Comparison of Aeroelasic Structural Sizing Approaches for Aircraft Conceptual Design

The aircraft performance optimization is a very important aspect in the increase of fuel efficiency and therefore ther eduction of environmental impact of air traffic. A possible improvement for the early design process in terms of time effort and accuracy, is the involvement of more accurate, calculation based data instead of statistical based handbook methods. To overcome huge calculation times for high fidelity data acquisition, reduced order models (ROM), which are machine learning models fed with results from higher order models, are a promising solution. In this paper, a process for the automated generation of reduced order models for aeroelastic structural sizing optimizations, using an open-source Python-library is investigated. After methodology development and implementation work, different ROM generating algorithms are applied to a test case. The resulting model can perform the calculation of aeroelastic structural sizing results in seconds, compared to hours for the original calculation method. This enables the calculation of the derivative for an optimization-algorithm and thereby the performing of a ROM-based shape optimization.