Optimization of types, numbers and locations of sensors and actuators used in modal analysis of aircraft structures using genetic algorithm


Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Havacılık ve Uzay Mühendisliği Bölümü, Türkiye

Tezin Onay Tarihi: 2017

Öğrenci: NIMA PEDRAMASL

Danışman: MELİN ŞAHİN

Özet:

Aircraft structures are exposed to dynamic loads under service conditions and therefore, it is necessary to determine their dynamic characteristics. Dynamic characteristics of a structure can be determined using simulation-based methods such as finite element analysis (FEA) or test-based methods such as experimental modal analysis (EMA). In order to perform an EMA with reliable and high quality results, test equipment must be lightweight and have high accuracy. In addition, the sensors and actuators must be positioned in an optimum pattern to extract dynamic characteristics (e.g., natural frequencies, mode shapes) of structure as correct as possible. In this study, a trapezoidal fin-like structure and an Unmanned Aerial Vehicle (UAV) wing are used as test structures, and it is aimed to find the optimum locations, types and numbers of transducers used in modal test which result in minimum mass loading error in natural frequency predictions, minimum mode shape observability vi error, minimum exciter errors (double hit with impact hammer and shaker-structure interaction with modal shaker) and minimum total sensor and actuator cost using a multi-objective optimization approach. The multi-objective genetic algorithm (MOGA) solver of the Global Optimization Toolbox in MATLAB is utilized to solve optimization problem. MSC©NASTRAN finite element solver is utilized to predict dynamic characteristics the structure. It is found that minimization of the mass loading error is achieved by locating the sensors near areas with minimum modal constant in all modes of interest and near clamped region of structures. It is also found that minimization of mode shape observability error is obtained by locating the sensors to the points with large displacements and avoiding nodal lines. With the inclusion of the optimum driving point error, the optimization results for the total error are also presented and validated with the experimental modal analyses.