A decision-support approach to severe accident management in nuclear power plants

Bohanec, Marko; Vrbanić, Ivan; Bašić, Ivica; Debelak, Klemen; Štrubelj, Luka

doi:10.1080/12460125.2020.1854426

Cited by 9 publications

(1 citation statement)

References 4 publications

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“…In the study [7], a model of changes in territory pollution after sedimentation of emission products was developed. The paper [8] defines the risks of emission at a nuclear power plant with recommendations for decision-making in the event of accidents. At the same time, the dynamics of the development of accidents is not taken into account.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Distribution of Emergency Release Products at a Nuclear Power Plant Unit

Biliaieva,

Levchenko,

Myshchenko

et al. 2024

A.I.S.

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Despite the fact that much attention is paid to the safe operation of nuclear power plants, there is a possibility of an accident with the release of radionuclides. This is especially true in Ukraine, where there is a threat of the damage to nuclear reactors as a result of military operations. It is impossible to research the distribution of products emergency releases radioactive substances in laboratory conditions. Therefore, the only tool for the development predicting of an accident is the modeling the spread of a radionuclides cloud. The purpose of the research is a modeling the distribution of emergency release products in a nuclear power plant unit, suitable for the operative assessment of a development an accident. Results of the research: The mathematical model of the distribution emission products of a nuclear power plant has been developed, which takes into account the value of the initial activity of emission products, the rate of the settling radioactive particles, the wind speed components, the intensity changes radionuclide emission over time. The technique for solving the boundary value problem of modeling in conditions of a complex shape of the computational domain, taking into account the presence of obstacles to the spread of emission products has been developed. The use of the velocity potential equation in evolutionary form allows us to speed up the calculation process. The chosen splitting scheme of an alternating-triangular method allows to find the speed potential according to the explicit form at each splitting step. This allowed software implementation of the CFD model. The visualized models of the emission cloud distribution allow to determine the radiation situation in any place of the emission product distribution zone. The developed model makes it possible to quickly predict the development of an accident in space and time, which makes it possible to take measures to protect people from exposure in the shortest possible time. Conclusions: The obtained emission cloud propagation models and their visualization make it possible to determine the state of environmental pollution under various initial conditions during the development of the accident.

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Section: Introductionmentioning

confidence: 99%

Modeling the Distribution of Emergency Release Products at a Nuclear Power Plant Unit

Biliaieva,

Levchenko,

Myshchenko

et al. 2024

A.I.S.

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Research on Process Diagnosis of Severe Accidents Based on Deep Learning and Probabilistic Safety Analysis

Liu¹,

Wang²

2023

Springer Proceedings in Physics

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Severe accident process diagnosis provides data basis for severe accident prognosis, positive and negative effect evaluation of Severe Accident Management Guidelines (SAMGs), especially to quickly diagnose Plant Damage State (PDS) for operators in the main control room or personnel in the Technical Support Center (TSC) based on historic data of the limited number of instruments during the operation transition from Emergency Operation Procedures (EOPs) to SAMGs. This diagnosis methodology is based on tens of thousands of simulations of severe accidents using the integrated analysis program MAAP. The simulation process is organized in reference to Level 1 Probabilistic Safety Analysis (L1 PSA) and EOPs. According to L1 PSA, the initial event of accidents and scenarios from the initial event to core damage are presented in Event Trees (ET), which include operator actions following up EOPs. During simulation, the time uncertainty of operations in scenarios is considered. Besides the big data collection of simulations, a deep learning algorithm, Convolutional Neural Network (CNN), has been used in this severe accident diagnosis methodology, to diagnose the type of severe accident initiation event, the breach size, breach location, and occurrence time of the initial event of LOCA, and action time by operators following up EOPs intending to take Nuclear Power Plant (NPP) back to safety state. These algorithms train classification and regression models with ET-based numerical simulations, such as the classification model of sequence number, break location, and regression model of the break size and occurrence time of initial event MBLOCA. Then these trained models take advantage of historic data from instruments in NPP to generate a diagnosis conclusion, which is automatically written into an input deck file of MAAP. This input deck originated from previous traceback efforts and provides a numerical analysis basis for predicting the follow-up process of a severe accident, which is conducive to severe accident management. Results of this paper show a theoretical possibility that under limited available instruments, this traceback and diagnosis method can automatically and quickly diagnose PDS when operation transit from EOPs to SAMGs and provide numerical analysis basis for severe accident process prognosis.

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Implementing nuclear power plants (NPPs): state of the art, challenges, and opportunities

Elhegazy

Kamal

2021

Innov. Infrastruct. Solut.

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A decision-support approach to severe accident management in nuclear power plants

Cited by 9 publications

References 4 publications

Modeling the Distribution of Emergency Release Products at a Nuclear Power Plant Unit

Modeling the Distribution of Emergency Release Products at a Nuclear Power Plant Unit

Research on Process Diagnosis of Severe Accidents Based on Deep Learning and Probabilistic Safety Analysis

Implementing nuclear power plants (NPPs): state of the art, challenges, and opportunities

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