With continuously increasing urbanization, consumer demands and expansion of water supply systems, determination of efficient pump schedules became a more difficult task. Pumping energy costs constitute a significant part of the operational cost of the water distribution networks. This study aims to provide an effective daily pump schedule by minimizing the energy costs for constant and also for multi tariff of electricity (3 Kademeli Elektrik Tarifesi) in water distribution network. A case study has been performed in an area covering N8.3 and N7 pressure zones which are parts of Ankara water distribution network. Both pressure zones consists of 3 multiple pumps in pump station and one tank having 5000 m3 storage volume each. By using genetic algorithm based software (WaterCAD Darwin Scheduler) least-cost pump scheduling and operation policy for each pump station has been determined while satisfying target hydraulic performance requirements such as minimum and maximum service pressures, final water level of storage tank and maximum velocity in pipeline. 32 different alternative scenarios have been created which include multi tariff energy prices, constant tariff energy price, insulated system condition, uninsulated system condition and different pump combinations. The existing base scenario and alternative scenarios which were prepared by using optimal pump schedules have been compared and the achievements of optimizing pump operation have been analyzed. At the end of the study, a satisfying result has been observed that by using determined optimal pump schedule, minimum % 14 of total energy cost can be saved in existing water supply system.

This thesis presents an aerodynamic shape optimization methodology for rotor blades of horizontal axis wind turbines. Genetic Algorithm and Blade Element Momentum Theory are implemented in order to find maximum power production at a specific wind speed, rotor speed and rotor diameter. The potential flow solver, XFOIL, provides viscous aerodynamic data of the airfoils. Optimization variables are selected as the sectional chord length, the sectional twist and the blade profiles at root, mid and tip regions of the blade. The blade sections are defined by the NACA four digit airfoil series or arbitrary airfoil profiles defined by a Bezier curve. Firstly, validation studies are performed with the airfoils and the wind turbines having experimental data. Then, optimization studies are performed on the existing wind turbines. Finally, design optimization applications are carried out for a 1 MWwind turbine.

Submitted by Maike Costa (maiksebas@gmail.com) on 2016-03-22T11:33:37Z No. of bitstreams: 1 arquivototal.pdf: 1643585 bytes, checksum: 5ba2336704d1de91b41bbe323ef3781e (MD5) / Made available in DSpace on 2016-03-22T11:33:37Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 1643585 bytes, checksum: 5ba2336704d1de91b41bbe323ef3781e (MD5) Previous issue date: 2015-05-15 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / In this paper we discuss the problem of linear tting to experimental data using a method bio-inspired of optimization, i.e., it imitates the biological concepts attempt to nd optimal or suboptimal results. The method used is the genetic algorithm (GA), AG makes use of the theory of Darwinian evolution to nd the best route for the desired maximum point. Traditionally, the linear tting is made through the method of least squares. The method is e cient, but is di cult to justify the pre-calculus classes. Therefore, the alternative AG comes as a computationally exhaustive procedure, however easy justi cation for these classes. Thus, the purpose of this study is to compare the results of linear tting for some control scenarios using this methods and certify the quality of the adjustments obtained by the approximate method. At the end of the work it was found that the results are solid enough to justify the alternative method and the proposed use of this optimization process has the potential to spark interest in other areas of mathematics. / Neste trabalho abordaremos o problema de ajuste linear para dados experimentais através de um método de otimização bio-inspirado, isto é, que mimetiza conceitos biológicos na tentativa de buscar resultados ótimos ou sub-ótimos. O método utilizado é o algoritmo genético (AG), AG faz uso da teoria da evolução Darwiniana para buscar a melhor rota para o ponto de máximo desejado. Tradicionalmente, o ajuste linear é feito através do método de mínimos quadrados. Tal método é e ciente, porém é de difícil justi cativa para as turmas pré-cálculo. Diante disso, a alternativa do AG vem como um procedimento exaustivo computacionalmente, entretanto de fácil justi cativa para essas turmas. Assim, a proposta do trabalho é comparar os resultados de ajuste linear para alguns cenários de controle através dos dois métodos e certi car a qualidade dos ajustes obtidos pelo método aproximado. No nal do trabalho constatou-se que os resultados encontrados sÿo sólidos o bastante para justi car o método alternativo e que a proposta da utilização desse processo de otimização tem potencial para despertar interesse em outras áreas da matemática.

Genetic algorithms (GA) are stochastic global search methods based on the mechanics of natural biological evolution, proposed by John Holland in 1975. Here in this thesis, we have exploited possible utilities of Genetic Algorithm optimization in Nuclear Magnetic Resonance (NMR) experiments. We have performed (i ) Pulse sequence generation and optimization for NMR Quantum Information Processing, (ii ) efficient creation of NOON states, (iii ) Composite operator design and (iv ) delay optimization for refocused quantitative INEPT. We have generated time optimal as well as robust pulse sequences for popular quantum gates. A Matlab package is developed for basic Target unitary operator to pulse sequence optimization and is explained with an example. Chapter 1 contains a brief introduction to NMR, Quantum computation and Genetic algorithm optimization. Experimental unitary operator decomposition using Genetic Algorithm is explained in Chapter 2. Starting from a two spin homonu- clear system (5-Bromofuroic acid), we have generated hard pulse sequences for performing (i ) single qubit rotation, (ii ) controlled NOT gates and (iii ) pseudo pure state creation, which demonstrates universal quantum computation in such systems. The total length of the pulse sequence for the single qubit rotation of an angle π/2 is less than 500µs, whereas the conventional method (using a selective soft pulse) would need a 2ms shaped pulse. This substantial shortening in time can lead to a significant advantage in quantum circuits. We also demonstrate the creation of Long Lived Singlet State and other Bell states, directly from thermal equilibrium state, with the shortest known pulse sequence. All the pulse sequences generated here are generic i.e., independent of the system and the spectrometer. We further generalized this unitary operator decomposition technique for a variable operators termed as Fidelity Profile Optimization (FPO) (Chapter 3) and performed quantum simulations of Hamiltonian such as Heisenberg XY interaction and Dzyaloshinskii-Moriya interaction. Exact phase (φ) dependent experimental unitary decompositions of Controlled-φ and Controlled Controlled-φ are solved using first order FPO. Unitary operator decomposition for experimental quantum simulation of Dzyaloshinskii-Moriya interaction in the presence of Heisenberg XY interaction is solved using second order FPO for any relative strengths of interactions (γ) and evolution time (τ ). Experimental gate time for this decomposition is invariant under γ or τ , which can be used for relaxation independent studies of the system dynamics. Using these decompositions, we have experimentally verified the entanglement preservation mechanism suggested by Hou et al. [Annals of Physics, 327 292 (2012)]. NOON state or Schrodinger cat state is a maximally entangled N qubit state with superposition of all individual qubits being at |0 and being at |1 . NOON states have received much attention recently for their high precession phase measurements, which enables the design of high sensitivity sensors in optical interfer- ometry and NMR [Jones et al. Science, 324 1166(2009)]. We have used Genetic algorithm optimization for efficient creation of NOON states in NMR (Chapter 4). The decompositions are, (i ) a minimal in terms of required experimental resources – radio frequency pulses and delays – and have (ii ) good experimental fidelity. A composite pulse is a cluster of nearly connected rf pulses which emulate the effect of a simple spin operator with robust response over common experimental imperfections. Composite pulses are mainly used for improving broadband de- coupling, population inversion, coherence transfer and in nuclear overhauser effect experiments. Composite operator is a generalized idea where a basic operator (such as rotation or evolution of zz coupling) is made robust against common experimental errors (such as inhomogeneity / miscalibration of rf power or errror in evaluation of zz coupling strength) by using a sequence of basic operators available for the system. Using Genetic Algorithm optimization, we have designed and experimentally verified following composite operators, (i ) broadband rotation pulses, (ii ) rf inhomogeneity compensated rotation pulses and (iii ) zz evolution operator with robust response over a range of zz coupling strengths (Chapter 5). We also performed rf inhomogeneity compensated Controlled NOT gate. Extending Genetic Algorithm optimization in classical NMR applications, we have improved the quantitative refocused constant-time INEPT experiment (Q-INEPT- CT) of M¨kel¨ et al. [JMR 204(2010) 124-130] with various optimization constraints . The improved ‘average polarization transfer’ and ‘min-max difference’ of new delay sets effectively reduces the experimental time by a factor of two (compared with Q-INEPT-CT, M¨kel¨ et al.) without compromising on accuracy (Chapter 6). We also introduced a quantitative spectral editing technique based on average polarization transfer. These optimized quantitative experiments are also described in Chapter 6. Time optimal pulse sequences for popular quantum gates such as, (i ) Controlled Hadamard (C-H) gate, (ii ) Controlled-Controlled-NOT (CCNOT) Gate and (iii ) Controlled SWAP (C-S) gate are optimized using Genetic Algorithm (Appendix. A). We also generated optimal sequences for Quantum Counter circuits, Quantum Probability Splitter circuits and efficient creation of three spin W state. We have developed a Matlab package based on GA optimization for three spin target operator to pulse sequence generator. The package is named as UOD (Unitary Operator Decomposition) is explained with an example of Controlled SWAP gate in Appendix. B. An algorithm based on quantum phase estimation, which discriminates quantum states non-destructively within a set of arbitrary orthogonal states, is described and experimentally verified by a NMR quantum information processor (Appendix. C). The procedure is scalable and can be applied to any set of orthogonal states. Scalability is demonstrated through Matlab simulation.

Electric vehicles have received substantial attention in the past few years since they provide a more sustainable, efficient, and greener transportation alternative in comparison to conventional fossil-fuel powered vehicles. Lithium-Ion batteries represent the most important component in the electric vehicle powertrain and thus require accurate monitoring and control. Many challenges are still facing the mass market production of electric vehicles; these challenges include battery cost, range anxiety, safety, and reliability. These challenges can be significantly mitigated by incorporating an efficient battery management system. The battery management system is responsible for estimating, in real-time, the battery state of charge, state of health, and remaining useful life in addition to communicating with other vehicle components and subsystems. In order for the battery management system to effectively perform these tasks, a high-fidelity battery model along with an accurate, robust estimation strategy must work collaboratively at various power demands, temperatures, and states of life. Lithium ion batteries are considered in this research. For these batteries, electrochemical models represent an attractive approach since they are capable of modeling lithium diffusion processes and track changes in lithium concentrations and potentials inside the electrodes and the electrolyte. Therefore, electrochemical models provide a connection to the physical reactions that occur in the battery thus favoured in state of charge and state of health estimation in comparison to other modeling techniques. The research presented in this thesis focuses on advancing the development and implementation of battery models, state of charge, and state of health estimation strategies. Most electrochemical battery models have been verified using simulation data and have rarely been experimentally applied. This is because most electrochemical battery model parameters are considered proprietary information to their manufacturers. In addition, most battery models have not accounted for battery aging and degradation over the lifetime of the vehicle using real-world driving cycles. Therefore, the first major contribution of this research is the formulation of a new battery state of charge parameterization strategy. Using this strategy, a full-set of parameters for a reduced-order electrochemical model can be estimated using real-world driving cycles while accurately calculating the state of charge. The developed electrochemical model-based state of charge parameterization strategy depends on a number of spherical shells (model states) in conjunction with the final value theorem. The final value theorem is applied in order to calculate the initial values of lithium concentrations at various shells of the electrode. Then, this value is used in setting up constraints for the optimizer in order to achieve accurate state of charge estimation. Developed battery models at various battery states of life can be utilized in a real-time battery management system. Based on the developed models, estimation of the battery critical surface charge using a relatively new estimation strategy known as the Smooth Variable Structure Filter has been effectively applied. The technique has been extended to estimate the state of charge for aged batteries in addition to healthy ones. In addition, the thesis introduces a new battery aging model based on electrochemistry. The model is capable of capturing battery degradation by varying the effective electrode volume, open circuit potential-state of charge relationship, diffusion coefficients, and solid-electrolyte interface resistance. Extensive experiments for a range of aging scenarios have been carried out over a period of 12 months to emulate the entire life of the battery. The applications of the proposed parameterization method combined with experimental aging results significantly improve the reduced-order electrochemical model to adapt to various battery states of life. Furthermore, online and offline battery model parameters identification and state of charge estimation at various states of life has been implemented. A technique for tracking changes in the battery OCV-R-RC model parameters as battery ages in addition to estimation of the battery SOC using the relatively new Smooth Variable Structure Filter is presented. The strategy has been validated at both healthy and aged battery states of life using driving scenarios of an average North-American driver. Furthermore, online estimation of the battery model parameters using square-root recursive least square (SR-RLS) with forgetting factor methodology is conducted. Based on the estimated model parameters, estimation of the battery state of charge using regressed-voltage-based estimation strategy at various states of life is applied. The developed models provide a mechanism for combining the standalone estimation strategy that provide terminal voltage, state of charge, and state of health estimates based on one model to incorporate these different aspects at various battery states of life. Accordingly, a new model-based estimation strategy known as the interacting multiple model (IMM) method has been applied by utilizing multiple models at various states of life. The method is able to improve the state of charge estimation accuracy and stability, when compared with the most commonly used strategy. This research results in a number of novel contributions, and significantly advances the development of robust strategies that can be effectively applied in real-time on-board of a battery management system. / Thesis / Doctor of Philosophy (PhD)