<p dir="ltr">The research focuses on improving core simulation procedures in Boiling Water Reactors (BWRs) by leveraging machine learning techniques. Aimed at better fuel planning and enhanced safety, a machine learning model has been developed to predict errors in existing low-fidelity, diffusion-based core simulators. The machine learning models have demonstrated the capability to accurately and efficiently predict errors in core eigenvalue and power distribution in BWR Operations. This results in a significant improvement over conventional simulation methods in nuclear reactors without increasing computational complexity.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/24302449 |
| Date | 13 October 2023 |
| Creators | Muhammad Rizki Oktavian (17138800) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/DEVELOPMENT_OF_A_MACHINE_LEARNING-ASSISTED_CORE_SIMULATION_FOR_BOILING_WATER_REACTOR_OPERATIONS/24302449 |
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