Paralel genetik algoritmalar kullanarak 3B kanadın aerodinamik ve yapısal optimizasyonu.

As a multi-disciplinary optimization problem, aerostructural shape optimization of airplane wings requires both aerodynamic and structural analysis to meet an objective defined as the sum of parameters like drag to lift ratio and wing weight, subjected to penalty of structural yield stress and geometrical sizing constraints to get aerodynamically efficient and lightweight 3D wings. In our study, genetic algorithms are utilized for optimization of 3D wing with its internal structural components. In order to optimize an airplane wing with genetic algorithms, parametric automated geometry and mesh generator is developed. Since automation and aerostructural analysis increase the complexity of the fitness calculation, utilization of parallelism for genetic algorithms becomes crucial. This thesis proposes an aerostructural 3D wing optimization tool using different models of parallel genetic algorithms. Moreover, the results of these models are discussed in the scope of this study.Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Computer Engineering