Recent Advances in Damage Mechanics of Composite Materials

Dear Colleagues,

This Special Issue calls for publications on recent advances surrounding the damage mechanics of composite materials. Advances in this field are focused on improving the ways that we understand, predict, and mitigate damage initiation, progression, and failure mechanisms. The aim is to present research results essential for addressing emerging challenges and optimizing the performances of composite structures in demanding environments. In this Special Issue, we plan to include the latest discoveries associated with the following: (a) multi-scale computational simulations and models of damage processes, from the microscale level to the macroscale response of composite structures; (b) refined progressive damage models to better capture damage accumulation under various loading conditions, considering interactions between different damage modes and providing insights into complex failure mechanisms; (c) advanced experimental characterization techniques that enable a detailed characterization of damage initiation and growth and provide valuable data for validating and refining damage models; (d) the integration of sensors and smart materials into composite structures, allowing the real-time monitoring of damage accumulation and structural health by detecting changes in mechanical properties, deformation, and temperature, thus providing early warnings; (e) results from efforts focused on developing self-healing mechanisms to autonomously repair damage and extend service life; (f) tailored design for damage tolerance based on advanced manufacturing techniques, which involve strategically reinforced critical areas and incorporate damage resistance features; (g) research results on the impact of environmental factors (moisture, temperature, etc.) on damage behavior and long-term durability; (h) machine learning and data-driven approaches to analyze large data sets generated from experiments and simulations to identify complex patterns, correlations, and predictive relationships in damage behavior, leading to more accurate and efficient damage prediction models.

Dr. Dionysios T.G. Katerelos
Guest Editor

Manuscript Submission Information

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• damage mechanics
• composites
• damage initiation and growth
• modelling
• damage sensing
• experimental techniques
• self-healing
• damage tolerance
• environmental effects
• machine learning