Modélisation Numérique Pour la Tolérance Aux Dommages D'impact Sur Stratifié Composite
Author | : Natthawat Hongkarnjanakul |
Publisher | : |
Total Pages | : 0 |
Release | : 2013 |
ISBN-10 | : OCLC:870416947 |
ISBN-13 | : |
Rating | : 4/5 (47 Downloads) |
Download or read book Modélisation Numérique Pour la Tolérance Aux Dommages D'impact Sur Stratifié Composite written by Natthawat Hongkarnjanakul and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Impacts on composite structures can greatly reduce their residual strength without leaving a visible mark on the outer surface. In aeronautics, a minimum detection threshold of the impact damage is defined, based on the permanent indentation left after impact. Below this threshold, the structure must withstand a defined load: it is the notion of impact damage tolerance. The numerical design of a composite structure taking into account aspects such as detectability and damage tolerance thus requires to know how to represent impact, permanent indentation and residual strength under compression.This work focuses on the numerical modeling of composite laminates made of unidirectional plies. The objective is to develop a predictive model of post-impact residual strength. An experimental study was conducted to investigate the damage scenario during impact and compression after impact (CAI), and provide experimental data to validate the simulations.A finite element modeling with a Discrete Ply Model (DPM) approach is performed based on previous work done at the laboratory. The impact model is improved and validated on different stacking sequences to ensure the robustness of the model. Specific three-point bending tests are performed to have a better understanding of the formation of permanent indentation. A new model of permanent indentation is then proposed and applied in the impact model. Finally, a model is built to predict CAI residual strength. The three steps: impact, indentation and CAI are combined into a single model.