Efficient Procedure for Robust Optimal Design of Aerospace Laminated Structures

Paper #:
  • 2017-01-2058

Published:
  • 2017-09-19
Citation:
Noziglia, F., Rigato, P., Cestino, E., Frulla, G. et al., "Efficient Procedure for Robust Optimal Design of Aerospace Laminated Structures," SAE Technical Paper 2017-01-2058, 2017.
Pages:
10
Abstract:
Innovative aircraft design studies have noted that uncertainty effects could become significant and greatly emphasized during the conceptual design phases due to the scarcity of information about the new aero-structure being designed. The introduction of these effects in design methodologies are strongly recommended in order to perform a consistent evaluation of structural integrity. The benefit to run a Robust Optimization is the opportunity to take into account uncertainties inside the optimization process obtaining a set of robust solutions. A major drawback of performing Robust Multi-Objective Optimization is the computational time required. The proposed research focus on the reduction of the computational time using mathematic and computational techniques. In the paper, a generalized approach to operate a Robust Multi-Objective Optimization (RMOO) for Aerospace structure using MSC software Patran/Nastran to evaluate the Objectives Function, is proposed. A Multi-Objective Differential Evolution Algorithm with a K-NN surrogate model and named MODE-LD+SS-KNN, is used. The robust evaluation is obtained via a Quasi Monte Carlo Method using Sobol sequence (QMCM), the uncertainties due to material and manufacturing process are modeled via Composite Micromechanics Theory. Example of applications presented include the optimization process for a composite flat plate for minimum weight and maximum uniaxial buckling load. The proposed approach is compared with classical Robust Multi-Objective Optimization method in terms of computational time and a reduction up to one order of magnitude has been pointed out. The computational time reduction makes the Robust Optimization a more suitable choice in comparison with non-Robust Optimization when uncertainty should be included in the optimization loop.
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