pyforce: Python Framework data-driven model Order Reduction for multi-physiCs problEms
pyforce is a Python package implementing some Data-Driven Reduced Order Modelling (DDROM) techniques for applications to multi-physics problems, mainly set in the Nuclear Engineering world. These techniques have been implemented upon the dolfinx package (currently v0.6.0), part of the FEniCSx project, to handle mesh generation, integral calculation and functions storage. 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.
The techniques implemented here follow the same underlying idea expressed in the following figure: in the offline (training) phase, a dimensionality reduction process retrieves a reduced coordinate system onto which encodes the information of the mathematical model; the sensor positioning algorithm then uses this set to select the optimal location of sensors according to some optimality criterion, which depends on the adopted algorithm. In the online phase, the DA process begins, retrieving a novel set of reduced variables and then computing the reconstructed state through a decoding step.
At the moment, the following techniques have been implemented:
- Proper Orthogonal Decomposition with Projection and Interpolation for the Online Phase
- Generalised Empirical Interpolation Method, either regularised with Tikhonov or not
- Parameterised-Background Data-Weak formulation
- an Indirect Reconstruction algorithm to reconstruct non-observable fields
This package is aimed to be a valuable tool for other researchers, engineers, and data scientists working in various fields, not only restricted in the Nuclear Engineering world.
If you are going to use pyforce in your research work, please cite the following articles. The authors would be pleased if you could cite the relevant papers:
- Stefano Riva, Carolina Introini, and Antonio Cammi. Multi-physics model bias correction with data-driven reduced order modelling techniques: Application to nuclear case studies, 2024. arXiv:2401.07300.
- Antonio Cammi, Stefano Riva, Carolina Introini, Lorenzo Loi, and Enrico Padovani. Data-driven model order reduction for sensor positioning and indirect reconstruction with noisy data: Application to a circulating fuel reactor. Nuclear Engineering and Design, 421:113105, 2024. doi:https://doi.org/10.1016/j.nucengdes.2024.113105.
For LaTeX users:
@misc{RMP_2024,
title={Multi-Physics Model Bias Correction with Data-Driven Reduced Order Modelling Techniques: Application to Nuclear Case Studies},
author={Stefano Riva and Carolina Introini and Antonio Cammi},
year={2024},
eprint={2401.07300},
archivePrefix={arXiv},
primaryClass={math.NA}
}
@article{DDMOR_CFR,
title = {Data-driven model order reduction for sensor positioning and indirect reconstruction with noisy data: Application to a Circulating Fuel Reactor},
journal = {Nuclear Engineering and Design},
volume = {421},
pages = {113105},
year = {2024},
issn = {0029-5493},
doi = {https://doi.org/10.1016/j.nucengdes.2024.113105},
url = {https://www.sciencedirect.com/science/article/pii/S002954932400205X},
author = {Antonio Cammi and Stefano Riva and Carolina Introini and Lorenzo Loi and Enrico Padovani},
keywords = {Hybrid Data-Assimilation, Generalized Empirical Interpolation Method, Indirect Reconstruction, Sensors positioning, Molten Salt Fast Reactor, Noisy data},
}
- Antonio Cammi, Stefano Riva, Carolina Introini, Lorenzo Loi, and Enrico Padovani. Indirect Field Recon- struction and Sensor Positioning in Circulating Fuel Reactors using Data-Driven Model Order Reduction. In 2023 International Congress on Advances in Nuclear Power Plants, Gyeongju, Korea, April 2023.
- Stefano Riva, Carolina Introini, and Antonio Cammi. Multi-Physics Model Correction with Data-Driven Reduced Order Modelling. In 32nd International Conference Nuclear Energy for New Europe (NENE2023), Portoroz, Slovenia, September 2023.
- Stefano Riva, Sophie Deanesi, Carolina Introini, Stefano Lorenzi, and Antonio Cammi. Neutron Flux Re- construction from Out-Core Sparse Measurements using Data-Driven Reduced Order Modelling. In International Conference on Physics of Reactors (PHYSOR24), San Francisco, USA, April 2024.
The package can be installed using pip, make sure all the dependencies are installed (following these steps).
The requirements are listed here.
Clone the repository
git clone https://github.com/ROSE-Polimi/pyforce.gitChange directory to pyforce and install using pip
python -m pip install pyforce/The pyforce package is tested on some tutorials available in the docs, including fluid dynamics and neutronics problems.
- Laminar Flow over Cylinder (DFG2 benchmark): solved with dolfinx;
- Multi-Group Neutron Diffusion (ANL11-A2 benchmark): solved in dolfinx.
- Differentially Heated Cavity (buoyant Navier-Stokes): solved with OpenFOAM-6, as in ROM4FOAM tutorial.
Coming Soon: multiphysics (neutronics+thermal-hydraulics) with dolfinx and OpenFOAM.
pyforce is currently developed and mantained at Nuclear Reactors Group - ERMETE Lab by
- Stefano Riva
- Carolina Introini
under the supervision of Prof. Antonio Cammi.
If interested, please contact stefano.riva@polimi.it, carolina.introini@polimi.it, antonio.cammi@polimi.it