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Civil-Comp Conferences
ISSN 2753-3239 CCC: 9
PROCEEDINGS OF THE FIFTEENTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STRUCTURES TECHNOLOGY Edited by: P. Iványi, J. Kruis and B.H.V. Topping
Paper 3.7
Topology Optimization of Lattice-Stiffener Hybrid Core for Composite Sandwich Panel Y. Huang, T. Gao, L. Song, Y. Li, P. Fang and W. Zhang
School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, China Y. Huang, T. Gao, L. Song, Y. Li, P. Fang, W. Zhang, "Topology Optimization of Lattice-Stiffener Hybrid Core for Composite Sandwich Panel", in P. Iványi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Fifteenth International Conference on
Computational Structures Technology", Civil-Comp Press, Edinburgh, UK,
Online volume: CCC 9, Paper 3.7, 2024, doi:10.4203/ccc.9.3.7
Keywords: topology optimization, multi-material, hybrid core, stiffeners, lattices, composite sandwich panel, penetration constraint, maximum size constraint.
Abstract
Hybrid core composite sandwich panels, as a typical load-bearing structure, have gained widespread recognition in various engineering fields due to their superior structural properties and outstanding multifunctional performances. However, the stiffeners and lattices in the structure will be staggered in the normal direction. Furthermore, the stiffeners in the in-plane direction are overly concentrated. Aiming at these problems, a multi-material topology optimization method with penetration constraint and maximum size constraint is proposed in this work. Lattices are equivalent to a virtual material by using energy-based homogenization method. To achieve the uniform distribution of stiffeners /lattices along the normal direction, the penetration constraint is proposed with local cylinder search region instead of traditional spherical search in the filtering process. Then the local porosity is introduced and local constraints are aggregated to the global one by p-mean function to achieve the precise size control of the stiffeners in the in-plane direction. The optimization model is efficiently solved utilizing a gradient-based optimization algorithm incorporating the sensitivity information. Finally, an engineering example is given to verify the validity and effectiveness of the proposed method.
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