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ABSTRACT
Multiscale Topology Optimization using Subspace-based Model Reduction Method
Yuan Zhu1, 2, Xin Ning1, 2, Yao Zhang1, 2, Yuwan Yin1, 2
1 School of Astronautics, Northwestern Polytechnical University, Xi’an, 710072, PR China
2 National Key Laboratory of Aerospace Flight Dynamics, Xi’an, 710072, PR China
* Corresponding Authors: Xin Ning and Yuan Zhu. Email: ; .
The International Conference on Computational & Experimental Engineering and Sciences 2021, 23(1), 11-12. https://doi.org/10.32604/icces.2021.08311
Abstract
High performance of the spacecraft structure is required in
the special environment, it includes mechanical performance and
operational performance, etc. When performing tasks, the
spaceborne equipment requires high precision. Therefore, the design
of lightweight, high stability and high reliability structure is essential
for spacecraft. Topology optimization is widely used in structural
design. However, there are some problems in the structure after
macro topology optimization, such as checkerboard, local optimal
solution and other phenomena. Despite a long calculation period, the
obtained structure is often not smooth enough and hard to
manufacture. Aiming to this issue, this paper proposes a combined
method of multiscale topology optimization method and multisubstructure multi-frequency quasi-static Ritz vector (MMQSRV)
method. Firstly, a shape interpolation technology is used to generate
microstructures. Those microstructures are predicted the effective
characteristics by Kriging metamodel, and then they are used to build
the macrostructure. Furthermore, variable thickness sheet (VTS)
method is used to get the structure of free distribution of materials.
Due to the similar topological characteristics, the interfaces of
microstructures are well connected. In addition, on the macro scale,
based on the Ritz vector method, the MMQSRV method simplifies
the model matrix and effectively guarantees the computational
efficiency and accuracy by Krylov Subspace Method. This method
optimizes the microstructure and macrostructure, and curtail the the
iteration period of topology optimization.
Keywords
Cite This Article
APA Style
Zhu, Y., Ning, X., Zhang, Y., Yin, Y. (2021). Multiscale topology optimization using subspace-based model reduction method. The International Conference on Computational & Experimental Engineering and Sciences, 23(1), 11-12. https://doi.org/10.32604/icces.2021.08311
Vancouver Style
Zhu Y, Ning X, Zhang Y, Yin Y. Multiscale topology optimization using subspace-based model reduction method. Int Conf Comput Exp Eng Sciences . 2021;23(1):11-12 https://doi.org/10.32604/icces.2021.08311
IEEE Style
Y. Zhu, X. Ning, Y. Zhang, and Y. Yin "Multiscale Topology Optimization using Subspace-based Model Reduction Method," Int. Conf. Comput. Exp. Eng. Sciences , vol. 23, no. 1, pp. 11-12. 2021. https://doi.org/10.32604/icces.2021.08311