The framework under development within AROMA-CFD is going to provide attractive capabilities for several industrial and medical applications (e.g. aeronautical, mechanical, naval, off-shore, wind, sport, biomedical engineering and cardiovascular surgery as well), combining high performance computing (in dedicated supercomputing centres) and advanced reduced order modelling (in common devices), to guarantee real time computing and visualisation. A new open source software library for AROMA-CFD is under construction: ITHACA, In real Time Highly Advanced Computational Applications, enhancing current RBniCS educational and training capabilities. The first one a is available with a finite volume full order solver and a spectral element method full order solver (and also a Discontinuous Galerkin solver is in progress), the latter with a finite element full order solver, with applications also in multi-physics, thanks to Multi-Phenics packages (mathlab.sissa.it/cse-software). Several other packages have been created and are available to provide basic tools in (shape) parametrisation (PyGem), data assimilation, and non-intrusive model reduction (EZyRB, PyDMD), uncertainty quantification (ATHENA) as well. ARGOS web-server as well as ATLAS for cardiovascular flows will provide a further strategic asset to the project and they will be the goal of the related PoC ARGOS for valorisation and to enhance innovation in scientific computing applied to real world.
The aim of AROMA-CFD has been the creation a team of scientists at SISSA for the development of Advanced Reduced Order Modelling techniques with a focus in Computational Fluid Dynamics (CFD), in order to face and overcome many current limitations of the state of the art and improve the capabilities of reduced order methodologies for more demanding applications in industrial, medical and applied sciences contexts. AROMA-CFD deals with strong methodological developments in numerical analysis, with special emphasis on mathematical modelling and an extensive exploitation of computational science and engineering. Several tasks of the project are under development to tackle fascinating problems and open questions in reduced order modelling: special emphasis on the study of bifurcations and instabilities in flows, important advances concerning the capability to deal with flows characterised by increasing Reynolds number, while guaranteeing the flow stability, moving towards (moderate and developed) turbulent flows, considering more and more complex geometrical parameterisations of shapes as computational domains into extended networks, and reducing the parameter space too. A reduced computational and geometrical framework has been developed for more and more complex nonlinear inverse problems, focusing on optimal flow control, shape optimisation and uncertainty quantification. Further, these advanced developments in reduced order modelling for CFD are going to be applied in multi-physics, such as fluid-structure interaction problems, and more general coupled phenomena involving inviscid, viscous and thermal flows, solids and porous media.
In the project development a more important role has been given to data, and the capability to incorporate them in wider and stronger mathematical setting like the one provided by optimal control, such that the assimilated data contribute to improve the mathematical models and results given by numerical simulation.
In this current setting also automatic learning has provided a more and more active role to methodological developments and to the improvement of numerical techniques at different levels.
