Abstract:
A major challenge in the aerospace design process is the balance between simulation fidelity and various types of resource constraints such as financial and computational. To develop new data analytical capabilities and improve the reliability of medium fidelity projects using well selected algorithms and methods, a multidisciplinary simulation and optimization tool is developed using MATLAB. In the process, several key areas of research are identified, investigated and methods of analyses are discussed. Primarily, a novel method for implementing a hybrid augmented Lagrangian genetic algorithm is developed and thoroughly tested using a variety of benchmark problems. The algorithm is then implemented to optimize surrogate models for an MDO case study. This test case is a wing model of size comparable to a Cessna 172, flying at cruising speed. However, the methodology presented in this thesis could be extended beyond subsonic aircraft wing optimization and even outside the realm of aerospace engineering. The study allows for a better understanding of the methods used in multidisciplinary design optimization for conceptual design. The proof-of-concept successfully demonstrates the versatility of the MDO framework and opens the project up to several future avenues of research.
