Systematic hydrogen risk analysis of OPR1000 containment before RPV failure under station blackout scenario

Kim, Nam Kyung; Jeon, Joongoo; Choi, Wonjun; Kim, Sung Joong

doi:10.1016/j.anucene.2018.02.050

Cited by 13 publications

(4 citation statements)

References 14 publications

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“…The summation expressions of neural networks in Equations (8) to (10) can be simplified using matrix and vector notations, as shown in Equations ( 11) to (13). Another important motivation for vectorization is the acceleration of training/testing calculations.…”

Section: Training Process Of Multi-layer Perceptronmentioning

confidence: 99%

“…1,5 These physical constraints were confirmed by recent reaction flow simulations conducted by Tolias et al 4 Further, they identified that approximately 10 to 100 hours of CPU time per second of physical time was required to investigate unsteady hydrogen deflagration through CFD simulations based on a domain size of 20:0 Â 14:4 Â 12:0 m. 4 However, in terms of their commercial application to the nuclear or hydrogen production industry, these costly simulations are impractical, given that hour-based accident analysis is required in the regulatory guide. 8 To reduce the computational cost, this work focuses on the application of machine learning techniques, which are at the forefront as the most innovative technology. 9 Recently, Vinuesa et al highlighted the role of artificial intelligence in many sectors including the energy research field.…”

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confidence: 99%

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Finite volume method network for the acceleration of unsteady computational fluid dynamics: Non‐reacting and reacting flows

Jeon

Lee

Kim

2022

Intl J of Energy Research

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Summary Despite rapid improvements in the performance of the central processing unit (CPU), the calculation cost of simulating chemically reacting flow using CFD remains infeasible in many cases. The application of the convolutional neural networks (CNNs) specialized in image processing in flow field prediction has been studied, but the need to develop a neural network design fitted for CFD has recently emerged. In this study, a neural network model introducing the finite volume method (FVM) with unique network architecture and physics‐informed loss function was developed to accelerate CFD simulations. The developed network model, considering the nature of the CFD flow field where the identical governing equations are applied to all grids, can predict the future fields with only two previous fields unlike the CNNs requiring many field images (>10 000). The performance of this baseline model was evaluated using CFD time series data from non‐reacting flow and reacting flow simulation; counterflow and hydrogen flame with 20 detailed chemistries. Consequently, we demonstrated that (a) the FVM‐based network architecture provided significantly improved accuracy of multistep time series prediction compared to the previous MLP model (b) the physic‐informed loss function prevented non‐physical overfitting problem and ultimately reduced the error in time series prediction (c) observing the calculated residuals in an unsupervised manner could monitor the network accuracy. Additionally, under the reacting flow dataset, the computational speed of this network model was measured to be about 10 times faster than that of the CFD solver.

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Section: Training Process Of Multi-layer Perceptronmentioning

confidence: 99%

mentioning

confidence: 99%

Finite volume method network for the acceleration of unsteady computational fluid dynamics: Non‐reacting and reacting flows

Jeon

Lee

Kim

2022

Intl J of Energy Research

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“…The nodalization of containment was based on the compartment configuration of the MAAP-CONTAIN analysis [4]. Figure 3 shows the nodalization of containment.…”

Section: Melcor Input Model Of Opr1000mentioning

confidence: 99%

“…Faithful to the Defence-in-Depth philosophy, a containment building is designed and constructed as the last barrier to prevent the fission product release under severe accident (SA) scenarios [1]. Many researchers have studied hazardous factor such as direct containment heating [2], steam explosion [3], hydrogen combustion [4], and overpressure [5]. These studies enhance reliability of the containment building.…”

Section: Introductionmentioning

confidence: 99%

A Conceptual Approach to Eliminate Bypass Release of Fission Products by In-Containment Relief Valve under SGTR Accident

Kim

Choi

Jeon

et al. 2018

Science and Technology of Nuclear Installations

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During a hypothesized severe accident, a containment building is designed to act as a final barrier to prevent release of fission products to the environment in nuclear power plants. However, in a bypass scenario of steam generator tube rupture (SGTR), radioactive nuclides can be released to environment even if the containment is not ruptured. Thus, thorough mitigation strategies are needed to prevent such unfiltered release of the radioactive nuclides during SGTR accidents. To mitigate the consequence of the SGTR accident, this study was conducted to devise a conceptual approach of installing In-Containment Relief Valve (ICRV) from steam generator (SG) to the free space in the containment building and it was simulated by MELCOR code for numerical analysis. Simulation results show that the radioactive nuclides were not released to the environment in the ICRV case. However, the containment pressure increased more than the base case, which is a disadvantage of the ICRV. To minimize the negative effects of the ICRV, the ICRV linked to Reactor Drain Tank (RDT) and cavity flooding was performed. Because the overpressurization of containment is due to heat of ex-vessel corium, only cavity flooding was effective for depressurization. The conceptual design of the ICRV is effective in mitigating the SGTR accident.

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Optimization of hydrogen mitigation systems in CPR1000 during a refueling outage

Huang

Miao

Zheng

et al. 2019

Annals of Nuclear Energy

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Systematic hydrogen risk analysis of OPR1000 containment before RPV failure under station blackout scenario

Cited by 13 publications

References 14 publications

Finite volume method network for the acceleration of unsteady computational fluid dynamics: Non‐reacting and reacting flows

Finite volume method network for the acceleration of unsteady computational fluid dynamics: Non‐reacting and reacting flows

A Conceptual Approach to Eliminate Bypass Release of Fission Products by In-Containment Relief Valve under SGTR Accident

Optimization of hydrogen mitigation systems in CPR1000 during a refueling outage

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