Modelagem computacional da dispersão atmosférica aplicada a um reator modular de pequeno porte

Curzio, Rodrigo Carneiro; Neto, Alberto Texeira; Moura, Bruno da Silva; Talon, João Domingos; Lopes, Thomaz Jacintho; Bonfim, Carlos; Cardoso, Domingos D'Oliveira; Gavazza, Sérgio; Morales, Rudnei Karam; Gomes, Renato Guedes

doi:10.15392/bjrs.v9i1.1244

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…This process was performed using a computer simulation model to obtain simulated spectra of other target nuclides listed in the inventory shown in Table 1, including a total of 24 radionuclides. The nuclide selection is based on a study of CURZIO [11], which obtained a radionuclide inventory resulting from a simulation of an accident involving a small DWR reactor using the SCALE code, and PEREIRA [15], which, in their study, evaluated the radioactive effects of a Radiological Dispersion Device (RDD), also known as a dirty bomb, through computer simulation.…”

Section: Second Phasementioning

confidence: 99%

Artificial Neural Networks Applied in the Classification of Radionuclides from Gamma Spectroscopy and Computational Simulation

de Azevedo,

Silva,

Nunes

et al. 2024

Preprint

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The present study seeks to develop a classifier of radioactive sources based on gamma spectroscopy and artificial intelligence, which makes use of Keras and TensorFlow [, both free and open-source technologies. Through these technologies, an artificial neural network (ANN) was developed, which makes use of supervised machine learning and the backpropagation algorithm. The neural network was trained with a dataset of spectra from simulations performed by the Monte Carlo N-Particle 5 code (MCNP5). To achieve the objective of functioning as a classifier within the proposed scope, several versions of the neural network were evaluated, to study its performance, to determine important parameters such as a learning rate, and the optimizer used, among others, where RNA performance was evaluated by analyzing the network accuracy and loss curves. The generalization capacity was assessed by submitting spectra raised experimentally by an apparatus composed of a NaI(Tl) detector, a multichannel analyzer, and the Maestro software for the experimental data acquisition, which were used with the use of sealed radioactive sources of Co-60, Cs-137, and Eu-152. Six versions of the ANN were selected, capable of solving the proposed problem, with accuracy greater than 95%.

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Section: Second Phasementioning

confidence: 99%

Artificial Neural Networks Applied in the Classification of Radionuclides from Gamma Spectroscopy and Computational Simulation

de Azevedo,

Silva,

Nunes

et al. 2024

Preprint

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“…analyzes a hypothetical dirty bomb explosion at a large public event. The radionuclides stored in the laboratory of the Instituto Militar de Engenharia (IME) were kept in the inventory, namely 152 Eu,133 Ba, 57 Co,60 Co and 54 Mn, in addition to137 Cs, present in the inventory surveyed by CURZIO[14].The experimentally obtained spectra of 137 Cs,60 Co and 152 Eu were again used as ANN production data.In this phase, the ANN configuration was started with the same final configuration of the first phase, which, when submitted to a new set of training data and tests, did not obtain satisfactory performance. A systematic evolution was carried out with alteration of several ANN parameters,…”

mentioning

confidence: 99%

Radionuclide classification based on gamma spectroscopy and artificial intelligence

et al. 2021

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Currently, in almost all segments of the production chain, automation is a requirement for productivity improvement. With respect to nuclear facilities, active online monitoring is one of best practices for nuclear security and safety maintenance, to prevent incidents that could compromise a particular installation. In this context, spectral signature monitoring automation can be explored, aiming at the rapid identification of adverse events, such as radiological accidents. The main objective of this work was an automated radionuclides classification technique establishment, using an Artificial Neural Networks (ANN) architecture. The methodology used consisted basically of simulating the geometry of an established experimental apparatus, using the MCNP5 code, obtaining the simulated gamma spectral signature for the studied nuclides. The simulated spectra were used to compose the ANN training and testing data set, while the experimental spectra were subjected to the artificial intelligence model classification, in order to allow the neural network quality assessment. The final developed architecture of ANN was correct to recognize the experimental spectra of 60Co, 137Cs and 152Eu. Therefore, the results were satisfactory and proved automation technique development viable.

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Modelagem computacional da dispersão atmosférica aplicada a um reator modular de pequeno porte

Cited by 2 publications

References 3 publications

Artificial Neural Networks Applied in the Classification of Radionuclides from Gamma Spectroscopy and Computational Simulation

Artificial Neural Networks Applied in the Classification of Radionuclides from Gamma Spectroscopy and Computational Simulation

Radionuclide classification based on gamma spectroscopy and artificial intelligence

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