Virtual sensing-enabled digital twin framework for real-time monitoring of nuclear systems leveraging deep neural operators
Abstract Real-time monitoring is a foundation of nuclear digital twin technology, crucial for detecting material degradation and maintaining nuclear system integrity. Traditional physical sensor systems face limitations, particularly in measuring critical parameters in hard-to-reach or harsh environ...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | npj Materials Degradation |
| Online Access: | https://doi.org/10.1038/s41529-025-00557-y |
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| author | Raisa Hossain Farid Ahmed Kazuma Kobayashi Seid Koric Diab Abueidda Syed Bahauddin Alam |
| author_facet | Raisa Hossain Farid Ahmed Kazuma Kobayashi Seid Koric Diab Abueidda Syed Bahauddin Alam |
| author_sort | Raisa Hossain |
| collection | DOAJ |
| description | Abstract Real-time monitoring is a foundation of nuclear digital twin technology, crucial for detecting material degradation and maintaining nuclear system integrity. Traditional physical sensor systems face limitations, particularly in measuring critical parameters in hard-to-reach or harsh environments, often resulting in incomplete data coverage. Machine learning-driven virtual sensors offer a transformative solution by complementing physical sensors in monitoring critical degradation indicators. This paper introduces the use of Deep Operator Networks (DeepONet) to predict key thermal-hydraulic parameters in the hot leg of pressurized water reactor. DeepONet acts as a virtual sensor, mapping operational inputs to spatially distributed system behaviors without requiring frequent retraining. Our results show that DeepONet achieves low mean squared and Relative L2 error, making predictions 1400 times faster than traditional CFD simulations. These characteristics enable DeepONet to function as a real-time virtual sensor, synchronizing with the physical system to track degradation conditions and provide insights within the digital twin framework for nuclear systems. |
| format | Article |
| id | doaj-art-67946d3d69ac42539bdb15ca74ac4cb0 |
| institution | DOAJ |
| issn | 2397-2106 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Materials Degradation |
| spelling | doaj-art-67946d3d69ac42539bdb15ca74ac4cb02025-08-20T02:47:07ZengNature Portfolionpj Materials Degradation2397-21062025-03-019111410.1038/s41529-025-00557-yVirtual sensing-enabled digital twin framework for real-time monitoring of nuclear systems leveraging deep neural operatorsRaisa Hossain0Farid Ahmed1Kazuma Kobayashi2Seid Koric3Diab Abueidda4Syed Bahauddin Alam5The Grainger College of Engineering, Nuclear, Plasma & Radiological Engineering, University of Illinois Urbana-ChampaignThe Grainger College of Engineering, Nuclear, Plasma & Radiological Engineering, University of Illinois Urbana-ChampaignThe Grainger College of Engineering, Nuclear, Plasma & Radiological Engineering, University of Illinois Urbana-ChampaignNational Center for Supercomputing Applications, University of Illinois Urbana-ChampaignNational Center for Supercomputing Applications, University of Illinois Urbana-ChampaignThe Grainger College of Engineering, Nuclear, Plasma & Radiological Engineering, University of Illinois Urbana-ChampaignAbstract Real-time monitoring is a foundation of nuclear digital twin technology, crucial for detecting material degradation and maintaining nuclear system integrity. Traditional physical sensor systems face limitations, particularly in measuring critical parameters in hard-to-reach or harsh environments, often resulting in incomplete data coverage. Machine learning-driven virtual sensors offer a transformative solution by complementing physical sensors in monitoring critical degradation indicators. This paper introduces the use of Deep Operator Networks (DeepONet) to predict key thermal-hydraulic parameters in the hot leg of pressurized water reactor. DeepONet acts as a virtual sensor, mapping operational inputs to spatially distributed system behaviors without requiring frequent retraining. Our results show that DeepONet achieves low mean squared and Relative L2 error, making predictions 1400 times faster than traditional CFD simulations. These characteristics enable DeepONet to function as a real-time virtual sensor, synchronizing with the physical system to track degradation conditions and provide insights within the digital twin framework for nuclear systems.https://doi.org/10.1038/s41529-025-00557-y |
| spellingShingle | Raisa Hossain Farid Ahmed Kazuma Kobayashi Seid Koric Diab Abueidda Syed Bahauddin Alam Virtual sensing-enabled digital twin framework for real-time monitoring of nuclear systems leveraging deep neural operators npj Materials Degradation |
| title | Virtual sensing-enabled digital twin framework for real-time monitoring of nuclear systems leveraging deep neural operators |
| title_full | Virtual sensing-enabled digital twin framework for real-time monitoring of nuclear systems leveraging deep neural operators |
| title_fullStr | Virtual sensing-enabled digital twin framework for real-time monitoring of nuclear systems leveraging deep neural operators |
| title_full_unstemmed | Virtual sensing-enabled digital twin framework for real-time monitoring of nuclear systems leveraging deep neural operators |
| title_short | Virtual sensing-enabled digital twin framework for real-time monitoring of nuclear systems leveraging deep neural operators |
| title_sort | virtual sensing enabled digital twin framework for real time monitoring of nuclear systems leveraging deep neural operators |
| url | https://doi.org/10.1038/s41529-025-00557-y |
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