PI: Anders Robertsson (LU). Co-PIs: R Johansson (LU), M Greiff (LU), R Tyllström (LU), E Rofors (LU).
During commissioning, operation and decommissioning of nuclear power plants, particle accelerators and industries dealing with radioactive materials, there is a need to monitor radiation levels and isotope composition over large swathes of land surrounding the facilities. Ideally, this would be done regularly by an automated system, but during today’s decommissioning of the Barsebäck plant and the building of European Spallation Source (ESS) in Lund, such measurements are taken manually using handheld devices by foot, or along roads around the facilities using car-mounted detector systems. Consequently, the goal of this project is to develop statistical methods for inference of radioactive isotope composition from gamma-radiation spectroscopy taken from an autonomous Unmanned Aerial Vehicle (UAV), permitting the automation of the process of radiation monitoring. This is a joint project between (i) the Department of Automatic Control (Lund University) responsible for the development of novel algorithmic solutions, (ii) the Department of Nuclear Physics (Lund University) providing new radiation detectors capable of being carried by the UAV, and (iii) the School of Aviation (TFHS) providing the piloting and expertise during field experiments. The proposed project is well aligned with the priority item 1 in the 2030 ELLIIT Foresight, as it will involve new models for decision-making and control with novel ways of representing and identifying the radiation intensity functions. This will be done with methods closely related to non-parametric machine learning, also aligning nicely with point A in the 2030 Foresight.
Project number: A3