Description
RAPIDO (Un modelo rápido para el mantenimiento predictivo de estructuras de material compuesto con distintos escenarios de daño producidos por impacto) aims to provide a rapid predictive tool for safe and intelligent vehicles for civil or defense applications. Continuous monitoring of the structure's health by employing sensors guarantees the structure's safety at any moment. For instance, the detection of any anomalies such as impact events caused by bird strike, hail during a storm, or even a tool drop during inspection of the vehicle, can prevent a catastrophic situation. With a safe, intelligent, and sustainable vehicle, safety and service life are increased, and maintenance costs are reduced.
Focusing on modeling Lamb wave-based Structural Health Monitoring (SHM) systems, the research community has made significant strides in solving the detection and localization of damage in structures.RAPIDO project involves a comprehensive approach, combining the generation of a very large and high-fidelity database of damage scenarios from finite element simulations executed in High-Performance Computing (HPC) environments, and the training of Machine Learning (ML) methods for damage detection, localization and quantification tasks on complex structures. Particularly, the use of Alya structural mechanics code for impact events and SHM analysis is the key enabler for ML to predict damage variables which are otherwise unfeasible to characterize experimentally, paving the path to innovative research for new damage prediction algorithms. The project belongsThe project belongs to the national call "Plan Estatal 2021-2023 (Impulsar la investigación científico-técnica y su transferencia)" and two institutions are involved Universidad Politécnica de Madrid and Barcelona Supercomputing Center.
