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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.
Payam Vaezi, Christopher Holland
Fusion Science and Technology | Volume 74 | Number 1 | July-August 2018 | Pages 77-88
Technical Paper | doi.org/10.1080/15361055.2017.1372987
Articles are hosted by Taylor and Francis Online.
Due to the strong nonlinear dependence of plasma turbulence on drive and dissipation mechanisms, uncertainties in experimental inputs can be greatly magnified in simulations of this turbulence. Thus, careful uncertainty quantification (UQ) and its inclusion within validation metrics is an integral part of plasma turbulence validation studies. To minimize the number of simulations required for UQ, we investigate the use of the rapidly converging nonintrusive probabilistic collocation method (PCM) for efficient plasma turbulence UQ. This approach is shown to yield more realistic uncertainty estimates than simple uniform sampling methods for a practical number of nonlinear simulations. The inclusion of UQ above and near critical gradients is discussed. To demonstrate its utility, the advantages of PCM are first illustrated using a simple model of critical gradient turbulence. It is then used on simulations from a validation study of drift-wave turbulence in the CSDX linear plasma device experiment. The advantage of more advanced methods for selecting samples from the uncertainties in the plasma turbulence simulations is also discussed.