Constrained optimization of sensor placement for nuclear digital twins
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866913236019838976 |
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| author | Karnik, Niharika Abdo, Mohammad G. Perez, Carlos E. Estrada Yoo, Jun Soo Cogliati, Joshua J. Skifton, Richard S. Calderoni, Pattrick Brunton, Steven L. Manohar, Krithika |
| author_facet | Karnik, Niharika Abdo, Mohammad G. Perez, Carlos E. Estrada Yoo, Jun Soo Cogliati, Joshua J. Skifton, Richard S. Calderoni, Pattrick Brunton, Steven L. Manohar, Krithika |
| contents | The deployment of extensive sensor arrays in nuclear reactors is infeasible due to challenging operating conditions and inherent spatial limitations. Strategically placing sensors within defined spatial constraints is essential for the reconstruction of reactor flow fields and the creation of nuclear digital twins. We develop a data-driven technique that incorporates constraints into an optimization framework for sensor placement, with the primary objective of minimizing reconstruction errors under noisy sensor measurements. The proposed greedy algorithm optimizes sensor locations over high-dimensional grids, adhering to user-specified constraints. We demonstrate the efficacy of optimized sensors by exhaustively computing all feasible configurations for a low-dimensional dynamical system. To validate our methodology, we apply the algorithm to the Out-of-Pile Testing and Instrumentation Transient Water Irradiation System (OPTI-TWIST) prototype capsule. This capsule is electrically heated to emulate the neutronics effect of the nuclear fuel. The TWIST prototype that will eventually be inserted in the Transient Reactor Test facility (TREAT) at the Idaho National Laboratory (INL), serves as a practical demonstration. The resulting sensor-based temperature reconstruction within OPTI-TWIST demonstrates minimized error, provides probabilistic bounds for noise-induced uncertainty, and establishes a foundation for communication between the digital twin and the experimental facility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_13637 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Constrained optimization of sensor placement for nuclear digital twins Karnik, Niharika Abdo, Mohammad G. Perez, Carlos E. Estrada Yoo, Jun Soo Cogliati, Joshua J. Skifton, Richard S. Calderoni, Pattrick Brunton, Steven L. Manohar, Krithika Optimization and Control Machine Learning The deployment of extensive sensor arrays in nuclear reactors is infeasible due to challenging operating conditions and inherent spatial limitations. Strategically placing sensors within defined spatial constraints is essential for the reconstruction of reactor flow fields and the creation of nuclear digital twins. We develop a data-driven technique that incorporates constraints into an optimization framework for sensor placement, with the primary objective of minimizing reconstruction errors under noisy sensor measurements. The proposed greedy algorithm optimizes sensor locations over high-dimensional grids, adhering to user-specified constraints. We demonstrate the efficacy of optimized sensors by exhaustively computing all feasible configurations for a low-dimensional dynamical system. To validate our methodology, we apply the algorithm to the Out-of-Pile Testing and Instrumentation Transient Water Irradiation System (OPTI-TWIST) prototype capsule. This capsule is electrically heated to emulate the neutronics effect of the nuclear fuel. The TWIST prototype that will eventually be inserted in the Transient Reactor Test facility (TREAT) at the Idaho National Laboratory (INL), serves as a practical demonstration. The resulting sensor-based temperature reconstruction within OPTI-TWIST demonstrates minimized error, provides probabilistic bounds for noise-induced uncertainty, and establishes a foundation for communication between the digital twin and the experimental facility. |
| title | Constrained optimization of sensor placement for nuclear digital twins |
| topic | Optimization and Control Machine Learning |
| url | https://arxiv.org/abs/2306.13637 |