Research Topic 2
Research Topic 2 (RT2) investigates the field of Computational Fluid Dynamics (CFD), which studies and simulates how fluids like air and water move and interact with other objects or with each other, through numerical computation algorithms. CFD is essential for modeling industrial, physiological, and environmental processes, thus enabling the realization of their Digital Twins. Yet, the subtleties of fluid dynamics pose considerable computational hurdles.
RT2 approaches this challenge by exploring Model Order Reduction (MOR) techniques. By keeping only the most relevant parameters in a model, MOR techniques offer a streamlined yet accurate portrayal of fluid behavior. MOR thus enables faster simulation times, paving the way for accelerated decision-making.
The crucial need to save computational resources drives RT2 to exploit Deep Learning (DL), a subset of artificial intelligence algorithms composed of many layers of interconnected networks that can extract more advanced features from initial data progressively. Deep Learning facilitates real-time data processing and ensures real-time response in the application of Digital Twins.
MOR techniques: some remarkable achievements
Naval industry
- Optimization of the shape of passenger ships to reduce fuel consumption and pollution.
- Optimization of ship structures for minimal material usage while maintaining stability.
Environment
- Study of the impact of pollutant diffusion in the Gulf of Trieste on marine ecosystems, considering factors like sea currents and wind.
Medicine
- Real-time patient-specific diagnostics for cardiovascular diseases.
Other sectors
- Food waste minimization.
- Appliance optimization.
- Early detection of structural damages in tunnels.
Coordination
The International School for Advanced Studies (SISSA), the University of Padua (UniPD), and the National Institute of Oceanography and Experimental Geophysics (OGS) are involved in Research Topic 2.
The coordinator of RT2 is Prof. Gianluigi Rozza from SISSA.
