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LLNL, Ampera partner to develop thorium-based TRISO fuel
Lawrence Livermore National Laboratory has formed a strategic partnership with Ampera to develop the company’s nuclear fuel concept through a project named THUNDER, for Thorium Unimodal Droplet Ejection for Reactors.
The focus of THUNDER is fabricating TRISO made with kernels of thorium rather than the usual uranium. LLNL and Ampera will evaluate and optimize liquid metal–jetting technology to produce highly uniform, spherical kernels of thorium-232 for later processing into TRISO fuel.
D. C. Groeneveld, L. K. H. Leung, Y. Guo, A. Vasic, M. El Nakla, S. W. Peng, J. Yang, S. C. Cheng
Nuclear Technology | Volume 152 | Number 1 | October 2005 | Pages 87-104
Technical Paper | Nuclear Reactor Thermal Hydraulics | doi.org/10.13182/NT152-87
Articles are hosted by Taylor and Francis Online.
Lookup tables (LUTs) have been used widely for the prediction of critical heat flux (CHF) and film-boiling heat transfer for water-cooled tubes. LUTs are basically normalized data banks. They eliminate the need to choose between the many different CHF and film-boiling heat transfer prediction methods available.The LUTs have many advantages; e.g., (a) they are simple to use, (b) there is no iteration required, (c) they have a wide range of applications, (d) they may be applied to nonaqueous fluids using fluid-to-fluid modeling relationships, and (e) they are based on a very large database. Concerns associated with the use of LUTs include (a) there are fluctuations in the value of the CHF or film-boiling heat transfer coefficient (HTC) with pressure, mass flux, and quality, (b) there are large variations in the CHF or the film-boiling HTC between the adjacent table entries, and (c) there is a lack or scarcity of data at certain flow conditions.Work on the LUTs is continuing. This will resolve the aforementioned concerns and improve the LUT prediction capability. This work concentrates on better smoothing of the LUT entries, increasing the database, and improving models at conditions where data are sparse or absent.