Integrated Structural Health Monitoring with Real-time Data Assimilation and Hybrid Twins of Dynamical Systems

The design of smart autonomous mechanical structures able to perform online control of their integrity, and take anticipated actions during service before downtime of failure occur, has become an active research area. It is a critical need in various industrial sectors (transport, energy, etc.) for more reliability but also more performance and durability of equipment (aircrafts, wind turbines, bridges, etc.). Implementing such an advanced technology would permit optimized maintenance and capability to operate in degraded mode, managing the decrease of loading capabilities by adapting the operating plan. However, the real-time monitoring of damage in engineering systems, by dynamically coupling predictive simulation tools (in terms of digital twins) and sensor observations, is made very difficult in practice due to several issues. In particular, the complex nonlinear multiscale phenomena which are involved may be associated with computationally intensive simulations (hardly compatible with real-time), which requires reduced order modeling and effective strategies for data assimilation and control. In addition, the problem is plagued with model bias, uncertain environment, and measurement noise, which need to be taken into account for accurate diagnosis and prognosis, and safe decision-making. In this context, an appealing trend is to refer to hybrid twins, in which an a priori physics-guided model is updated and enriched on the-fly with data-based information, thus making benefit of all knowledge available. In the talk, I will present some effective numerical methods and recent developments for the construction and use of such hybrid twins, accommodating real-time, accuracy and robustness issues. I will focus on a novel sequential data assimilation framework (based on a modified dual Kalman filter) that continuously integrates sensor data into the numerical model, for damage detection, prediction, and control. I will detail the overall cross-disciplinary methodology which has been chosen, embracing experimental mechanics, data science (including deep learning), advanced modeling and simulation strategies (e.g., use of ROM and adaptive approaches), or computer science. The performance of the proposed strategy will be illustrated on some selected engineering applications e.g, the online control of shaking table tests performed on large-scale damageable concrete buildings, or the design of smart structures equipped with embedded high-resolution distributed optic fiber sensors.

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 101002857)