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20 results on '"Cui, Tianchen"'

3. An efficient and easy-to-extend Matlab code of the Moving Morphable Component (MMC) method for three-dimensional topology optimization

Author

Du, Zongliang, Cui, Tianchen, Liu, Chang, Zhang, Weisheng, Guo, Yilin, and Guo, Xu

Subjects
Mathematics - Optimization and Control
Abstract

Explicit topology optimization methods have received ever-increasing interest in recent years. In particular, a 188-line Matlab code of the two-dimensional (2D) Moving Morphable Component (MMC)-based topology optimization method was released by Zhang et al. (Struct Multidiscip Optim 53(6):1243-1260, 2016). The present work aims to propose an efficient and easy-to-extend 256-line Matlab code of the MMC method for three-dimensional (3D) topology optimization implementing some new numerical techniques. To be specific, by virtue of the function aggregation technique, accurate sensitivity analysis, which is also easy-to-extend to other problems, is achieved. Besides, based on an efficient identification algorithm for load transmission path, the degrees of freedoms (DOFs) not belonging to the load transmission path are removed in finite element analysis (FEA), which significantly accelerates the optimization process. As a result, compared to the corresponding 188-line 2D code, the performance of the optimization results, the computational efficiency of FEA, and the convergence rate and the robustness of optimization process are greatly improved. For the sake of completeness, a refined 218-line Matlab code implementing the 2D-MMC method is also provided., Comment: Accepted by Structural and Multidisciplinary Optimization on 3-26-2022

Published
2022
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19. PdAg alloy nanoparticles immobilized on functionalized MIL-101-NH2: effect of organic amines on hydrogenation of carbon dioxide into formic acid.

Author

Xu, Lingfei, Cui, Tianchen, Zhu, Juan, Wang, Xinkui, and Ji, Min

Subjects
*FORMIC acid, *HYDROGENATION, *METAL clusters, *AMINES, *NANOPARTICLES
Abstract

Exploration of excellent performance catalysts for carbon dioxide (CO2) hydrogenation is essential for CO2 utilization. In this work, a series of PdAg nanoparticles (NPs) immobilized on various organic amine functionalized MIL-101-NH2 catalysts were synthesized and investigated for the hydrogenation of CO2 to formic acid (FA). This showed that PdAg NPs immobilized on the triethylenediamine (TEDA) functionalized MIL-101-NH2 catalyst with an average size of 1.6 nm, denoted PdAg/TEDA-MIL-101-NH2, possessed the highest TON of 1500 at 70 °C after 10 h. Decorated amine groups in MOFs not only played a key role in the formation of ultra-small metal clusters, but also had a strong metal–support interaction with PdAg nanoparticles, where the electron-rich states of Pd could promote H2 dissociation, thereby enhancing the catalytic activity. [ABSTRACT FROM AUTHOR]

Published
2021
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20. Artificial intelligence-enhanced bioinspiration: Design of optimized mechanical lattices beyond deep-sea sponges

Author

Du, Zongliang, Hao, Wenyu, Chen, Xiaodong, Hou, Xiuquan, Huo, Wendong, Liu, Chang, Zhang, Weisheng, Cui, Tianchen, and Guo, Xu

Abstract

Euplectella aspergillum, as a textbook example presented by nature, is famous for its superior buckling resistance and stiffness subject to compression of arbitrary directions and its double-diagonal reinforced square microstructure has attracted considerable interest. In the present work, a parametric model is developed for a class of sponge-like lattices, and a neural network model is trained to realize accurate and efficient mapping from the geometry parameters to the corresponding effective stiffness and buckling resistance under uniaxial compression along all directions. Then an optimization formulation is proposed for designing a sponge-like lattice with on-demand requirements. Using the same amount of material, in the present design framework, the average buckling resistance of the original sponge-lattice model in literature is increased by about 40% and the design efficiency is improved by about 3–4 orders as compared to the traditional optimization process with exact mechanical modeling. Combining the intelligence of nature, humans and computers, the present AI-enhanced bioinspiration paradigm provides a novel pathway for designing innovative materials and structural systems.

Published
2023
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