The aim of this master thesis is optimization of the rotor in the terms of efficiency. An analysis of the effects of rotor parameters is performed by RMXPRT. A genetic algorithm is created to find the optimal solution. Solutions are evaluated and adjusted according to the results. Measurements on the actual machine are performed and evaluated to verify the functionality of the simulation models.

Genetic algorithm has been widely used in different areas of optimization problems. Ithas been combined with renewable energy domain, photovoltaic system, in this thesis.To participate and win the solar boat race, a control program is needed and C++ hasbeen chosen for programming. To implement the program, the mathematic model hasbeen built. Besides, the approaches to calculate the boundaries related to conditionhave been explained. Afterward, the processing of the prediction and real time controlfunction are offered. The program has been simulated and the results proved thatgenetic algorithm is helpful to get the good results but it does not improve the resultstoo much since the particularity of the solar driven boat project such as the limitationof energy production

The realization of planning, design, construction, operation and maintenance of water supply systems pictures one of the largest infrastructure projects of municipalities / water distribution networks should be designed very meticulously. Genetic algorithm is an optimization method that is based on natural evolution and is used for the optimization of water distribution networks. Genetic algorithm is comprised of operators and the operators affect the performance of the algorithm. Although these operators are related with parameters, not much attention has been given for the determination of these parameters for this specific field of water distribution networks. This study represents a novel methodology, which investigates the parameters of the algorithm for different networks. The developed computer program is applied to three networks. Two of these networks are well known examples from the literature / the third network is a pressure zone of Ankara water distribution network. It is found out that, the parameters of the algorithm are related with the network, the case to be optimized and the developed computer program. The pressure penalty constant value varied depending on the pipe costs and the network characteristics. The mutation rate is found to vary in a range of [0.0075 &ndash / 0.0675] for three networks. Elitism rate is determined as the minimum value for the corresponding population size. Crossover probability is found to vary in a range of [0.5 &ndash / 0.9]. The methodology should be applied to determine the appropriate parameter set of genetic algorithm for each optimization study. Using the method described, fairly well results are obtained.

Application of optimization techniques for determining the optimal operating policy for reservoirs is a major title in water resources planning and management. Genetic algorithms, ruled by evolution techniques, have become popular for solving optimization problems in diversified fields of science. The main aim of this research was to explore the efficiency and effectiveness of the applicability of genetic algorithm in optimization of multi-reservoirs. A computer code has been constructed for this purpose and verified by means of a reference problem with a known global optimum. Three reservoirs in the Colorado River Storage Project were optimized for maximization of energy production. Besides, a real-time approach utilizing a blend of online and a posteriori data was proposed. The results achieved were compared to the real operational data and genetic algorithms were found to be effective, competitive and can be utilized as an alternative technique to other traditional optimization techniques.

An aerodynamic design and optimization tool for wind turbines is developed by using both Blade Element Momentum (BEM) Theory and Genetic Algorithm. Turbine blades are optimized for the maximum power production for a given wind speed, a rotational speed, a number of blades and a blade radius. The optimization variables are taken as a fixed number of sectional airfoil profiles, chord lengths, and twist angles along the blade span. The airfoil profiles and their aerodynamic data are taken from an airfoil database for which experimental lift and drag coefficient data are available. The BEM analysis tool developed is first validated with the experimental data for low wind speeds. A 100 kW wind turbine, which is used in the validation, is then optimized. As a result of the optimization, the power production is improved by 40 to 80 percent. The optimization methodology is then employed to design a 1MW wind turbine with a 25m radius.

In this thesis work, minimization of platform-based coupling between the antennas of two VHF radios on an aircraft platform and two HF radios on a ship platform is aimed. For this purpose / an optimal antenna placement, which yields minimum average coupling between the antennas over the whole frequency band of operation is determined for each platform. Two important global optimization techniques, namely Genetic Algorithm Optimization and Particle Swarm Optimization, are used in determination of these optimal antenna placements. Aircraft &amp / ship platforms and antennas placed on them are modeled based on their real electrical and physical properties in CST &ndash / MWS (Microwave Studio) simulation tool. For each platform, antenna placements and coupling results determined by two different optimization techniques and performances of these optimization techniques are compared with each other. At the end of this thesis work / for each platform, far-field radiation pattern performances of the antennas at their optimal places are analyzed in terms of directivity and coverage.

The AOE Accumulated Ocean Energy Inc. (AOE) wave energy converter (WEC) is a cascaded pneumatic system, in which air is successively compressed through three point absorber devices on the way to shore; this air is then used to drive an electricity generator. To better quantify the performance of this device, this thesis presents a dynamically coupled model architecture of the AOE WEC, which was developed using the finite element solver ProteusDS and MATLAB/Simulink. This model is subsequently applied for the development and implementation of control in the AOE WEC. At each control stage, comprehensive power matrix data is generated to assess power production as a function of control complexity. The nature of the AOE WEC presented a series of novel challenges, centered on the significant residency time of air within the power take-off (PTO). As a result, control implementation was broken into two stages: passive and active control. The first stage, passive control, was realized as an optimization of eight critical PTO parameters with the objective of maximizing exergy output. After only 15 generations, the genetic algorithm optimization led to an increase of 330.4% over an initial, informed estimate of the optimal design, such that the annually-averaged power output was 29.37 kW. However, a disparity in power production between low and moderate energy sea-states was identified, which informed the development of an active control strategy for the increase of power production in low energy sea-states. To this aim, a recirculation-based control strategy was developed, in which three accumulator tanks were used to selectively pressurize and de-pressurize the piston at opportune times, thereby increasing the continuity of air throughput. Under the influence of active control, sea-states with significant wave heights between 0.75 m – 1.75 m, which on average encompass 55.93% of the year at the Amphitrite Bank deployment location, saw a 16.3% increase in energy production. / Graduate / 2018-08-18

This study gives a description about the development of a computer model, RealPipe, which relates genetic algorithm (GA) to the well known problem of least-cost design of water distribution network. GA methodology is an evolutionary process, basically imitating evolution process of nature. GA is essentially an efficient search method basically for nonlinear optimization cases. The genetic operations take place within the population of chromosomes. By means of various operators, the genetic knowledge in chromosomes change continuously and the success of the population progressively increases as a result of these operations. GA optimization is also well suited for optimization of water distribution systems, especially large and complex systems. The primary objective of this study is optimization of a water distribution network by GA. GA operations are realized on a special program developed by the author called RealPipe. RealPipe optimizes given water network distribution systems by considering capital cost of pipes only. Five operators are involved in the program algorithm. These operators are generation, selection, elitism, crossover and mutation. Optimum population size is found to be between 30-70 depending on the size of the network (i.e. pipe number) and number of commercially available pipe size. Elitism rate should be around 10 percent. Mutation rate should be selected around 1-5 percent depending again on the size of the network. Multipoint crossover and higher rates are advisable. Also pressure penalty parameters are found to be much important than velocity parameters. Below pressure penalty parameter is the most important one and should be roughly 100 times higher than the other. Two known networks of the literature are examined using RealPipe and expected results are achieved. N8.3 network which is located in the northern side of Ankara is the case study. Total cost achieved by RealPipe is 16.74 percent lower than the cost of the existing network / it should be noted that the solution provided by RealPipe is hydraulically improved.

Tato práce se zabývá možností paralelizace Genetického Algoritmu a jeho ná-sledné evaluace pomocí testovacích účelových funkcí. První část je teoretická a shrnuje základní poznatky z oblasti Genetických Algoritmů, paralelních archi-tektur, paralelních výpočtů a optimalizace. A dále je tato část doplněna o mož-nosti paralelizace Genetického Algoritmu. V následující praktické části je rozebrán algoritmus paralelního Genetického Algoritmu, jenž je použitý při experimentu a také je diskutována struktura a účel zvoleného experimentu. Následně jsou diskutovány výsledky získané z běhu experimentu na Eridani Clusteru z pohledu zrychlení výpočtu, kvality nalezeného řešení a závislosti kvality řešení na migračním schématu.

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