Speaker
Description
The rapid advancement of Industrial technologies and data collection and handling methods has paved the way for the widespread adoption of Digital Twins (DTs) in engineering, enabling seamless integration between physical systems and their virtual counterparts. Digital Twins are dynamic, real-time digital replicas of physical assets, systems, or processes designed to optimise performance, enhance predictive capabilities, and support decision-making across diverse engineering domains.
The current work presents a comprehensive framework for building robust and scalable Digital Twins tailored for material testing applications using a universal testing machine (UTM), focusing on core challenges such as model fidelity, data integration, and computational efficiency. Our goal is to build a Digital Twin of a material subjected to cyclic loading. A simple linear elastic material model with its governing PDE is considered in this case. The model is used to simulate the material's behaviour through the finite element method. Later, the obtained high-fidelity solution is reduced with the help of a well-known POD greedy method, which helps to improve the computational efficiency. The solution is further improved by integrating data collected by sensors in real time. The Parametrized-Background-Data-Weak (PBDW) (ref) method is implemented to realise the data integration.
Our approach integrates physical knowledge, which is available in terms of constitutive or material modelling, and real-time sensor data to construct and continuously update the Digital Twins. Emphasis is placed on the knowledge available through Partial Differential Equations, which address the reliability and robustness of the digital twin model. This work highlights the advantages of the digital twins in the predictive maintenance and health monitoring of assets or systems, which eliminates unexpected failures and downtimes in engineering applications.
