The objective of this study is to investigate the influences of the shape of grinding wheel to the teeth profile and the contact pattern of Curvic couplings. Firstly, the equations of concave and convex teeth profiles are successfully derived by using coordinate transformation technique. Then, assembly errors and shape parameters errors of the grinding wheel are introduced, and the error-involved teeth profiles equations are also obtained. Finally, the optimization technique is employed to find the optimized parameters of the grinding wheel such that Curvic couplings with prescribed proper mating conditions can be manufactured. The influences of the each parameter of grinding wheel with respect to the associated contact condition are also analyzed. The radii of grinding wheels for both concave and convex teeth, and the center distance between the grinding wheel and coupling blank are identified to significantly influence the contact area. A computer program for obtaining the optimum profile of grinding wheel involved error settings has been developed by using Visual Basic language.

Research was performed to optimize high-efficiency jet ejector geometry (Holtzapple, 2001) by varying nozzle diameter ratios from 0.03 to 0.21, and motive velocities from Mach 0.39 to 1.97. The high-efficiency jet ejector was simulated by Fluent Computational Fluid Dynamics (CFD) software. A conventional finite-volume scheme was utilized to solve two-dimensional transport equations with the standard k-?? turbulence model (Kim et. al., 1999). In this study of a constant-area jet ejector, all parameters were expressed in dimensionless terms. The objective of this study was to investigate the optimum length, throat diameter, nozzle position, and inlet curvature of the convergence section. Also, the optimum compression ratio and efficiency were determined. By comparing simulation results to an experiment, CFD modeling has shown high-quality results. The overall deviation was 8.19%, thus confirming the model accuracy. Dimensionless analysis was performed to make the research results applicable to any fluid, operating pressure, and geometric scale. A multi-stage jet ejector system with a total 1.2 compression ratio was analyzed to present how the research results may be used to solve an actual design problem. The results from the optimization study indicate that the jet ejector efficiency improves significantly compared to a conventional jet-ejector design. In cases with a subsonic motive velocity, the efficiency of the jet ejector is greater than 90%. A high compression ratio can be achieved with a large nozzle diameter ratio. Dimensionless group analysis reveals that the research results are valid for any fluid, operating pressure, and geometric scale for a given motive-stream Mach number and Reynolds ratio between the motive and propelled streams. For a given Reynolds ratio and motivestream Mach number, the dimensionless outlet pressure and throat pressure are expressed as Cp and Cpm, respectively. A multi-stage jet ejector system with a total 1.2 compression ratio was analyzed based on the optimization results. The result indicates that the system requires a lot of high-pressure motive steam, which is uneconomic. A high-efficiency jet ejector with mixing vanes is proposed to reduce the motive-steam consumption and is recommended for further study.

The objective of this research is to develop a flexible design grammar and genetic algorithm representation to be used in a multi-objective optimization method to design efficient steel roof trusses given space dimensions and loading requirements by the user. The goal of implementing the method as a multi-objective problem is to obtain a set of near-optimal trusses for the defined unstructured problem domain, not just a single near-optimal design. The method developed was required to support the exploration of a broad range of conceptual designs before making design decisions. Therefore, a method was developed that could define numerous design variables, support techniques to locate global or near-global optimal designs, and improve the efficiency of the computational procedures implemented. This research effort was motivated by the need to consider structural designs that may be beyond the established conventions of designers in the search for cost-efficient, structurally-sound designs. An effective design grammar that is capable of generating stable trusses is defined in this research. The design grammar supports the optimization of member size, in addition to truss geometry and topology. Multi-objective genetic algorithms were used to evolve sets of Pareto-optimal trusses that had varying topology, geometry, and member sizes. The Pareto-optimal curves provided design engineers with a range of near-optimal design alternatives that showed the tradeoffs that occur in meeting the stated objectives. Designers can select their final design from this set based on their own individual weighting of the design objectives. Trials are performed using a multiobjective genetic algorithm that works with the design grammar to evolve trusses for different span lengths. In addition to evaluate the performance of the developed optimization method further, trials were performed on a benchmark truss problem domain and the results obtained were compared with results obtained by other researchers. The results of the performance evaluation trials for the proposed method, in which the sizing, shape and topology were simultaneously performed, indicated that the method was effective in evolving a variety of truss topologies compared to previous published results, which evolved from a ground structure. The diverse topologies, however, were obtained over several trials instead of being found in a Pareto-optimal set found by a single trial. In addition, the proposed method was not able to locally optimize the member section sizes. Additional trials were performed to determine the benefit of applying local optimization to the member section sizes for a given truss topology or geometry provided by the method. The results indicate that significant weight reduction could be achieved by performing local optimization to the truss designs obtained by the proposed multi-objective optimization method.

Automated systems for large-span roof truss optimization provide engineers with the flexibility to consider multiple alternatives during conceptual design. This investigation extends previous work on multi-objective roof truss optimization to include the design preferences of a human user. The incorporation of user preferences into the optimization process required creation of a mechanism to identify and model preferences as well as discovery of an appropriate location within the algorithm for preference application. The first stage of this investigation developed a characteristic feature vector to describe the physical appearance of an individual truss. The feature vector translates visual elements of a truss into quantifiable properties transparent to the computer algorithm. The nine elements in the feature vector were selected from an assortment of geometrical and behavioral factors and describe truss simplicity, general shape, and chord shape. Using individual feature vectors, a truss population may be divided into groups of similar design. Partitioning the population simplifies the feedback process by allowing users to identify groups that best suit their design preferences. Several unsupervised clustering mechanisms were evaluated for their ability to generate truss classifications that matched human judgment and minimized intra-group deviation. A one-dimensional Kohonen self-organizing map was selected. The characteristic feature vectors of truss designs within user-selected groups provided a basis for determining whether or not a user would like a new design. After analyzing user inputs, prediction algorithm trials sought to reproduce these inputs and apply them to the prediction of acceptable designs. This investigation developed a hybrid method combining rough set reduct techniques and a back-propagation neural network. This hybrid prediction mechanism was embedded into the operations of an Implicit Redundant Representation Genetic Algorithm. Locations within the ranking and selection processes of this algorithm formed the basis of a study to investigate the effect of user preference on truss optimization. Final results for this investigation prove that incorporating a user's aesthetic design preferences into the optimization project generates more design alternatives for the user to examine; that these alternatives are more in line with a user's conceptual perception of the project; and that these alternatives remain structurally optimal.

This Record of Study contains the details of an optimization model developed for Shell Oil Co. This model will be used during the strategic design process of a supply chain for a new technology commercialization. Unlike traditional supply chain deterministic optimization, this model incorporates different levels of uncertainty at suppliers' nominal capacity. Because of the presence of uncertainty at the supply stage, the objective of this model is to define the best diversification and safety stock level allocated to each supplier, which minimize the total expected supply chain cost. We propose a Monte Carlo approach for scenario generation, a two-stage non-linear formulation and the Sample Average Approximation (SAA) procedure to solve the problem near optimality. We also propose a simple heuristic procedure to avoid the nonlinearity issue. The sampling and heuristic optimization procedures were implemented in a spreadsheet with a user's interface. The main result of this development is the analysis of the impact of diversification in strategic sourcing decisions, in the presence of stochastic supply disruptions.

<p>Methods for optimal design of different means of control aredeveloped in this thesis. The main purpose is to maintain thelaminar flow on wings at a chord Reynolds number beyond what isusually transitional or turbulent. Linear stability analysis isused to compute the exponential amplification of infinitesimaldisturbances, which can be used to predict the location oflaminar-turbulent transition. The controls are computed usinggradient-based optimization techniques where the aim is tominimize an objective function based upon, or related to, thedisturbance growth. The gradients of the objective functionswith respect to the controls are evaluated from the solutionsof adjoint equations.</p><p>Sensitivity analysis using the gradients of the disturbancekinetic energy with respect to different periodic forcing showwhere and by what means control is most efficiently made. Theresults are presented for flat plate boundary layer flows withdifferent free stream Mach numbers.</p><p>A method to compute optimal steady suction distributions tominimize the disturbance kinetic energy is presented for bothincompressible and compressible boundary layer flows. It isshown how to formulate an objective function in order tominimize simultaneously different types of disturbances whichmight exist in two, and three-dimensional boundary layer flows.The problem formulation also includes control by means ofrealistic pressure chambers, and results are presented wherethe method is applied on a swept wing designed for commercialaircraft.</p><p>Optimal temperature distributions for disturbance controlare presented for flat plate boundary layer flows. It is shownthat the efficiency of the control depends both on the freestream Mach number, and whether the wall downstream of thecontrol domain is insulated, or heat transfer occurs.</p><p>Shape optimization is presented with the aim of reducing theaerodynamic drag, while maintaining operational properties.Results of optimized airfoils are presented for cases whereboth the disturbance kinetic energy, and wave drag are reducedsimultaneously while lift, and pitch-moment coefficients aswell as the volume are kept at desired values.</p><p><b>Keywords:</b>fluid mechanics, laminar-turbulent transition,boundary layer, laminar flow control, natural laminar flow,adjoint equations, optimal control, objective function, PSE,APSE, ABLE, HLFC, eN-method, Euler equations</p>

Thesis (Ph. D.)--Lehigh University, 2000. / Includes vita. Includes bibliographical references (leaves 108-114).

Thesis (Ph. D.)--University of Florida, 2005. / Title from title page of source document. Document formatted into pages; contains 105 pages. Includes vita. Includes bibliographical references.

Thesis (Ph. D.)--Rutgers University, 2008. / "Graduate Program in Operations Research." Includes bibliographical references (p. 419-435).

Thesis (Honors)--College of William and Mary, 2009. / Includes bibliographical references (leaf 37). Also available via the World Wide Web.