6 papers · 1 filter
pyforce-1.0.0: Python Framework for data-driven model Order Reduction of multi-physiCs problEms
Stefano Riva, Yantao Luo, Carolina Introini +1
pyforce is a Python package implementing Data-Driven Reduced Order Modelling techniques for applications to multi-physics problems, mainly set in the Nuclear Engineering world. The…
CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models
Stefano Riva, Carolina Introini, Antonio Cammi +13
The demand for clean energy is ever increasing, with new nuclear technologies presenting a complementary solution to renewable energies. However, designing and operating these syst…
From Models To Experiments: Shallow Recurrent Decoder Networks on the DYNASTY Experimental Facility
Stefano Riva, Andrea Missaglia, Carolina Introini +2
The Shallow Recurrent Decoder networks are a novel paradigm recently introduced for state estimation, combining sparse observations with high-dimensional model data. This architect…
Application of parametric Shallow Recurrent Decoder Network to magnetohydrodynamic flows in liquid metal blankets of fusion reactors
M. Lo Verso, C. Introini, E. Cervi +3
Magnetohydrodynamic (MHD) phenomena play a pivotal role in the design and operation of nuclear fusion systems, where electrically conducting fluids (such as liquid metals or molten…
Surrogate models for nuclear fusion with parametric Shallow Recurrent Decoder Networks: applications to magnetohydrodynamics
M. Lo Verso, C. Introini, E. Cervi +3
Magnetohydrodynamic (MHD) effects play a key role in the design and operation of nuclear fusion systems, where electrically conducting fluids (such as liquid metals or molten salts…
Towards Efficient Parametric State Estimation in Circulating Fuel Reactors with Shallow Recurrent Decoder Networks
Stefano Riva, Carolina Introini, J. Nathan Kutz +1
The recent developments in data-driven methods have paved the way to new methodologies to provide accurate state reconstruction of engineering systems; nuclear reactors represent p…