Modeling and results of the dry cask simulator (DCS) with STAR-CCM+

Benavides, Julio; Varas, Gonzalo Jiménez; López, Santiago; Galban, Marta; LLoret, Miriam

doi:10.1016/j.nucengdes.2020.110820

Cited by 5 publications

(2 citation statements)

References 15 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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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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“…Moreover, heat removal tests were conducted in Japan on two types of full-scale concrete casks, taking into account different decay heat values [13]. Data from these experiments have been employed in several works [14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty and Sensitivity Analysis of a Dry Cask for Spent Nuclear Fuel

et al. 2022

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Nuclear safety relies to a good extent on thoroughly validated codes. However, code predictions are affected by uncertainties that need to be quantified for a more accurate evaluation of safety margins. In this regard, the present paper proposes a preliminary uncertainty and sensitivity analysis of the thermal behavior of a concrete-based dry cask for spent nuclear fuel storage, employing the MELCOR code and a series of MATLAB scripts. As thermal behavior is of utmost importance for the fulfillment of United States Nuclear Regulatory Commission (USNRC) safety requirements, the Peak Cladding Temperature (PCT) has been addressed as the key Figure of Merit (FOM). Variables related to the main heat transfer mechanisms have been selected as input parameters for the uncertainty quantification, whereas heat source and heat sink, namely decay power and external air temperature, have been dealt with in a separate sensitivity analysis. The results show that the selected parameters have a weak influence on the PCT, whereas it is strongly related to the decay power and external air temperature values. In any case, PCT stays below the regulatory threshold even under the considered off-normal conditions.

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