Coupling one-dimensional user code and STAR-CCM+ for simulation of rolling conditions in a rod bundle channel

Zhang, Yinxing; Gao, Puzhen; Tian, Canrong; He, Xiaoqiang; Chen, Chong; Wang, Zhongyi

doi:10.1016/j.anucene.2020.107888

Cited by 7 publications

(1 citation statement)

References 29 publications

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“…It is also employed to model natural circulation flow in the PWR fuel (Ni et al, 2021), estimation of the countercurrent flow in the downcomer (Li et al, 2023a), analysis of loss of flow accidents (Corzo et al, 2023), and study rod ejection accidents (El-Sahlamy et al, 2022). Further, the NPPs data is generated from the STAR-CCM+ CFD simulation tool for several applications (Marfaing et al, 2018;Benavides et al, 2020;Zhang et al, 2021;Yang et al, 2023). Besides, the big data of the NPPs could be collected from education and training simulators.…”

Section: Software Data Sourcementioning

confidence: 99%

Application of artificial intelligence technologies and big data computing for nuclear power plants control: a review

Ejigu,

Tuo,

Liu

2024

Front. Nucl. Eng.

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Nuclear power plants produce a massive amount of clean energy and necessitate safe operation through intelligence technologies. Recently, the rapid advancements in communication infrastructures including artificial intelligence, big data computing, and Internet of Things devices moving the nuclear industries towards digitalization and intelligence to improve safety. The integration of these technologies into the nuclear sector offers effective tactics in addressing several challenges in the control and safe operation of nuclear power plants. This can be achieved through the insights generated from massive amounts of data. This paper comprehensively reviews the literature on artificial intelligence technologies and big data, seeking to provide a holistic perspective on their relations and how they can be integrated with nuclear power plants. The utilization of computing platforms boosts the deployment of artificial intelligence and big data analytics effectively in nuclear power plants. Further, this review also points out the future opportunities as well as challenges for applying artificial intelligence and big data computing in the nuclear industry.

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