Summary of Predicting Bwr Criticality with Data-driven Machine Learning Model, by Muhammad Rizki Oktavian et al.
Predicting BWR Criticality with Data-Driven Machine Learning Model
by Muhammad Rizki Oktavian, Anirudh Tunga, Jonathan Nistor, James Tusar, J. Thomas Gruenwald, Yunlin Xu
First submitted to arxiv on: 11 Nov 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 A novel approach using a data-driven deep learning model is proposed to estimate the excess criticality of a boiling water reactor, addressing the challenge of optimizing fuel usage in nuclear power plants. The model aims to predict the amount of fuel needed for a cycle, ensuring efficient operation and minimizing economic losses due to early coastdown or wasted excess reactivity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed an innovative way to decide how much fuel is needed in a nuclear power plant. They created a special computer program that uses big data to predict when the reactor will have too much or not enough fuel. This helps keep the plant running smoothly and saves money by using just the right amount of fuel. |
Keywords
* Artificial intelligence * Deep learning
