Multi-scale Mechanics of 2D Triaxially Braided Composites: Characterization, Mechanics and Machine-learning Based Modelling presents the latest advances in this important research field. The book begins with a brief introduction to these materials including their mechanical features, damage and failure mechanisms and typical applications. The contents are then divided into three main sections on experimental characterization; mechanics-based modeling and machine-learning based modeling approaches. By taking a multi-scale modeling approach, that includes progressive damage and impact simulation, as well as theoretical modelling, machine-learning and multi-scale mechanics aspects, the author presents key findings in this important field. A systematic introduction is given to the multi-scale and machine-learning based modeling approach, along with their corresponding source codes for the progressive damage model. Then an up-to-date theoretical model is also presented, as well as, high-efficient finite element mesh of the unit cell, for conducting multi-scale analysis and design of these materials and structures. Numerical examples are also presented to illustrate the application of presented methods for quasi-static and impact problems. To enhance the reader’s understanding numerous engineering case studies are also included, together with examples of material/structure optimization. The book provides the latest knowledge and methodology for the design and analysis of aerospace structures and other materials technologies, guiding the researcher to understand this cutting-edge research framework.
Table of Contents
SECTION 1. Experimental Characterization1. Two-dimensional Triaxially Braided Composite (2DTBC) and its manufacturing
2. Quasi-static failure behavior and free-edge effect
3. Dynamic compressive failure behavior
4. Low-velocity impact failure behavior
5. High-velocity impact behavior
6. Effect of hydrothermal aging
SECTION 2. Mechanics-based Modeling
7. Volume-average based analytical methods
8. Three-dimensional generalized analytical model
9. Meso-FE model
10. Modelling the temperature-rise behavior
11. Multi-scale models
SECTION 3. Machine-learning based modeling
12. Introduction of Machine-learning based modelling approaches
13. Machine-learning based modelling approaches for isotropic elastoplastic materials
14. Machine-learning based modelling approaches for anisotropic composites
15. Machine-learning based modelling for textile composites
16. Conclusion and Prospects
Authors
Chao Zhang Professor, School of Civil Aviation, Northwestern Polytechnical University, Xi'an, China. Dr. Chao Zhang is a professor in the School of Civil Aviation, at Northwestern Polytechnical University (Xi'an, China). He obtained his Ph.D. degree from The University of Akron, USA in 2013 and then went on to work as a postdoctoral fellow at The University of North Carolina at Charlotte and the National Renewable Energy Laboratory from 2014 to 2016. He joined NPU in 2016. Dr. Zhang has more than 10 years' research experience working on the mechanics of composites for aerospace and energy applications, with a specific focus on textile composites and multi-scale modeling methods. Yulong li Northwestern Polytechnical University, China.Yulong Li is currently a full Professor in the School of Civil Aviation at Northwestern Polytechnical University, Xi-an, China. He received his PhD in Engineering Solid Mechanics, from Northwestern Polytechnical University (NPU), in 1991.
During his career, he has held many visiting positions at the Universit� Pierre et Marie Curie in Paris and John Hopkins University in the United States. His main areas of research include numerical simulation of materials behavior and structural response under impact loading;
dynamic behavior and failure evolution in composite materials and experimental techniques to determine materials behavior at high strain rates and elevated temperature. He has also been awarded many scientific awards throughout the course of his career.
