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Vanquishing the computational cost of passive gamma emission tomography simulations leveraging physics-aware reduced order modeling

Research output: Contribution to Journal/MagazineJournal articlepeer-review

  • Nicola Cavallini
  • Riccardo Ferretti
  • Gunnar Bostrom
  • Stephen Croft
  • Aurora Fassi
  • Giovanni Mercurio
  • Stefan Nonneman
  • Andrea Favalli
Article number15034
<mark>Journal publication date</mark>12/09/2023
<mark>Journal</mark>Scientific Reports
Issue number1
Publication StatusPublished
<mark>Original language</mark>English


Passive Gamma Emission Tomography (PGET) has been developed by the International Atomic Energy Agency to directly image the spatial distribution of individual fuel pins in a spent nuclear fuel assembly and determine potential diversion. The analysis and interpretation of PGET measurements rely on the availability of comprehensive datasets. Experimental data are expensive and limited, so Monte Carlo simulations are used to augment them. However, Monte Carlo simulations have a high computational cost to simulate the 360 angular views of the tomography. Similar challenges pervade numerical science. With the aim to create a large dataset of PGET simulated scenarios, we addressed the computational cost of Monte Carlo simulations by developing a physics-aware reduced order modeling approach. This approach combines a small subset of the 360 angular views (limited views approach) with a computationally inexpensive proxy solution (real-time forward model) that brings the essence of the physics to obtain a real-time high-fidelity solution at all angular views but at a fraction of the computational cost. The method’s ability to reconstruct 360 views with accuracy from a limited set of angular views is demonstrated by testing its performance for different types of reactor fuel assemblies.