About the Lecturer

Huang Minsheng, Dean of Engineering Mechanics, School of Aerospace Engineering, Huazhong University of Science and Technology, serves as a member of the Youth Working Committee of the Chinese Society of Theoretical and Applied Mechanics as well as an editorial board member of the English version of Chinese Journal of Solid Mechanics. Professor Huang worked as Senior Research Assistant and Guest Senior Lecturer at the University of Portsmouth, UK. He engages in the study of multi-scale simulation of the blade with nickel-based superalloys, and his areas of researches cover multi-scale plasticity and damage, the mechanics behaviors of high temperature and radiation resistant materials and the ones with strong magnetic field as well as their multi-scale correlation. Professor Huang has published more than 60 SCI papers in the top journals of solid mechanics and international prestigious journals of materials science such as JMPS, Int. J. Plasticity, Acta Mater, J. Nuc., Mat., etc.

About the Lecture

The damage and fracture of metal materials are closely related to the nucleation, growth and polymerization of microvoids. Scholars at home and abroad have studied the long process of void growth. “Isotropic Matrix” serves as the basic assumption for both the widely used Gurson model and GTN model, as well as the void damage model newly developed from the former two. In fact, the size of void damage is much smaller than or of the same size as that of crystal grain. But very few reports were made on the inevitable impact on the evolution of the pores by the anisotropic crystal environment and the material micro-structure of the voids. In this regard, the research team firstly studied the effect of polycrystalline orientation and local micro-structure on the void growth based on the crystal plasticity, and discussed the correlation between them and the external load. An innovative random statistical void growth model was also developed. Then, the team developed an efficient discrete dislocation dynamics algorithm based on XFEM, studied the effects of grain scale discrete plasticity and scale effect on the growth of random statistical voids, and probed into the mechanism of internal dislocation. Finally, the team established a convolutional neural network + cyclic neural network model based on PyTorch, aiming to predict the growth of polycrystalline voids in different crystal orientations, grain sizes, void sizes, and local environment.