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Enhancing Radiation Detection by Drones through Numerical Fluid Dynamics Simulations

Department of Industrial Engineering, University of Rome Tor Vergata, 00133 Rome, Italy
Department of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy
Department of Biomedicine and Prevention, University of Rome Tor Vergata, 00133 Rome, Italy
Author to whom correspondence should be addressed.
Sensors 2020, 20(6), 1770;
Received: 23 January 2020 / Revised: 2 March 2020 / Accepted: 18 March 2020 / Published: 23 March 2020
(This article belongs to the Section Physical Sensors)
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. View Full-Text
Keywords: detection; drone; radiation; simulation; measure; instrumentation detection; drone; radiation; simulation; measure; instrumentation
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Marturano, F.; Ciparisse, J.-F.; Chierici, A.; d’Errico, F.; Di Giovanni, D.; Fumian, F.; Rossi, R.; Martellucci, L.; Gaudio, P.; Malizia, A. Enhancing Radiation Detection by Drones through Numerical Fluid Dynamics Simulations. Sensors 2020, 20, 1770.

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