Digital Twins

Digital Twins

Digital Twins (DT) are one-to-one digital copies of existing entities, integrating all the knowledge of the mathematical models combined with the working data acquired during real operations. 

Data is collected from the real process, typically from IoT sensors, to map its behaviour virtually; the digital twin uses the data collected to simulate and predict the behaviour of the real entity.Knowing and predicting the evolution of a process is especially useful in the industrial field to reduce production losses and optimise processes, diagnose possible anomalies, and correct production according to the expected future scenario. 

Creating a digital twin requires a diverse set of skills. It entails the need for expertise in various domains, including:  

  • Infrastructural: This involves creating the data collection architecture from IoT sensors. 
  • Data Management: to manage the data and process it,  
  • Mathematical: Modeling the real-world process using the available data. Moreover, developing control and prediction HPC algorithms are key aspects since the amount of data collected and the ability to manage and scale the algorithms processing it is paramount. Proficiency in HPC is critical for this purpose. 

The role of simulation in digital twins, enabled by HPC infrastructures, is dual-fold. In the design phase, simulations allow for exploring various process behaviours, aiding in predicting potential risk situations. When the prediction algorithm encounters uncertainty in the operational phase, simulations can be employed to understand possible anomalies better.  

Cineca, with its extensive expertise in the realm of HPC, actively contributes to numerous projects centred around digital twins, making a substantial impact in this field.