Innovative adaptive penalty in surrogate-assisted robust optimization of blade attachments

This paper proposes the combination of the adaptive penalty method based on design space sampling and evolutionary optimization towards the solution of a multi-objective blade attachment robust design problem. An adaptive penalty function based on Latin hypercube sampling was applied to tackle the non-feasible spaces inside the searching domain. Implementing this method provided a reduction in time to convergence. A genetic algorithm (GA) was used as an optimizer to minimize the stress state in critical areas of the attachment. The state of stress was computed using a finite-element model denoted as high-fidelity model. To reduce the call back to the high-fidelity model, a meta-model (also denoted as surrogate model) was developed and embedded in the GA to reduce the computational time. Using the surrogate model instead of the high-fidelity model also provided a reduction in the time needed to find the optimum. Besides, in order to obtain the most robust solution among the optimums given by the Pareto front, the same Kriging surrogate model was employed to perform a global sensitivity analysis.