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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.
J. M. Canik, X.-Z. Tang
Fusion Science and Technology | Volume 71 | Number 1 | January 2017 | Pages 103-109
Technical Paper | doi.org/10.13182/FST16-124
Articles are hosted by Taylor and Francis Online.
While the sensitivity of the scrape-off layer and divertor plasma to the highly uncertain cross-field transport assumptions is widely recognized, the plasma is also sensitive to the details of the plasma-material interface (PMI) models used as part of comprehensive predictive simulations. Here, these PMI sensitivities are studied by varying the relevant submodels within the SOLPS plasma transport code. Two aspects are explored: the sheath model used as a boundary condition in SOLPS, and fast particle reflection rates for ions impinging on a material surface. Both of these have been the study of recent high-fidelity simulation efforts aimed at improving the understanding and prediction of these phenomena. It is found that in both cases quantitative changes to the plasma solution result from modification of the PMI model, with a larger impact in the case of the reflection coefficient variation. This indicates the necessities to better quantify the uncertainties within the PMI models themselves and to perform thorough sensitivity analysis to propagate these throughout the boundary model; this is especially important for validation against experiment, where the error in the simulation is a critical and less-studied piece of the code-experiment comparison.