Development of empirical parameter network to enhance accident monitoring resilience

Pak, Sukyoung; Heo, Gyunyoung; Ahmed, Rizwan; Kim, Jung Taek

doi:10.1016/j.pnucene.2014.09.007

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…A capability to predict pollutants' pathways early in time with reasonable accuracy can assist emergency management in cases of accidental radioactivity release. An analogous study to predict temporal behaviour of system parameters during severe accidents at nuclear power plants using simulated data and machine learning algorithms has already been reported [21,22]. Recently, an online data-assimilation approach has been proposed to reconstruct source terms with dispersion employing a Kalman filter algorithm and mobile sensor data to support emergency management [23].…”

Section: Introductionmentioning

confidence: 99%

“…For the comparison and validation of results, the complete set of experimental data and RAMS simulation results have been adopted from reference [7]. For the assessment of off-site radiological impact, various nuclides are considered such as I-131, I-129, Kr-85, Ar-41, Cs-137, Xe-133, etc, depending upon their characteristics of physical form, activity, half-life, and deposition potential [17,18,[22][23][24]. We considered I-131 as a suitable nuclide for assessing the radiological consequences to the public, as it is highly volatile in nature, has short half-life, and has potential to reside and deliver doses to the thyroid.…”

Section: Introductionmentioning

confidence: 99%

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Real-time simulation of accidental passive transport of radioactive pollutant from a proposed nuclear power plant

Ahmad¹,

Muhammad²,

Ahmed³

et al. 2021

J. Radiol. Prot.

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Power plant’s site selection is a complex task and involves through analyses of multi-disciplinary processes which are interlinked with each other. The site selection for nuclear power plants additionally requires an assessment of radiation doses to the environment and public during normal operation and in the case of an accident. This demands the problem of radioactive particles’ dispersion in atmosphere to be analysed in real time for a comprehensive set of radioactive release scenarios in prevailing meteorological conditions in the plant surroundings. In this study, a local scale atmospheric dispersion problem, considering a hypothetical accidental release (1 Bq s−1 of I-131) from a nuclear power plant is simulated with a combination of weather forecasting and particle dispersion codes on a multiprocessor computer system. The meteorological parameters are predicted with a weather research and forecasting (WRF) model and used in Lagrangian particle dispersion model based code FLEXPART to calculate the trajectory of released particles, and thereby, the estimation of spatial I-131 dose distribution. The concentration of particles and radiation doses were calculated for release heights of 10, 57, and 107 m and found in a reasonable agreement with the observed data and better than an earlier investigation done with regional atmospheric modelling system (RAMS) code. A comparison between the results of WRF and RAMS for various meteorological parameters revealed that better space–time predictions of wind speeds and directions by WRF had a profound effect on tracing the trajectories of particles and thereby the spatial dose distribution. The particles followed the changes in wind direction predicted by WRF that were known to prevail in the region.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-time simulation of accidental passive transport of radioactive pollutant from a proposed nuclear power plant

Ahmad¹,

Muhammad²,

Ahmed³

et al. 2021

J. Radiol. Prot.

Self Cite

View full text Add to dashboard Cite

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Development of a prediction method for SAMG entry time in NPPs using the extended group method of data handling (GMDH) model

Lee

Seong

2018

Annals of Nuclear Energy

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Application of Multi Parameter Path Length Method for Resilience (MP-PLMR) to Engineering Systems

Prasad

Vinod

Andrews

2022

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

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Multi Parameter-Path length method for Resilience (MP-PLMR), has been proposed to determine the resilience of system multi-parameter considerations. It was applied to two engineering situations : (i) Passive catalytic device for hydrogen management in Nuclear Power Plant (NPP) (ii) Engineered systems for hydrogen mitigation in NPP. The method involves normalizations of the system parameters, the time domain and correlation coefficient across the parameters. The path length for the transient was defined using all the parameters and their correlations. The resilience value in the two case studies depended on the number of parameters considered and correlations. System resilience without the consideration of correlation was also estimated. The difference between the correlated and uncorrelated resilience was significant. While there is no established metric against which the calculated values could be compared, these values can be used to define system effectiveness in conjunction with reliability of systems.

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Development of empirical parameter network to enhance accident monitoring resilience

Cited by 3 publications

References 3 publications

Real-time simulation of accidental passive transport of radioactive pollutant from a proposed nuclear power plant

Real-time simulation of accidental passive transport of radioactive pollutant from a proposed nuclear power plant

Development of a prediction method for SAMG entry time in NPPs using the extended group method of data handling (GMDH) model

Application of Multi Parameter Path Length Method for Resilience (MP-PLMR) to Engineering Systems

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