Hibrit global optimizasyon algoritmasının geliştirilmesi ve helikopter rotor yapısal optimizasyonuna uygulanması.

Helicopters are notorious for their high vibration levels and the rotor system are the main contributors to the problem. The rotor vibrations can be minimized by optimizing the rotor structure, which require time-consuming high-fidelity solution for vibration predictions. To solve this problem, an effective and efficient global search algorithm called Explorer-Settler Optimization algorithm is developed by combining the advantageous aspects of Particle Swarm Optimization and Nelder-Mead Optimization algorithms. It is shown that the developed algorithm performs superior when compared to other popular search algorithms in terms of search space exploration with minimum number of objective function calls. Moreover, to reduce the rotor optimization times, surrogate-based models are implemented for rotor blade structural property predictions. Data transformation techniques are evaluated and applied to minimize prediction errors. For the reduction of rotor vibrations, two main approaches are implemented namely, frequency separation approach (FSA) and direct vibration reduction approach (DVRA). Along with vibration reduction, both approaches aim to minimize rotor blade mass while satisfying various dynamic and static constraints. However, FSA attempts to achieve minimum vibrations by separating the natural frequencies of the rotor blades from excitation frequencies to avoid resonances; whereas, DVRA directly targets vibration amplitudes. A four-bladed rotor is optimized using both approaches and the performances of them are compared against each other. The results indicate that DVRA performs better in vibration reduction while FSA provides lighter rotor blades.Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Mechanical Engineering

Evaluation of Surrogate-Based Modeling Methods for the Optimization of Helicopter Rotor Structures for Minimum Vibration

Helicopters are notorious for their high vibration levels and the rotors are the main contributors. At preliminary stages, it is essential to design the rotors to achieve minimum vibration amplitudes, which is generally realized by using optimization routines. The optimization of a helicopter rotor for minimum vibrations requires repeated high-fidelity solutions, which lead to high computational times. Moreover, since the rotor optimization problem contains many local minima by its nature, the optimization process might be repeated in order to guarantee the global minimum, which results in increased solution times. In this study, surrogate-based models for the prediction of optimization results are investigated to reduce the computational expense for the optimization of a four-blade rotor. The composite rotor blade cross-sectional design parameters are utilized for the optimization and the rotor is assumed to be in a high-speed forward flight. Along with vibration minimization, the minimum blade mass is also targeted and both of the objectives are subjected to a number of static and dynamic constraints. The objectives and the constraints are written as functions of design variables and for each function, a surrogate model is constructed. To reduce prediction errors in the surrogate model, data transformation techniques are employed. Using these surrogate models, the rotor is optimized. It is concluded that by using surrogate-based modeling and data transformation techniques, computational time required by the optimization process can be significantly reduced without compromising the optimization accuracy

Disappearance of Biodiversity and Future of Our Foods

“I. Uluslararası Organik Tarım ve Biyoçeşitlilik Sempozyumu 27-29 Eylül Bayburt