Abstract In this paper, a novel multi-fidelity modelling-based optimisation framework is developed for the robust design of composite structures. The proposed framework provides significant savings on computation time compared to… Click to show full abstract
Abstract In this paper, a novel multi-fidelity modelling-based optimisation framework is developed for the robust design of composite structures. The proposed framework provides significant savings on computation time compared to both conventional multi-fidelity and high-fidelity modelling methods while maintaining an acceptable level of accuracy. Artificial neural networks (ANNs) and multi-level optimisation approach are both incorporated into this multi-fidelity modelling formulation. The framework utilises varied High-Fidelity Model (HFM) and Low-Fidelity Model (LFM) covering different design spaces. This means that the HFM has only a few design variables, whereas the LFM explores the entire design spaces during the optimisation process. The proposed multi-fidelity formulation is demonstrated by the robust design optimisation (RDO) of a mono-stringer stiffened composite panel considering design uncertainty under non-linear post-buckling regime.
               
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