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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.
Robert O. Hoover, Supathorn Phongikaroon, Michael F. Simpson, Shelly X. Li, Tae-Sic Yoo
Nuclear Technology | Volume 171 | Number 3 | September 2010 | Pages 276-284
Technical Paper | Pyro 08 Special / Reprocessing | doi.org/10.13182/NT10-2A
Articles are hosted by Taylor and Francis Online.
The electrochemical processing of spent metallic nuclear fuel has been demonstrated by and is currently in operation at the Idaho National Laboratory (INL). At the heart of this process is the Mark-IV electrorefiner (ER). This process involves the anodic dissolution of spent nuclear fuel into a molten salt electrolyte along with a simultaneous deposition of pure uranium on a solid cathode. This allows the fission products to be separated from the fuel and processed into an engineered waste form. A one-dimensional model of the Mark-IV ER has begun to be developed. The computations thus far have modeled the dissolution of the spent nuclear fuel at the anode taking into account uranium (U3+), plutonium (Pu3+), and zirconium (Zr4+). Uranium and plutonium are the two most important elements in the system, whereas zirconium is the most active of the noble metals. The model shows that plutonium is quickly exhausted from the anode, followed by dissolution of primarily uranium, along with small amounts of zirconium. The total anode potential as calculated by the model has been compared to experimental data sets provided by INL. The anode potential has been shown to match the experimental values quite well with root-mean-square (rms) values of 2.27 and 3.83% for two different data sets, where rms values closer to zero denote better fit.