MOSAIQUE – A network based software for probabilistic uncertainty analysis of computerized simulation models

Lim, Ho-Gon; Han, Sanghoon; Jeong, Jae Jun

doi:10.1016/j.nucengdes.2011.01.021

Cited by 18 publications

(7 citation statements)

References 8 publications

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“…Information on this incident is available in OPIS (operational performance information system for nuclear power plant), which is managed by the Korea Institute of Nuclear Safety (KINS) [42]. Te MARS (multidimensional analysis of reactor safety) [43] and MOSAIQUE [25] were used to quantify the total conditional core damage probability (CCDP).…”

Section: Asp Analysis Of a Steam Generator Tube Rupture Eventmentioning

confidence: 99%

“…MCDET (Monte Carlo dynamic event tree) enables the proper use of the Monte Carlo method and DDET [15,16]. Tools such as ADAPT (analysis of dynamic accident progression trees) [17,18], SCAIS (simulation code system for integrated safety assessment) [19,20], PyCATSOO (Pythonic object-oriented hybrid stochastic automata) [21,22], DICE (dynamic integrated consequence evaluation) [23,24], MOSAIQUE (module for sampling input and quantifying estimator) [25,26] have also been developed.…”

Section: Introductionmentioning

confidence: 99%

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Accident Sequence Precursor Analysis of an Incident in a Japanese Nuclear Power Plant Based on Dynamic Probabilistic Risk Assessment

Kubo

2023

Science and Technology of Nuclear Installations

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Probabilistic risk assessment (PRA) is an effective methodology that could be used to improve the safety of nuclear power plants in a reasonable manner. Dynamic PRA, as an advanced PRA, allows for more realistic and detailed analyses by handling time-dependent information. However, the applications of this method to practical problems are limited because it remains in the research and development stage. This study aimed to investigate the possibility of utilizing dynamic PRA in risk-informeddecision-making. Specifically, the author performed an accident sequence precursor (ASP) analysis on the failure of emergency diesel generators that occurred at Unit 1 of the Tomari Nuclear Power Plant in Japan using dynamic PRA. The results were evaluated by comparison with the results of simplified classical PRA. The findings indicated that dynamic PRA may estimate lower risks compared with those obtained from classical PRA by reasonable modeling of alternating current power recovery. The author also showed that dynamic PRA can provide detailed information that cannot be obtained with classical PRA, such as uncertainty distribution of core damage timing and importance measure considering the system failure timing.

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Section: Asp Analysis Of a Steam Generator Tube Rupture Eventmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Accident Sequence Precursor Analysis of an Incident in a Japanese Nuclear Power Plant Based on Dynamic Probabilistic Risk Assessment

Kubo

2023

Science and Technology of Nuclear Installations

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“…To obtain probability density functions of the key variables, [44] used main control room simulator data, fault tree models with the failure rates of the components, and expert judgements. Following this approach, the probability density functions of the four variables except the variable 'break size', were determined as follows, and are plotted in Monte Carlo sampling and multiple TH simulations were supported by MOSAIQUE code [45].…”

Section: A Securing Refined Data For Deep Learningmentioning

confidence: 99%

Application of a Deep Learning Technique to the Development of a Fast Accident Scenario Identifier

2020

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To obtain more accurate results of probabilistic safety assessment (PSA), it is necessary to reflect more complete dynamics of nuclear power plants. In analyzing these more realistic PSA models, numerous thermal-hydraulic code runs should be performed that typically take from a few minutes to several hours. This paper proposes a fast running model using deep learning techniques to obtain plausible accident scenarios while reducing the resources required to conduct PSA. The developed model is built from a conditional autoencoder, and an analysis of its performance is carried out under both trained and untrained ranges. Taking about one second per scenario, the developed model shows about 0.4% and 1.6% error in the trained and untrained ranges, respectively. As a feasibility study, the aggressive cooldown operation under a small break loss-of-coolant accident in the APR1400 plant was considered. The proposed method can reduce uncertainty in PSA and contribute a key technique to dynamic PSA.

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“…19 Overall, simulation-based and dynamic risk assessment can be used to evaluate the risks associated with external events in detail. Methods and tools for dynamic PRA have been developed by many organizations; for example, ADS-IDAC (Accident Dynamic Simulator Coupled with the Information, Decision, and Action in a Crew Context), 20 PyCATSHOO (PythoniC Object Oriented Hybrid Stochastic AuTomata), 21 SCAIS (Simulation Codes System for Integrated Safety Assessment), 22 CMMC (Continuous Markov Chain and Monte Carlo), 23 GO-FLOW, which is a success-oriented system analysis technique, 24 MOSAIQUE (Module for Sampling Input and Quantifying Estimator), 25 and DICE (Dynamic Integrated Consequence Evaluation). 26 The Japan Atomic Energy Agency (JAEA) has developed an integrated severe accident code, THALES2 (Thermal-Hydraulic Analysis of Loss of Coolant, Emergency Core Cooling, and Severe Core Damage, version 2), 27 which is used to measure the chemical behaviors of fission products during severe accident.…”

Section: Introductionmentioning

confidence: 99%

Simulation-based dynamic probabilistic risk assessment of an internal flooding-initiated accident in nuclear power plant using THALES2 and RAPID

Kubo

Zheng

Tanaka

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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Probabilistic risk assessment (PRA) is a method used to assess the risks associated with large and complex systems. However, the timing at which nuclear power plant structures, systems, and components are damaged is difficult to handle explicitly if the risk of an external event is evaluated using conventional PRA based on event trees and fault trees. A methodology coupling thermal-hydraulic analysis with external event simulations using Risk Assessment with Plant Interactive Dynamics (RAPID) is therefore proposed to overcome this limitation. A flood propagation model based on Bernoulli’s theorem was applied to represent internal flooding in the turbine building of the pressurized water reactor. Uncertainties were also taken into account, including the flow rate of the floodwater source and the failure criteria for the mitigation systems. The simulated recovery actions included the operator isolating the floodwater source and using a drainage pump; these actions were modeled using several simplifications. Overall, the results indicate that combining isolation and drainage can reduce the conditional core damage probability upon the occurrence of flooding by approximately 90%.

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MOSAIQUE – A network based software for probabilistic uncertainty analysis of computerized simulation models

Cited by 18 publications

References 8 publications

Accident Sequence Precursor Analysis of an Incident in a Japanese Nuclear Power Plant Based on Dynamic Probabilistic Risk Assessment

Accident Sequence Precursor Analysis of an Incident in a Japanese Nuclear Power Plant Based on Dynamic Probabilistic Risk Assessment

Application of a Deep Learning Technique to the Development of a Fast Accident Scenario Identifier

Simulation-based dynamic probabilistic risk assessment of an internal flooding-initiated accident in nuclear power plant using THALES2 and RAPID

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