The FaReX project aims at developing new numerical methods for the efficient simulation of complex multi-scale and multiphysics problems, such as fluid-structure interaction or phase transition, using non-matching modeling techniques, immersed methods and model reduction techniques, also through machine learning. The involved units (SISSA, PoliTO, UniBS) work jointly on numerical analysis, complexity reduction and open source software development.
m.a.r.i.n.A.I. aims to reduce the noise radiated into water by the propellers of ships and motor yachts, using an innovative methodology based on numerical fluid dynamics and hydroacoustic simulations to train a machine learning-based algorithm. The result will be a tool for designing more efficient propulsion systems, reducing noise, fuel consumption, and design time.
Il progetto SHip OPtimization MEsh Parameterization Assistant (SH.OP. ME.PA.) è un progetto svolto nell’ambito del Piano Nazionale di Ripresa e Resilienza (PNRR) in collaborazione con Fincantieri S.p.A., avente obiettivo l’integrazione di tecniche di ottimizzazione e di machine learning nella fase di progettazione strutturale delle navi da crociera.
Recently, the simulation of complex engineering problems has become possible due to modern simulation techniques. However, this capability comes at the cost of high computational requirements that are often incompatible with the deployment in computationally constrained environments.
Reduced Order Models (ROMs) provide a possible solution to this limitation by taking the output of the “Full Order Models” (FOMs) and identifying the common patterns allowing similar problems to be solved at a much-reduced computational cost.
The goal of the project between MIT and SISSA mathLab is the improvement of parametric ocean acidification models. We exploit optimal control, non-intrusive techniques, and probabilistic learning to enhance environmental predictions. Moreover, this kind of application needs not only reliable simulations, but they should be fast.
To this end, we propose model order reduction to deal with it.
The project is rooted in marine and coastal environments: a topic of primary relevance for society and economy.
In the framework of the numerical analysis for parametric partial differential equations (PDEs), reduced-order modeling for fluid-structure interaction problems and probabilistic multi-disciplinary ship design, we want to go beyond the state of the art through this strategic collaboration between SISSA mathLab (modelling and scientific computing), Prof. Gianluigi Rozza’s group, and MIT Sea Grant - Department of Mechanical Engineering, Prof. Michael Triantafyllou’s group.
During this collaboration, we aim to develop a multi-disciplinary design framework capable of providing online probabilistic predictions of systems performance and characteristics, based solely on data generated by numerical solvers running off-line.
