Indiana University-Purdue University Indianapolis (IUPUI) / This thesis focuses on optimization of wind power tracking control systems in order to capture maximum wind power for the generation system. In this work, a mathematical simulation model is developed for a variable speed wind turbine power generation system. The system consists a wind turbine with necessary transmission system, and a permanent magnet synchronous generator and its vector control system. A new fuzzy based hill climbing method for power tracking control is proposed and implemented to optimize the wind power for the system under various conditions. Two existing power tracking control methods, the tip speed ratio (TSR) control method and the speed sensorless control method are also implemented with the wind power system. The computer simulations with a 5 KW wind power generation system are performed. The results from the proposed control method are compared with those obtained using the two existing methods. It is illustrated that the proposed method generally outperforms the two existing methods, especially when the operating point is far away from the maximum point. The proposed control method also has similar stable characteristic when the operating point is close to the peak point in comparison with the existing methods. The proposed fuzzy control method is computationally efficient and can be easily implemented in real-time.

Indiana University-Purdue University Indianapolis (IUPUI) / Our ability to engineer materials is limited by our capacity to tailor the material’s microstructure morphology and predict resulting properties. The insufficient knowledge on microstructure-property relationship is due to complexity and randomness in all materials at different scales. The objective of this research is to establish a design optimization methodology for microstructured materials. The material design problem is stated as finding the optimum microstructure to maximize the desired performance satisfying material processing constrains. This problem has been solved in this thesis by means of numerical techniques through four main steps: microstructure characterization, model reconstruction, property evaluation, and optimization. Two methods of microstructure characterizations have been investigated along with the advantages and disadvantages of each method. The first microstructure characterization method is a statistical method which utilizes correlation functions to extract the microstructural information. Algorithms for calculating these correlations functions have been developed and optimized based on their computational cost using MATLAB software. The second microstructure characterization method is physical characterization which works based on evaluation of physical features in microstructured domain. These features have been measured by means of MATLAB codes. Three model reconstruction techniques are proposed based on these characterization methods and employed to generate material models for further evaluation. The first reconstructing algorithm uses statistical functions to reconstruct the statistical equivalent model through simulating annealing optimization method. The second algorithm uses cellular automaton concepts to simulate the grain growth utilizing physical descriptors, and the third one generates elliptical inclusions in a material matrix using physical characteristic of microstructure. The finite element method is used to analysis the mechanical behavior of material models. Several material samples with different microstructural characteristics have been generated to model the micro-scale design domain of AZ31 magnesium alloy and magnesium matrix composite with silicon carbide fibers. Then, surrogate models have been created based on these samples to approximate the entire design domain and demonstrate the sensitivity of the desired mechanical property to two independent microstructural features. Finally, the optimum microstructure characteristics of material samples for fracture strength maximization have been obtained.