SHAOFAN LI
@berkeley.edu
Department of Civil and Environmental Engineering
University of California, Berkeley
EDUCATION
Ph.D. in Mechanical Engineering (06/1997)
Northwestern University, Evanston, IL
M.S. in Aerospace Engineering (05/1993)
University of Florida, Gainesville, FL
M.S. in Computational Mechanics (06/1989)
Huazhong University of Science and Technology, Wuhan, China
B.S. in Mechanical Engineering (06/1982)
East China University of Science and Technology, Shanghai, China
RESEARCH, TEACHING, or OTHER INTERESTS
Computational Mechanics, Materials Science, Civil and Structural Engineering, Computational Theory and Mathematics
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Yu Ao, Huilin Duan, and Shaofan Li
Elsevier BV
Dana Bishara and Shaofan Li
Elsevier BV
F.M. Ren, J.R. Xiong, S.F. Li, S.Y. Tian, Y.S. Li, C.L. Lai, and J.X. Mo
Elsevier BV
Bing Xue, A-Man Zhang, Yu-Xiang Peng, Qi Zhang, and Shaofan Li
Springer Science and Business Media LLC
Jianrong Xiong, Fengming Ren, Shaofan Li, Shiyu Tian, Yongsheng Li, and Jinxu Mo
Elsevier BV
Ruohan Wang, Shaofan Li, Yong Liu, Xuan Hu, Xin Lai, and Michael Beer
Elsevier BV
Jing Han, Shaofan Li, Wen-Tao Liu, and Jiale Yan
Elsevier BV
Ao Yu, Yunbo Li, Shaofan Li, and Jiaye Gong
Springer Science and Business Media LLC
AbstractIn this work, we constructed a neural network proxy model (NNPM) to estimate the hydrodynamic resistance in the ship hull structure design process, which is based on the hydrodynamic load data obtained from both the potential flow method (PFM) and the viscous flow method (VFM). Here the PFM dataset is applied for the tuning, pre-training, and the VFM dataset is applied for the fine-training. By adopting the PFM and VFM datasets simultaneously, we aim to construct an NNPM to achieve the high-accuracy prediction on hydrodynamic load on ship hull structures exerted from the viscous flow, while ensuring a moderate data-acquiring workload. The high accuracy prediction on hydrodynamic loads and the relatively low dataset establishment cost of the NNPM developed demonstrated the effectiveness and feasibility of hybrid dataset based NNPM achieving a high precision prediction of hydrodynamic loads on ship hull structures. The successful construction of the high precision hydrodynamic prediction NNPM advances the artificial intelligence-assisted design (AIAD) technology for various marine structures.
Kun Zhou, Xueyu Bai, Pengfei Tan, Wentao Yan, and Shaofan Li
Elsevier BV
Qi Zheng, Chengyao Liang, Jinyang Jiang, Haiyan Mao, Karen C. Bustillo, Chengyu Song, Jeffrey A. Reimer, Paulo J.M. Monteiro, Haimei Zheng, and Shaofan Li
Elsevier BV
Weidong Li, Nhon Nguyen-Thanh, Qi Zhang, Hejun Du, Shaofan Li, and Kun Zhou
Springer Science and Business Media LLC
Jiale Yan, Shaofan Li, Xingyu Kan, Pengyu Lv, A-Man Zhang, and Huiling Duan
Springer Science and Business Media LLC
Renwei Liu, Yanzhuo Xue, and Shaofan Li
Springer Science and Business Media LLC
Yuxi Xie, Shaofan Li, C. T. Wu, Zhipeng Lai, and Miao Su
Springer Science and Business Media LLC
Pu‐Song Ma, Xing‐Cheng Liu, Xue‐Ling Luo, Shaofan Li, and Lu‐Wen Zhang
Wiley
AbstractThe intractable multiscale constitutives and the high computational cost in direct numerical simulations are the bottlenecks in fracture analysis of heterogeneous materials. In an attempt to achieve a balance between accuracy and efficiency, we propose a mathematically rigorous phase‐field model for multiscale fracture. Leveraging the phase‐field theory, the difficulty of discrete‐continuous coupling in conventional cross‐scale crack propagation analysis is resolved by constructing a continuum description of the crack. Based on the asymptotic expansion, an equivalent two‐field coupled boundary‐value problem is well‐defined, from which we rigorously derive the macroscopic equivalent parameters, including the equivalent elasticity tensor and the equivalent fracture toughness tensor. In our approach, both the displacement field and the phase‐field are simultaneously expanded, allowing us to obtain a fracture toughness tensor with diagonal elements of the corresponding matrix controlling anisotropic fracture behavior and non‐diagonal elements governing crack deflection. This enables multiscale finite element homogenization procedure to accurately reproduce microstructural information, and capture the crack deflection angle in anisotropic materials without any a priori knowledge. From the numerical results, the proposed multiscale phase‐field method demonstrates a significant reduction in computation time with respect to full‐field simulations. Moreover, the method accurately reproduces physical consistent anisotropic fracture of non‐centrosymmetric porous media, and the experimentally consistent damage response of fiber‐reinforced composites. This work fuses well‐established mathematical homogenization theory with the cutting‐edge fracture phase‐field method, sparking a fresh perspective for the fracture of heterogeneous media.
Yu Ao, Shaofan Li, Yunbo Li, and Jiaye Gong
Informa UK Limited
Zilan Zhang, Yu Ao, Shaofan Li, and Grace X. Gu
Elsevier BV
Yu Ao, Jian Xu, Dapeng Zhang, and Shaofan Li
ASME International
Abstract Designing an excellent hull to reduce the sailing path energy consumption of UUVs is crucial for improving the energy endurance of UUVs. However, path energy consumption-based UUV hull design requires a tremendous amount of calculation due to the frequent changes in relative velocity and attack angle between a UUV and ocean current. In order to address this issue, this work developed a data-driven design methodology for energy consumption-based UUV hull design using artificial intelligence-aided design (AIAD). The design methodology in this work combined a deep learning (DL) algorithm that predicts UUVs’ resistance with different hull shapes under different velocities and attack angles with the particle swarm optimization (PSO) algorithm for UUV hull design. We tested the proposed methodology in a path energy consumption-based experiment, where the optimized UUV hull showed an 8.8% reduction in path energy consumption compared with the initial UUV hull, and design costs were greatly reduced compared with the traditional computational fluid dynamics (CFD)-based methodology. Our work demonstrates that AIAD has the potential to solve UUV design problems previously thought to be too complex by offering a data-driven engineering shape (body surface) design method.
Caglar Tamur, Shaofan Li, and Danielle Zeng
MDPI AG
Predicting material properties of 3D printed polymer products is a challenge in additive manufacturing due to the highly localized and complex manufacturing process. The microstructure of such products is fundamentally different from the ones obtained by using conventional manufacturing methods, which makes the task even more difficult. As the first step of a systematic multiscale approach, in this work, we have developed an artificial neural network (ANN) to predict the mechanical properties of the crystalline form of Polyamide12 (PA12) based on data collected from molecular dynamics (MD) simulations. Using the machine learning approach, we are able to predict the stress–strain relations of PA12 once the macroscale deformation gradient is provided as an input to the ANN. We have shown that this is an efficient and accurate approach, which can provide a three-dimensional molecular-level anisotropic stress–strain relation of PA12 for any macroscale mechanics model, such as finite element modeling at arbitrary quadrature points. This work lays the foundation for a multiscale finite element method for simulating semicrystalline polymers, which will be published as a separate study.
Qi Zheng, Chengyao Liang, Jinyang Jiang, Xinle Li, and Shaofan Li
Elsevier BV
Mehrdad Ebrahimi, Elnaz Nobahar, Reza Karami Mohammadi, Ehsan Noroozinejad Farsangi, Mohammad Noori, and Shaofan Li
Elsevier BV
Lin Ma, C. S. Cai, Guangdong Zhou, and Shaofan Li
American Society of Civil Engineers (ASCE)
Quan Gu, Zhe Lin, Lei Wang, Zhijian Qiu, Surong Huang, and Shaofan Li
Elsevier BV