Abstract
This study presents an advance in predicting long-term stability in waste containment systems. Employing a surrogate model based on the 25 GEMS Python scripts, we simulate geochemical interactions within waste degradation processes over 100 years. The model simplifies complex full-scale geochemical models using a mixing tank approach, primarily examining the evolution of material properties influenced by uncertain surface characteristics and reaction kinetics. 1 million cases are generated using the neural network-based surrogate model, drastically reducing computational time (∼1.9 seconds) compared to traditional methods (∼78.4 days). The model evaluates the deterioration mechanisms of various materials like iron, aluminum, zinc, and brass, in cementitious waste packages, crucial for assessing their impact on the integrity of waste containment over extended periods.
Our findings with the neural network-based surrogate model, including ion concentrations and mass changes in materials like iron, brass, aluminum, and copper, offer detailed insights into chemical changes in the system. Incorporating a sensitivity analysis with 1 million cases generated by the surrogate model, the study underscores the interplay between chemical reactions and material properties, establishing a digital twin that links reaction rates to the stability of nuclear waste repositories. This study presents key indicators of potential integrity threats due to material expansion, contraction, or gas-induced pressure variations.
Our findings with the neural network-based surrogate model, including ion concentrations and mass changes in materials like iron, brass, aluminum, and copper, offer detailed insights into chemical changes in the system. Incorporating a sensitivity analysis with 1 million cases generated by the surrogate model, the study underscores the interplay between chemical reactions and material properties, establishing a digital twin that links reaction rates to the stability of nuclear waste repositories. This study presents key indicators of potential integrity threats due to material expansion, contraction, or gas-induced pressure variations.
Original language | English |
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Title of host publication | Proceedings of 2024 31st International Conference on Nuclear Engineering, ICONE 31 |
Subtitle of host publication | August 4-8, 2024, Prague, Czech Republic |
Publisher | American Society of Mechanical Engineers |
Number of pages | 7 |
Volume | 8 |
ISBN (Electronic) | 978-0-7918-8828-5 |
DOIs | |
Publication status | Published - 1 Nov 2024 |
Externally published | Yes |
Event | 2024 31st International Conference on Nuclear Engineering - Prague, Czech Republic Duration: 4 Aug 2024 → 8 Aug 2024 |
Conference
Conference | 2024 31st International Conference on Nuclear Engineering |
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Abbreviated title | ICONE31 |
Country/Territory | Czech Republic |
City | Prague |
Period | 4/08/24 → 8/08/24 |
Keywords
- surrogate model
- digital twin
- neural network
- geochemical processes
- cementitious waste package
- nuclear waste disposal
- Cementitious waste package
- Digital twin
- Surrogate model
- Geochemical processes
- Neural network
- Nuclear waste disposal