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Antares achieves zero-power criticality at INL
Leveraging more than $140 million in private capital fundraising, over 322,000 square feet of operational manufacturing space, and multifaceted partnerships with the Departments of Energy and Defense, reactor start-up Antares has become the first company involved in the Reactor Pilot Program to achieve zero-power fueled criticality—a full month ahead of the July 4 deadline set by President Trump’s Executive Order 14301.
This milestone, announced yesterday, was achieved with the company’s Mark-0: a sodium heat-pipe-cooled, TRISO-fueled microreactor. The Mark-0 is a forerunner to the company’s flagship design, which it calls the R1. For Antares, this development represents a key validation of its reactor physics, control systems, and supply chain.
Keisuke Fujii, Motoshi Goto, Shigeru Morita, Masahiro Hasuo
Fusion Science and Technology | Volume 69 | Number 2 | April 2016 | Pages 514-525
Technical Paper | doi.org/10.13182/FST15-168
Articles are hosted by Taylor and Francis Online.
The Balmer-α line profile observed from high-temperature magnetized plasmas can be interpreted as the sum of narrow and broad components corresponding to the emission from atoms generated in edge and core regions, respectively. The inversion of this line profile reveals the atom density distribution in the plasma. The inversion method we reported in previous studies [Nucl. Fusion, 55, 063029 (2015) and Rev. Sci. Instrum., 85, 023502 (2014)] requires a regularization parameter that must be manually tuned to avoid overfitting. Therefore, it has been difficult to evaluate the uncertainty of the results. Here, we report an improved method based on Bayesian statistics in which the regularization parameter is interpreted as an adjustable parameter, which is then marginalized for the uncertainty evaluation. Two types of prior distributions were examined. The first is an empirical prior that assumes the smoothness of a solution, and the second is based on a diffusion model of hydrogen atoms. We found the use of the diffusion model as prior information to have an advantage with respect to the accuracy of the core region atom density.