Year
2022
Abstract
To prepare for real-world emergencies involving high-radiation-dose hazards or unknown radiation hazards, first responders need to practice in those environments. However, training with actual (high radioactivity) hazardous high radiation sources involves tremendous logistical difficulties, expense, and radiation exposure. In general, current approaches to training avoid high radioactivity sources. Frequently such training uses event controllers who tell participants what their instruments should be reading or utilize simulated instruments with preprogrammed readings but this is underpreparing emergency responders for the complexities of such hazards because instruments behave differently in such high-hazard environments. People in charge of the training recognize the inadequacies of these approaches and wish for something better. The Radiation Field Training Simulator is the solution they have wished for.
Current training simulators are limited in a variety of ways. They do not adequately provide the operational realities of training with actual operating responder equipment. They also do not provide the most realistic and scientifically-sound scenarios to train against. To address this need, the Lawrence Livermore National Laboratory (LLNL) developed a next-generation training capability, the Radiation Field Training Simulator (RaFTS) for which we were awarded an R&D100 Award by R&D Magazine in 2017 and a TechConnect Defense Innovation Award in 2018. Three US patents protect LLNL’s intellectual property. RaFTS has been granted a two-year US Department of Energy (DOE) Technology Commercialization Fund award for LLNL to work with the UK’s Argon Electronics Limited and the Tennessee-based ORTEC, among other detector manufacturers, to commercialize RaFTS. Our project has been selected for a 2021 FLC National Excellence in Technology Transfer award. This paper will describe our past work and then discuss the next generation of RaFTS which will meet and hopefully exceed the expectations of the radiation detection community.