This study investigates the advantages and disadvantages of using probabilistic optimization methods in aircraft structural design. The necessity to achieve a design insensitive to system's variations (robust) and less likely to fail (reliable) is addressed, in order to reduce costs and risk of accidents. Because of the complex nature of composite structures, the need of surrogate models to predict structural responses arises. In order to build a meta-model, Design of Experiments (DOE) methods are used to determine the location of sampling points in the design space. Monte Carlo Simulations (MCS), creating random samples, are used to propagate uncertainties from the surrogate model inputs to variations in model outputs. Different optimization algorithms and surrogate models are compared, in order to speed up the optimization process and reduce modelling errors. The deterministic design resulted in a design which is neither robust nor reliable. Stochastic approaches accounting for uncertainties, on the other hand, resulted in enhanced robustness (Robust Design), enhanced reliability (Reliable Design) or a combination of both (Robust and Reliable Design).
Uncertainty propagation in inverse reliability-based design of composite structures