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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
Conference on Nuclear Training and Education: A Biennial International Forum (CONTE 2025)
February 3–6, 2025
Amelia Island, FL|Omni Amelia Island Resort
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
US, Korea sign MOU for nuclear cooperation
The U.S. departments of Energy and State have signed a memorandum of understanding with the Republic of Korea’s ministries of Trade, Industry and Energy and of Foreign Affairs for the two nations to partner on nuclear exports and cooperation.
Shawn A. Campbell, John Palsmeier, Sudarshan K. Loyalka
Nuclear Science and Engineering | Volume 182 | Number 3 | March 2016 | Pages 287-296
Technical Paper | doi.org/10.13182/NSE15-40
Articles are hosted by Taylor and Francis Online.
The nuclear source term is greatly affected by the formation and presence of aerosols in the reactor primary vessel and the containment. In simulations, the aerosol distribution is often assumed spatially homogeneous (well mixed), and there have been relatively few studies of the effects of spatial inhomogeneity on aerosol evolution in nuclear accidents. We have explored here an extension of some of our recent work on the Direct Simulation Monte Carlo (DSMC) method to spatially inhomogeneous aerosol. In doing so, we have also departed from the traditional applications of the DSMC method where the computational domain is divided into fixed cells. We have explored here an alternative, mesh-free method by utilizing a clustering technique. This technique associates particles according to a distance parameter and is commonly used in group theory and machine learning. To benchmark this mesh-free modeling, we have verified the DSMC results against those obtained from the use of the cell balanced sectional technique for a spherical geometry where both coagulation and diffusion take place.