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- W3117005376 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." @default.
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- W3117005376 date "2021-03-01" @default.
- W3117005376 modified "2023-09-25" @default.
- W3117005376 title "Multi-fidelity robust design optimisation for composite structures based on low-fidelity models using successive high-fidelity corrections" @default.
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- W3117005376 doi "https://doi.org/10.1016/j.compstruct.2020.113477" @default.
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