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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2303.04675 |
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| _version_ | 1866913862516736000 |
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| author | Cavallini, Nicola Ferretti, Riccardo Bostrom, Gunnar Croft, Stephen Fassi, Aurora Mercurio, Giovanni Nonneman, Stefan Favalli, Andrea |
| author_facet | Cavallini, Nicola Ferretti, Riccardo Bostrom, Gunnar Croft, Stephen Fassi, Aurora Mercurio, Giovanni Nonneman, Stefan Favalli, Andrea |
| contents | Passive Gamma Emission Tomography (PGET) has been developed by the International Atomic Energy Agency as a way to directly image the spatial distribution of individual fuel pins in a spent nuclear fuel assembly and so determine potential diversion.
Constructing the analysis and interpretation of PGET measurements rely on the availability of comprehensive datasets. Experimental data are expensive, limited, and so are augmented by Monte Carlo simulations. The main issue concerning Monte Carlo simulations is the high computational cost to simulate the 360 angular views of the tomography. Similar challenges pervade numerical science.
To address this challenge, we have developed a physics-aware reduced order modeling approach. It provides a framework to combine a small subset of the 360 angular views with a computationally inexpensive proxy solution, 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_04675 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Vanquishing the computational cost of passive gamma emission tomography simulations: a physics-aware reduced order modeling approach Cavallini, Nicola Ferretti, Riccardo Bostrom, Gunnar Croft, Stephen Fassi, Aurora Mercurio, Giovanni Nonneman, Stefan Favalli, Andrea Numerical Analysis Computational Physics Passive Gamma Emission Tomography (PGET) has been developed by the International Atomic Energy Agency as a way to directly image the spatial distribution of individual fuel pins in a spent nuclear fuel assembly and so determine potential diversion. Constructing the analysis and interpretation of PGET measurements rely on the availability of comprehensive datasets. Experimental data are expensive, limited, and so are augmented by Monte Carlo simulations. The main issue concerning Monte Carlo simulations is the high computational cost to simulate the 360 angular views of the tomography. Similar challenges pervade numerical science. To address this challenge, we have developed a physics-aware reduced order modeling approach. It provides a framework to combine a small subset of the 360 angular views with a computationally inexpensive proxy solution, 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. |
| title | Vanquishing the computational cost of passive gamma emission tomography simulations: a physics-aware reduced order modeling approach |
| topic | Numerical Analysis Computational Physics |
| url | https://arxiv.org/abs/2303.04675 |