Summary of Virtual Sensing-enabled Digital Twin Framework For Real-time Monitoring Of Nuclear Systems Leveraging Deep Neural Operators, by Raisa Bentay Hossain et al.
Virtual Sensing-Enabled Digital Twin Framework for Real-Time Monitoring of Nuclear Systems Leveraging Deep Neural Operators
by Raisa Bentay Hossain, Farid Ahmed, Kazuma Kobayashi, Seid Koric, Diab Abueidda, Syed Bahauddin Alam
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research introduces the use of Deep Operator Networks (DeepONet) as a core component of a digital twin framework for predicting key thermal-hydraulic parameters in the hot leg of an AP-1000 Pressurized Water Reactor (PWR). The authors aim to address the limitations of traditional physical sensor systems by developing machine learning-driven virtual sensors that can monitor critical degradation indicators like pressure, velocity, and turbulence. Conventional machine learning models struggle with real-time monitoring due to high-dimensional data and frequent retraining requirements. This study demonstrates the potential of DeepONet as a dynamic and scalable virtual sensor that accurately maps the interplay between operational input parameters and spatially distributed system behaviors. The results show accurate predictions with low mean squared error and relative L2 error, and the ability to make predictions on unknown data 1400 times faster than traditional CFD simulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers are trying to solve a big problem in nuclear power plants. They need to find a way to detect when materials start to degrade, so they can fix it before something bad happens. Right now, they use sensors that can be tricky to install and don’t always give them the right information. They want to create a new kind of sensor that uses machine learning to predict what’s going on inside the plant. This new sensor is called DeepONet, and it’s really good at predicting what will happen in the future based on what’s happening now. It can even make predictions 1400 times faster than usual! This technology has the potential to make nuclear power plants safer and more efficient. |
Keywords
* Artificial intelligence * Machine learning
