This research contributes to the development of a structural optimization software system designed to support design optimization. The focus of this thesis work is on formulating strategies to obtain accurate solutions and enhance the efficiency of the optimization process, particularly when dealing with large and complex finite element (FE) models, utilizing statistical concepts. A potential avenue explored in this study is the adaptive response surface optimization process. The adaptive response surface optimization method involves the adaptive control of samples selected through the design of experiments and empirical models constructed via the response surface methodology, with the sampling of the design space and empirical model terms dynamically adjusted throughout the optimization progression. The empirical models are constructed with statistically significant terms to maximize the utilization of information from each sample generated using the design of experiments. If the available information is fully utilized by the empirical model and the adaptive response surface optimization process needs to progress further until an optimal solution is identified, additional samples are generated. The methodology is applied to a benchmark bell crank problem, optimizing the bell crank for maximum operational value by simultaneously increasing fatigue life and reducing the overall component cost. This demonstration showcases the structural optimization software's capability to handle both design and manufacturing aspects seamlessly. The approach to solving the structural optimization problem involves constructing a constrained parametric bell crank part in Abaqus/CAE as it facilitates easy manipulation of the geometry. The entire process of geometry generation, meshing, simulation, and output extraction was supported by developing Python scripts. Response surface model building and other statistical analyses are conducted using the JMP statistical software. Nonlinear constrained optimization is executed through the sequential quadratic programming (SLSQP solver) from the SciPy library, allowing optimization on the response surfaces representing the objective function and constraints to identify the optimal solution. The optimal solution is obtained utilizing a small composite design with individual response surface models for the objective function and each constraint, is compared with results from the Abaqus finite element model, and the percentage difference was 0.9% at the optimal design variable values. / Master of Science / Optimization processes, in general, require multiple iterations to converge to the optimal solution. Structural optimization, dealing with large and complex computationally intensive models are typically very time-consuming. To address this challenge, approximations of the actual design space, called response surfaces, are created using the statistical concept known as response surface methodology. Response surfaces are developed by selecting specific regions within the design space and studying them using complex computational models. The results obtained from these computational models are combined with statistical tools to build a response surface that approximately represents the actual design objective function and the associated constraints of the design within the specified design space. In this research, an adaptive approach called adaptive response surface optimization is implemented. In this approach, the regions studied and the response surfaces are dynamically adjusted based on the progression of the optimization process. Such adaptability significantly accelerates the structural optimization process and yields successful results. To illustrate this method, a benchmark problem was solved using the finite element solver Abaqus, the statistical software JMP, and the optimization toolbox from the Scipy library.

The control of pest populations by using insect pathogens has been an attractive alternative to the application of chemical pesticides employed for the same purpose. As these chemicals not only damage the environment, but also trigger development of resistance by the pests and can harm other organisms together with the target pest, biological control is preferable and Bacillus thuringiensis (Bt) subspecies have been the most widely used bioinsecticides in forestry, agriculture and mosquito/ black fly control. The most important property of Bt subspecies is the synthesis of protoxins named as delta-endotoxins (crystal proteins). In this study, response surface optimization of Bt subsp. israelensis HD500 batch fermentation for high level production of its toxin proteins Cry4Ba and Cry11Aa was performed. As the interaction of the medium components as well as cultivation conditions are expected to influence the production of the toxin proteins, an experimental chart was prepared by accepting the previously reported optimal values for the most important parameters as zero points: [Mn], 10-6 M / [K2HPO4], 50 mM / C:N ratio, 20:1 and incubation temperature / 30&deg / C. When the combinations of these variables at different levels were studied at 30 batch cultures and analysed for the optimum toxin protein concentrations, temperature: 28.3&amp / #61616 / C, [Mn]: 3.3x10-7M, C:N ratio: 22.2 and [K2HPO4]: 66.1mM yielded the highest concentrations of both Cry4Ba and Cry11Aa toxin proteins.

ABSTRACT PARAMETER OPTIMIZATION OF CHEMICALLY ACTIVATED MORTARS CONTAINING HIGH VOLUMES OF POZZOLAN BY STATISTICAL DESIGN AND ANALYSIS OF EXPERIMENTS Aldemir, BaSak M.S., Department of Industrial Engineering Supervisor: Prof. Dr. &Ouml / mer Saat&ccedil / ioglu Co-Supervisor: Assoc. Prof. Dr. Lutfullah Turanli January 2006, 167 pages This thesis illustrates parameter optimization of early and late compressive strengths of chemically activated mortars containing high volumes of pozzolan by statistical design and analysis of experiments. Four dominant parameters in chemical activation of natural pozzolans are chosen for the research, which are natural pozzolan replacement, amount of pozzolan passing 45 &amp / #956 / m sieve, activator dosage and activator type. Response surface methodology has been employed in statistical design and analysis of experiments. Based on various second-order response surface designs / experimental data has been collected, best regression models have been chosen and optimized. In addition to the optimization of early and late strength responses separately, simultaneous optimization of compressive strength with several other responses such as cost, and standard deviation estimate has also been performed. Research highlight is the uniqueness of the statistical optimization approach to chemical activation of natural pozzolans.

Furthering our understanding of the physics of flapping flight has the potential to benefit the field of micro air vehicles. Advancements in micro air vehicles can benefit applications such as surveillance, reconnaissance, and search and rescue. In this research, flapping kinematics of a ladybug was explored using a direct linear transformation. A flapping mechanism design is presented that was capable of executing ladybug or other species-specific kinematics. The mechanism was based on a differential gear design, had two wings, and could flap in harsh environments. This mechanism served as a test bed for force analysis and optimization studies. The first study was based on a Box-Behnken screening design to explore wing kinematic parameter design space and manually search in the direction of flapping kinematics that optimized the objective of maximum combined lift and thrust. The second study used a Box-Behnken screening design to build a response surface. Using gradient-based techniques, this surface was optimized for maximum combined lift and thrust. Box-Behnken design coupled with response surface methodology was an efficient method for exploring the mechanism force response. Both methods for optimization were capable of successfully improving lift and thrust force outputs. The incorporation of the results of these studies will aid in the design of more efficient micro air vehicles and with the ultimate goal of leading to a better understanding of flapping wing aerodynamics and the development of aerodynamic models.

Though still in relative infancy, the field of flapping flight has potential to have a far-reaching impact on human life. Nature presents a myriad of examples of successful uses of this locomotion. Human efforts in flapping flight have seen substantial improvement in recent times. Wing kinematics are a key aspect of this study. This study summarizes previous wing trajectory generators and presents a new trajectory generation method built upon previous methods. This includes a novel means of commanding unequal half-stroke durations subject to robotic trajectory continuity requirements. Additionally, previous optimization methods are improved upon. Experimental optimization is performed using the new trajectory generation method and a more traditional means. Methods for quantifying and compensating for sensor time-dependence are also discussed. Results show that the Polar Fourier Series trajectory generator advanced rapidly through the optimization process, especially during the initial phase of experimentation. The Modified Berman and Wang trajectory generator moved through the design space more slowly due to the increased number of kinematic parameters. When optimizing lift only, the trajectory generators produced similar results and kinematic forms. The findings suggest that the objective statement should be modified to reward efficiency while maintaining a certain amount of lift. It is expected that the difference between the capabilities of the two trajectory generators will become more apparent under such conditions.