High Fidelity and Efficient Computations of Dynamic Loads for Multidisciplinary Design Optimization of Flexible Transport Aircraft

Abstract

The current state-of-the-art design optimization of airframes is tightly wounded to its loads analyses as the process is usually conducted employing a deterministic set of critical load cases. The sheer number of scenarios required to estimate the critical loading conditions prevent these two processes from integrating, obstructing the development of a Multidisciplinary Design Optimization (MDO) framework. In this thesis, the problem of high fidelity and efficient estimation of critical dynamic aeroelastic loads is addressed, as a first step, towards the development of an integrated MDO platform for airframes at preliminary and detailed design stages. The method is based on the Kriging metamodeling technique along with the Latin Hypercube scheme for initial sampling and the expected improvement function for subsequent selection of sample points, known formally as the Efficient Global Optimization (EGO) algorithm. Furthermore, different inexpensive metrics, based on the concept of modal contribution factors, are investigated to serve as indicators to determine if a substantial change in the loads has occurred during the design optimization cycle, triggering the requirement for the re-exploration of the loads design space. A case study is presented to evaluate the performance of the proposed methodology versus a full factorial search. A reduction of 84% was achieved in the total time of execution employing the proposed methodology.