A series of novel algorithms for performing aerostructural shape and topology optimization are introduced and applied to the design of aircraft wings. An isoparametric level set method is developed for performing topology optimization of wings and other non-rectangular structures that must be modeled using a non-uniform, body-fitted mesh. The shape sensitivities are mapped to computational space using the transformation defined by the Jacobian of the isoparametric finite elements. The mapped sensitivities are then passed to the Hamilton-Jacobi equation, which is solved on a uniform Cartesian grid. The method is derived for several objective functions including mass, compliance, and global von Mises stress. The results are compared with SIMP results for several two-dimensional benchmark problems. The method is also demonstrated on a three-dimensional wingbox structure subject to fixed loading. It is shown that the isoparametric level set method is competitive with the SIMP method in terms of the final objective value as well as computation time. In a separate problem, the SIMP formulation is used to optimize the structural topology of a wingbox as part of a larger MDO framework. Here, topology optimization is combined with aerodynamic shape optimization, using a monolithic MDO architecture that includes aerostructural coupling. The aerodynamic loads are modeled using a threedimensional panel method, and the structural analysis makes use of linear, isoparametric, hexahedral elements. The aerodynamic shape is parameterized via a set of twist variables representing the jig twist angle at equally spaced locations along the span of the wing. The sensitivities are determined analytically using a coupled adjoint method. The wing is optimized for minimum drag subject to a compliance constraint taken from a 2g maneuver condition. The results from the MDO algorithm are compared with those of a sequential optimization procedure in order to quantify the benefits of the MDO approach. While the sequentially optimized wing exhibits a nearly-elliptical lift distribution, the MDO design seeks to push a greater portion of the load toward the root, thus reducing the structural deflection, and allowing for a lighter structure. By exploiting this trade-off, the MDO design achieves a 42% lower drag than the sequential result.

A series of novel algorithms for performing aerostructural shape and topology optimization are introduced and applied to the design of aircraft wings. An isoparametric level set method is developed for performing topology optimization of wings and other non-rectangular structures that must be modeled using a non-uniform, body-fitted mesh. The shape sensitivities are mapped to computational space using the transformation defined by the Jacobian of the isoparametric finite elements. The mapped sensitivities are then passed to the Hamilton-Jacobi equation, which is solved on a uniform Cartesian grid. The method is derived for several objective functions including mass, compliance, and global von Mises stress. The results are compared with SIMP results for several two-dimensional benchmark problems. The method is also demonstrated on a three-dimensional wingbox structure subject to fixed loading. It is shown that the isoparametric level set method is competitive with the SIMP method in terms of the final objective value as well as computation time. In a separate problem, the SIMP formulation is used to optimize the structural topology of a wingbox as part of a larger MDO framework. Here, topology optimization is combined with aerodynamic shape optimization, using a monolithic MDO architecture that includes aerostructural coupling. The aerodynamic loads are modeled using a threedimensional panel method, and the structural analysis makes use of linear, isoparametric, hexahedral elements. The aerodynamic shape is parameterized via a set of twist variables representing the jig twist angle at equally spaced locations along the span of the wing. The sensitivities are determined analytically using a coupled adjoint method. The wing is optimized for minimum drag subject to a compliance constraint taken from a 2g maneuver condition. The results from the MDO algorithm are compared with those of a sequential optimization procedure in order to quantify the benefits of the MDO approach. While the sequentially optimized wing exhibits a nearly-elliptical lift distribution, the MDO design seeks to push a greater portion of the load toward the root, thus reducing the structural deflection, and allowing for a lighter structure. By exploiting this trade-off, the MDO design achieves a 42% lower drag than the sequential result.

Thesis (M.A. Sc.)--University of Toronto, 2007. / Source: Masters Abstracts International, Volume: 45-06, page: 3197.

Thesis (Ph.D.)--Aerospace Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Hodges, Dewey H.; Committee Member: Bauchau, Olivier A.; Committee Member: Johnson, Ellis; Committee Member: Makeev, Andrew; Committee Member: Volovoi, Vitali V.

Thesis (M.S.)--Brigham Young University. Dept. of Mechanical Engineering, 2007. / Includes bibliographical references (p. 65-67).

Genetic Algorithms (GAs) are powerful tools to solve large scale design optimization problems. The research interests in GAs lie in both its theory and application. On one hand, various modifications have been made on early GAs to allow them to solve problems faster, more accurately and more reliably. On the other hand, GA is used to solve complicated design optimization problems in different applications. The study in this thesis is both theoretical and applied in nature. On the theoretical side, an improved GA�Evolution Direction Guided GA (EDG-GA) is proposed based on the analysis of Schema Theory and Building Block Hypothesis. In addition, a method is developed to study the structure of GA solution space by characterizing interactions between genes. This method is further used to determine crossover points for selective crossover. On the application side, GA is applied to generate optimal tolerance assignment plans for a series of manufacturing processes. It is shown that the optimal tolerance assignment plan achieved by GA is better than that achieved by other optimization methods such as sensitivity analysis, given comparable computation time.

Requirement verification and validation is a critical component of building and delivering space vehicles with testing as the preferred method. This Master’s Project presents the space vehicle test process from planning through test design and execution. It starts with an overview of the requirements, validation, and verification. The four different verification methods are explained including examples as to what can go wrong if the verification is done incorrectly. Since the focus of this project is on test, test verification is emphasized. The philosophy behind testing, including the “why” and the methods, is presented. The different levels of testing, the test objectives, and the typical tests are discussed in detail. Descriptions of the different types of tests are provided including configurations and test challenges. While most individuals focus on hardware only, software is an integral part of any space product. As such, software testing, including mistakes and examples, is also presented. Since testing is often not performed flawlessly the first time, sections on anomalies, including determining root cause, corrective action, and retest is included. A brief discussion of defect detection in test is presented. The project is actually presented in total in the Appendix as a Power Point document.

This report discusses one application of a flight software design for a spacecraft in which the software executes from a database that can be managed by systems engineering. This report gives an overview of how such a software design can be developed and implemented. It also discusses why this approach is beneficial to the systems engineering program.

Supply chain management is the set of tasks through which businesses acquire, process, and move raw materials and final products from suppliers through factories and distribution points to customers. The mathematical problems encountered in supply chain optimization models are difficult to solve. Free Trade Agreements can simplify the models of inter-company trade between countries. Another way to make these models more tractable is to decompose the complete supply chain into a set of small, manageable units representing businesses or business processes and optimize the system by controlling the interactions between these units. We illustrate such a model and optimize it with genetic-algorithm-controlled Multidisciplinary Design Optimization

Supply chain management is the set of tasks through which businesses acquire, process, and move raw materials and final products from suppliers through factories and distribution points to customers. The mathematical problems encountered in supply chain optimization models are difficult to solve. Free Trade Agreements can simplify the models of inter-company trade between countries. Another way to make these models more tractable is to decompose the complete supply chain into a set of small, manageable units representing businesses or business processes and optimize the system by controlling the interactions between these units. We illustrate such a model and optimize it with genetic-algorithm-controlled Multidisciplinary Design Optimization