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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.
Emma E. Regentova, Lei Zhang, Ajay K. Mandava, Vijay K. Mandava, Kranthi K. Potetti, Gongyin Chen, Zane Wilson
Nuclear Technology | Volume 175 | Number 1 | July 2011 | Pages 276-285
Technical Paper | Special Issue on the 16th Biennial Topical Meeting of the Radiation Protection and Shielding Division / Radiation Measurements and General Instrumentation | doi.org/10.13182/NT11-A12300
Articles are hosted by Taylor and Francis Online.
Megavoltage X-ray technology is utilized to detect fissile materials that can be smuggled by terrorists among commercial goods in cargo containers. Material discrimination with dual energy barriers is based on a ratio of penetration levels at respective energies. However, for a broad bremsstrahlung spectrum, the approach is not reliable because of its sensitivity to mass thickness. Furthermore, cargo containers usually have combinations of materials in a stack that further complicates material identification. It is imperative to study the capability of dual mega-electron-volt energy radioscopy to detect materials of interest for its practical application at customs. The time to perform this inspection automatically and the need to manually open the container for examination are to be minimized for the smooth transport of goods through the national border. In this work, Linatron K9, developed and manufactured by Varian Inc., Inspection and Security Products, is used for experimentation. By switching 6- and 9-MeV beams, an interlaced penetration response is obtained. The automated detection of materials of high atomic numbers in the stack of materials is performed by proposed adaptive thresholding algorithm. The evaluation of the system based on a worst case scenario shows that the system meets requirements defined in the congressional report in terms of true and false positive identification rates, smallest object resolution, and the processing time.