Advanced method for source term estimation and status of its integration in JRODOS

Kovalets, Ivan V.; Andronopoulos, S.; Hofman, Radek; Seibert, Petra; Ievdin, I.

doi:10.1051/radiopro/2016046

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…Moreover, to regularize the inverse problem, the source term's initial projections, often derived from bottom-up models, are utilized. A successful application of this approach in modeling nuclear disasters has been adequately documented by Kovalets et al [7]. Due to their lack of real-time dimension, previous implementations were considered less effective.…”

Section: Source Inversion Algorithmsmentioning

confidence: 99%

Applications of Machine Learning to Consequence Analysis of Hypothetical Accidents at Barakah Nuclear Power Plant Unit 1

Almteiri¹,

Kim²

2022

Energies

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The United Arab Emirates (UAE) built four nuclear power plants at the Barakah site to supply 25% of the region’s electricity. Among the Barakah Nuclear Power Plants, (BNPPs), their main objectives are to achieve the highest possible safety for the environment, operators, and community members; quality nuclear reactors and energy; and power production efficiency. To meet these objectives, decision-makers must access large amounts of data in the case of a nuclear accident to prevent the release of radioactive materials. Machine learning offers a feasible solution to propose early warnings and help contain accidents. Thus, our study aimed at developing and testing a machine learning model to classify nuclear accidents using the associated release of radioactive materials. We used Radiological Assessment System for Consequence Analysis (RASCAL) software to estimate the concentration of released radioactive materials in the four seasons of the year 2020. We applied these concentrations as predictors in a classification tree model to classify three types of severe accidents at Unit 1 of BNPPs each season. The average accuracy of the classification models in the four seasons was 97.3% for the training data and 96.5% for the test data, indicating a high efficacy. Thus, the generated classification models can distinguish between the three simulated accidents in any season.

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Section: Source Inversion Algorithmsmentioning

confidence: 99%

Applications of Machine Learning to Consequence Analysis of Hypothetical Accidents at Barakah Nuclear Power Plant Unit 1

Almteiri¹,

Kim²

2022

Energies

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“…Raskob et al (2016) discussed the capabilities and functions of JRODOS and discussed its applicability at national, regional and international level and during the various stages of an emergency. Kovalets et al (2016) proposed the integration of an algorithm into JRODOS to estimate the source term that is based on gamma dose rate measurements around the facility. Nanba et al (2016) andZheleznyak et al (2014) presented the integration of a hydrological dispersion module into the JRODOS to predict the transport of radionuclides in reservoirs, rivers and coastal waters.…”

Section: Application Of Dss In Emergency Preparedness and Responsementioning

confidence: 99%

JRODOS customization for Pakistan and assessment of the consequences of a hypothetical nuclear accident

Hussain¹,

Mehboob²,

Ilyas³

et al. 2023

J. Radiol. Prot.

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Since the Chernobyl disaster in 1986, decision support systems and modelling tools are utilized in response to nuclear and radiological emergencies. The Java-based Real-time On-line Decision Support System (JRODOS) is a decision support tool that can be utilised in response to an emergency in managing off-site radiological consequences. This article documents the customization and use of JRODOS for Pakistan. The JRODOS was tailored to the local Pakistan conditions, and a case study for a theoretical nuclear power plant accident was used to assess JRODOS's feasibility as a decision support tool. A worse case probabilistic accident scenario was used to identify zones and areas where urgent protective actions, early protective actions and food restriction and other response actions could be required. The areas and distances identified for the implementation of protective and response actions for such a hypothetical accident were found to be in agreement with the International Atomic Energy Agency (IAEA) suggested emergency planning zones and distances. Additionally, the implications of meteorological and source term input parameters on predicting the radiation doses to members of the public were investigated. It was identified that the output of such tools strongly depends on the availability and accuracy of the input parameters such as radioactive release and meteorological data. Limitations and uncertainties associated with these tools need to be considered in deciding on protective and other response actions in response to a nuclear accident. As established by the IAEA, protective and other response actions need to be applied on a graded approach taking into account the protection strategy and uncertainties and limitation in the available information and criteria, based on the conditions at the facility and off the site.

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“…Another type of measurements are surface activities that can be measured using fallout for each nuclide and integrated over period of time, daily at the best as in the case of Fukushima (Saunier et al ; Leelössy et al ). Therefore, gamma dose rate (GDR) measurements are mostly adopted when rapid response is needed due to their availability (Katata et al ; Kovalets et al ). However, they represent a challenge for the inversion method since its value is a sum of contributions from all nuclides forming the plume radiation and deposition radiation (Saunier et al ; Zhang et al ).…”

Section: Introductionmentioning

confidence: 99%

Source term estimation of multi‐specie atmospheric release of radiation from gamma dose rates

Tichý

Šmı́dl

Hofman

et al. 2018

Quart J Royal Meteoro Soc

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Determination of a source term of an accidental release of radioactive material into the atmosphere is very important for evaluating emergency situations and their consequences. However, knowledge of the source term and its composition is typically vague and uncertain. One possible way to obtain the source term is inverse modeling in which an atmospheric transport model is combined with field measurements. The most accessible measurements are those from gamma dose rate (GDR) detectors. However, GDR measurements represent a sum of contribution from all nuclides from both plume and deposition which makes the problem particularly difficult. The same difficulty arises when the measurements can not distinguish contribution from another species in the release, such as nuclides attached to different particle sizes. We propose a Bayesian method for recovery of the source term from GDR measurements where a priori knowledge on ratios of different species is given in the form of bounds. This knowledge is incorporated into the model of covariance matrix of the source term. The Bayesian methodology allows to handle uncertain knowledge on the nuclide ratios as well as unknown temporal correlations of the source term. We evaluate and compare the proposed method with other state‐of‐the‐art methods on a twin experiment of a non‐stationary release of 16 nuclides from the Czech nuclear power plant Temelin being registered by the Austrian GDR monitoring network. Real‐world validation of the approach is performed on the latest measurements of concentration and deposition of caesium‐137 from the Chernobyl accident, where we estimate composition of the source term from different particle sizes (species). The estimated source term is in very good agreement with previously reported results and the calculated species ratios are supported by the available observations.

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Advanced method for source term estimation and status of its integration in JRODOS

Cited by 7 publications

References 8 publications

Applications of Machine Learning to Consequence Analysis of Hypothetical Accidents at Barakah Nuclear Power Plant Unit 1

Applications of Machine Learning to Consequence Analysis of Hypothetical Accidents at Barakah Nuclear Power Plant Unit 1

JRODOS customization for Pakistan and assessment of the consequences of a hypothetical nuclear accident

Source term estimation of multi‐specie atmospheric release of radiation from gamma dose rates

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