🤖 AI Summary
This study addresses the challenges of complex multiphysics system modeling—such as those encountered in nuclear engineering—where intricate model development, difficulties in sensor placement, and insufficient integration of real-world measurement data hinder accurate simulation. To overcome these issues, this work proposes a general and efficient data-driven reduced-order modeling framework implemented in Python. The framework is compatible with any solver that outputs VTK format and features a redesigned architecture that replaces DOLFINx with PyVista for mesh processing, numerical integration, and visualization. Field variables are uniformly stored as NumPy arrays, significantly enhancing usability and cross-platform compatibility. The resulting toolkit supports optimal sensor placement and seamless fusion of experimental measurements, thereby improving both understanding and computational efficiency in simulating complex multiphysics systems.
📝 Abstract
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 package is part of the ROSE (Reduced Order modelling with data-driven techniques for multi-phySics problEms): mathematical algorithms aimed at reducing the complexity of multi-physics models (for nuclear reactors applications), at searching for optimal sensor positions and at integrating real measures to improve the knowledge on the physical systems. With respect to the previous original implementation based on dolfinx package (v0.6.0), version 1.0.0 of pyforce has been completely re-written using pyvista as backend for mesh importing, computing integrals, and visualisation of results; in addition, functions are stored as numpy arrays, improving the ease of use of the package. This choice allows to use pyforce with any software solver able to export results in VTK format.