optimization revealed several difficulties inherent in the evaluation of constraining equations for the stresses and frequencies, which makes the optimization very difficult. </p> / Thesis / Master of Engineering (ME)

Reliable, affordable access to electrical power is a requirement for almost all aspects of developed societies. Challenges associated with reducing carbon emissions has led to growing interest in nuclear-renewable hybrid energy systems (N-RHES). Much work has already been done in suggesting and analyzing various N-RHES using a variety of optimization techniques and assumptions. This work builds upon previous techniques for simultaneous combined design and dispatch optimization (CDDO) for hybrid energy systems (HES). The first contribution of this work is the development and application of sensitivity analysis tailored to the combined design and dispatch optimization problem. This sensitivity analysis cover uncertainty in design parameters, time series and dispatch horizon lengths. The result is a deeper insight into which sources of uncertainty are most important to account for and how the uncertainty around these sources can be quantified. The second contribution of this work is a novel multi-scale optimization algorithm for the combined HES design and dispatch optimization. This algorithm supports optimization of nonlinear models over very long-time horizons. This method is based on a multi-dimensional distribution of the optimal capacities for a system as determined by a large number of combined design and dispatch optimization problems each covering a subset of the complete time horizon. This method shows good agreement with the direct solution to multiple example systems and is then used to solve a problem with a dispatch horizon length 112.5 times longer than is solvable directly. The third contribution of this work is the application of the novel multi-scale method to three HES. Each of the application systems is used to demonstrate the strengths, validation and applicability of the developed algorithm to a wide range of possible HES/NHES designs.

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<p> Global routing in VLSI (very large scale integration) design is one of the most challenging discrete optimization problems in computational theory and practice. In this thesis, we present a polynomial time approximation algorithm for the global routing problem based on an integer programming formulation. The algorithm features a theoretical approximation bound, while ensuring all the routing demands are concurrently satisfied.</p> <p> We provide both a serial and a parallel implementation, as well as develop several heuristics to improve the quality of the solution and reduce running time. Our computational tests on a well-known benchmark set show that, combined with certain heuristics, our new algorithms perform very well compared with other integer programming approaches.</p> / Thesis / Master of Science (MSc)

The success of any racing car hinges on three key factors: its speed, handling, and reliability. In a highly competitive environment where lap times are extremely tight, even slight variations in components can significantly affect performance and, consequently, lap times. At the heart of a race car's performance lies the upright—a critical component of its suspension system. The upright serves to link the suspension arms to the wheels, effectively transmitting steering and braking forces to the suspension setup. Achieving optimal performance requires finding the right balance between lightweight design and ample stiffness, crucial for maintaining precise steering geometry and overall vehicle dynamics, especially under intense loads. Furthermore, there is a need to explore the system of structural optimization and seamlessly integrate Finite Element (FE) Models into the mathematical optimization process. This thesis explores a technique for parametric structural optimization utilizing finite element analysis and response surfaces to minimize the weight of the upright. Various constraints such as frequency, stress, displacement, and fatigue are taken into consideration during this optimization process. A parametric finite element model of the upright was designed, along with the mathematical formulation of the optimization problem as a nonlinear programming problem, based on the design objectives and suspension geometry. By conducting parameter sensitivity analysis, three design variables were chosen from a pool of five, and response surfaces were constructed to represent the constraints and objective function to be used to solve the optimization problem using Sequential Quadratic Programming (SQP). To streamline the process of parameter sensitivity analysis and response surface development, a Python scripting procedure was employed to automate the finite element job analysis and results extraction. The optimized upright design resulted in overall weight reduction of 25.3% from the maximum weight design of the parameterized upright. / Master of Science / The success of any racing car depends on three key factors: its speed, handling and reliability. In a highly competitive environment where lap times are extremely tight, even slight variations in components can significantly affect performance and consequently, lap times. At the heart of a race car's performance lies the upright—a critical component of its suspension system. The upright serves to link the suspension arms to the wheels, effectively transmitting steering and braking forces to the suspension setup. To achieve the best performance, upright must be as light as possible but it needs to be strong enough to ensure that the car is predictable when turning in a corner or while braking. Additionally, there is a need to explore methods of structural optimization and integrate finite element analysis seamlessly into the optimization process. Finite element analysis (FEA) is the use of part models, simulations, and calculations to predict and understand how an object might behave under certain physical conditions. This thesis examines a technique for optimizing the upright by designing it with numerous adjustable features for testing and then utilizing response surfaces to minimize its weight. Throughout this process, factors such as vibration, stress, deformation, and fatigue are carefully considered. A detailed parametric finite element model of the upright was developed, alongside the formulation of the optimization problem as a nonlinear programming problem, based on the objectives of the design and the geometry of the suspension. Through rigorous testing of parameters for optimization potential, design variables are selected for optimization. Response surfaces were then constructed to represent the constraints and objective function necessary to solve the optimization problem using Sequential Quadratic Programming (SQP). To enhance the efficiency of this process, a Python script was created to handle specific tasks within the finite element solver. This automation streamlined the analysis of the finite element model and the extraction of results. Ultimately, the optimized design of the upright yielded a 25.3% reduction in weight compared to its maximum weight configuration.

Although additive manufacturing technologies are often referred to as "3D printing," the family of technologies typically deposit material on a layer-by-layer basis. For material extrusion (ME) in particular, the deposition process results in weak inter- and intra-layer bonds that reduce mechanical performance in those directions. Despite this shortcoming, ME offers the opportunity to specifically and preferentially align the reinforcement of a composite material throughout a part by customizing the toolpath. Recent developments in multi-axis deposition have demonstrated the ability to place material outside of the XY-plane, enabling depositions to align to any 3D (i.e., non-planar) vector. Although mechanical property improvements have been demonstrated, toolpath planning capabilities are limited; the geometries and load paths are restricted to surface-based structures, rather than fully 3D load paths. By specifically planning deposition paths (roads) where the composite reinforcement is aligned to the load paths within a structure, there is an opportunity for a step-change in the mechanical properties of ME parts. To achieve this goal for arbitrary geometries and load paths, the author presents a design and process planning workflow that concurrently optimizes the topology of the part and the toolpath used to fabricate it. The workflow i) identifies the optimal structure and road directions using topology optimization (TO), ii) plans roads aligned to those optimal directions, iii) orders those roads for collision-free deposition, and iv) translates that ordered set of roads to a robot-interpretable toolpath. A TO algorithm, capable of optimizing 3D material orientations, is presented and demonstrated in the context of 2D and 3D load cases. The algorithm achieved a 38% improvement in final solution compliance for a 3D Wheel problem relative to existing TO algorithms with planar orientation optimization considerations. Optimized geometries and their associated orientation fields were then propagated with the presented alignment-focused deposition path planner and conventional toolpath planners. The presented method resulted in a 97% correlation between the road directions and the orientation field, while the conventional methods only achieved 77%. A planar multi-load case was then fabricated using each of these methods and tested in both tension and bending; the presented alignment-focused method resulted in a 108.24% and 29.25% improvement in each load case, respectively. To evaluate the workflow in a multi-axis context, an inverted Wheel problem was optimized and processed by the workflow. The resulting toolpaths were then fabricated on a multi-axis deposition platform and mechanically evaluated relative to geometrically similar structures using a conventional toolpath planner. While the alignment in the multi-axis specimen was improved from the conventional method, the mechanical properties were reduced due to limitations of the multi-axis deposition platform. / Doctor of Philosophy / The material extrusion additive manufacturing process is widely used by hobbyists and industry professionals to produce demonstration parts, but the process is often overlooked for end-use, load bearing parts. This is due to the layer-by-layer fabrication method used to create the desired geometries; the bonding between layers is weaker than the direction material is deposited. If load paths acting on the printed structure travel across those layer interfaces, the part performance will decrease. Whereas gantry-based systems are forced into this layer-by-layer strategy, robotic arms allow the deposition head to rotate, which enables depositions to be placed outside of the XY-plane (i.e., the typical layer). If depositions are appropriately planned using this flexibility, the layer interfaces can be oriented away from the load paths such that all of the load acts on the (stronger) depositions. Although this benefit has been demonstrated in literature, the existing methods for planning robotic toolpaths have limits on printability; certain load paths and geometries cannot be printed due to concerns that the robotic system will collide with the part being printed. This work focuses on increasing the generality of these toolpath planning methods by enabling any geometry and set of load paths to be printed. This is achieved through three objectives: i) identify the load paths within the structure, ii) plan roads aligned to those load paths, iii) order those roads such that collisions will not occur. The author presents and evaluates a design workflow that addresses each of these three objectives by simultaneously optimizing the geometry of the part as well as the toolpath used to fabricate it. Planar and 3D load cases are optimized, processed using the presented workflow, and then fabricated on a multi-axis deposition platform. The resulting specimens are then mechanically tested and compared to specimens fabricated using conventional toolpath planning methods.