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Division Spotlight
Robotics & Remote Systems
The Mission of the Robotics and Remote Systems Division is to promote the development and application of immersive simulation, robotics, and remote systems for hazardous environments for the purpose of reducing hazardous exposure to individuals, reducing environmental hazards and reducing the cost of performing work.
Meeting Spotlight
Utility Working Conference and Vendor Technology Expo (UWC 2024)
August 4–7, 2024
Marco Island, FL|JW Marriott Marco Island
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Latest News
ARPA-E announces $40 million to develop transmutation technologies for UNF
The Department of Energy’s Advanced Research Projects Agency–Energy (ARPA-E) announced $40 million in funding to develop cutting-edge technologies to enable the transmutation of used nuclear fuel into less-radioactive substances. According to ARPA-E, the new initiative addresses one of the agency’s core goals as outlined by Congress: to provide transformative solutions to improve the management, cleanup, and disposal of radioactive waste and spent nuclear fuel.
R. R. Romatoski, L. W. Hu
Nuclear Technology | Volume 205 | Number 11 | November 2019 | Pages 1495-1512
Technical Paper | doi.org/10.1080/00295450.2019.1610686
Articles are hosted by Taylor and Francis Online.
An important fluoride-salt-cooled high-temperature reactor (FHR) development step is to design, build, and operate a test reactor. The uncertainties of the coolant thermophysical properties range between 2% and 20%. This study determines the effects of these high uncertainties by incorporating uncertainty propagation in a thermal-hydraulic safety analysis for test reactor licensing. A hot channel thermal-hydraulic model, Monte Carlo statistical sampling uncertainty propagation, and a limiting safety systems settings (LSSS) approach are combined to ensure sufficient margin to fuel and material thermal limits during steady-state operation while incorporating margin for high-uncertainty inputs. The method calculates LSSS parameters to define safe operation.
The methodology is applied to two test reactors currently considered, i.e., China’s first Solid Fueled Thorium Molten Salt Reactor (TMSR-SF1) pebble bed design and Massachusetts Institute of Technology’s Transportable FHR prismatic core design; two candidate coolants, i.e., flibe (LiF-BeF2) and nafzirf (NaF-ZrF4); and forced flow and natural circulation conditions to compare operating regions and LSSS power (maximum power not exceeding any thermal limits). The calculated operating region accounts for uncertainty (2σ) with an LSSS power for forced flows of 25.37 0.72, 22.56 1.15, 21.28 1.48, and 11.32 1.35 MW for pebble flibe, pebble nafzirf, prismatic flibe, and prismatic nafzirf, respectively. The pebble bed has superior heat transfer with an operating region reduced 10% less when switching coolants and 50% smaller uncertainty than the prismatic. The maximum fuel temperature constrains the pebble bed while the maximum coolant temperature constrains the prismatic due to different dominant heat transfer modes. Sensitivity analysis revealed that (1) thermal conductivity and thus conductive heat transfer dominate in the prismatic design while convection is superior in the pebble bed and (2) the impact of thermophysical property uncertainties is ranked and should be considered for experimental measurements in the following order: thermal conductivity, heat capacity, density, and last, viscosity. Broadly, the methodology incorporates uncertainty propagation that can be used to evaluate parametric uncertainties to satisfy guidelines for nonpower reactor licensing applications, and its application shows that the pebble bed is more attractive for thermal-hydraulic safety. Although the method is developed and evaluated for coolant property uncertainties, it is readily applicable for other parameters of interest.