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Enhancing Radiation Detection by Drones through Numerical Fluid Dynamics Simulations

This study addresses the optimization of the location of a radioactive-particle sensor on a drone. Based on the analysis of the physical process and of the boundary conditions introduced in the model, computational fluid dynamics simulations were performed to analyze how the turbulence caused by dro...

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Autores principales: Marturano, Fabio, Ciparisse, Jean-François, Chierici, Andrea, d’Errico, Francesco, Di Giovanni, Daniele, Fumian, Francesca, Rossi, Riccardo, Martellucci, Luca, Gaudio, Pasquale, Malizia, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7147154/
https://www.ncbi.nlm.nih.gov/pubmed/32210063
http://dx.doi.org/10.3390/s20061770
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author Marturano, Fabio
Ciparisse, Jean-François
Chierici, Andrea
d’Errico, Francesco
Di Giovanni, Daniele
Fumian, Francesca
Rossi, Riccardo
Martellucci, Luca
Gaudio, Pasquale
Malizia, Andrea
author_facet Marturano, Fabio
Ciparisse, Jean-François
Chierici, Andrea
d’Errico, Francesco
Di Giovanni, Daniele
Fumian, Francesca
Rossi, Riccardo
Martellucci, Luca
Gaudio, Pasquale
Malizia, Andrea
author_sort Marturano, Fabio
collection PubMed
description This study addresses the optimization of the location of a radioactive-particle sensor on a drone. Based on the analysis of the physical process and of the boundary conditions introduced in the model, computational fluid dynamics simulations were performed to analyze how the turbulence caused by drone propellers may influence the response of the sensors. Our initial focus was the detection of a small amount of radioactivity, such as that associated with a release of medical waste. Drones equipped with selective low-cost sensors could be quickly sent to dangerous areas that first responders might not have access to and be able to assess the level of danger in a few seconds, providing details about the source terms to Radiological-Nuclear (RN) advisors and decision-makers. Our ultimate application is the simulation of complex scenarios where fluid-dynamic instabilities are combined with elevated levels of radioactivity, as was the case during the Chernobyl and Fukushima nuclear power plant accidents. In similar circumstances, accurate mapping of the radioactive plume would provide invaluable input-data for the mathematical models that can predict the dispersion of radioactivity in time and space. This information could be used as input for predictive models and decision support systems (DSS) to get a full situational awareness. In particular, these models may be used either to guide the safe intervention of first responders or the later need to evacuate affected regions.
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spelling pubmed-71471542020-04-20 Enhancing Radiation Detection by Drones through Numerical Fluid Dynamics Simulations Marturano, Fabio Ciparisse, Jean-François Chierici, Andrea d’Errico, Francesco Di Giovanni, Daniele Fumian, Francesca Rossi, Riccardo Martellucci, Luca Gaudio, Pasquale Malizia, Andrea Sensors (Basel) Article This study addresses the optimization of the location of a radioactive-particle sensor on a drone. Based on the analysis of the physical process and of the boundary conditions introduced in the model, computational fluid dynamics simulations were performed to analyze how the turbulence caused by drone propellers may influence the response of the sensors. Our initial focus was the detection of a small amount of radioactivity, such as that associated with a release of medical waste. Drones equipped with selective low-cost sensors could be quickly sent to dangerous areas that first responders might not have access to and be able to assess the level of danger in a few seconds, providing details about the source terms to Radiological-Nuclear (RN) advisors and decision-makers. Our ultimate application is the simulation of complex scenarios where fluid-dynamic instabilities are combined with elevated levels of radioactivity, as was the case during the Chernobyl and Fukushima nuclear power plant accidents. In similar circumstances, accurate mapping of the radioactive plume would provide invaluable input-data for the mathematical models that can predict the dispersion of radioactivity in time and space. This information could be used as input for predictive models and decision support systems (DSS) to get a full situational awareness. In particular, these models may be used either to guide the safe intervention of first responders or the later need to evacuate affected regions. MDPI 2020-03-23 /pmc/articles/PMC7147154/ /pubmed/32210063 http://dx.doi.org/10.3390/s20061770 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Marturano, Fabio
Ciparisse, Jean-François
Chierici, Andrea
d’Errico, Francesco
Di Giovanni, Daniele
Fumian, Francesca
Rossi, Riccardo
Martellucci, Luca
Gaudio, Pasquale
Malizia, Andrea
Enhancing Radiation Detection by Drones through Numerical Fluid Dynamics Simulations
title Enhancing Radiation Detection by Drones through Numerical Fluid Dynamics Simulations
title_full Enhancing Radiation Detection by Drones through Numerical Fluid Dynamics Simulations
title_fullStr Enhancing Radiation Detection by Drones through Numerical Fluid Dynamics Simulations
title_full_unstemmed Enhancing Radiation Detection by Drones through Numerical Fluid Dynamics Simulations
title_short Enhancing Radiation Detection by Drones through Numerical Fluid Dynamics Simulations
title_sort enhancing radiation detection by drones through numerical fluid dynamics simulations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7147154/
https://www.ncbi.nlm.nih.gov/pubmed/32210063
http://dx.doi.org/10.3390/s20061770
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