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GAIN vouchers go to Constellation, Nano Nuclear, and NuCube
The Department of Energy’s Gateway for Accelerated Innovation in Nuclear (GAIN) has awarded three fiscal year 2026 vouchers to support the development of advanced nuclear technologies. Each company will get access to specific capabilities and expertise in the DOE’s national laboratory complex—in this round of awards both Oak Ridge National Laboratory and Argonne National Laboratory are named—and will be responsible for a minimum 20 percent cost share, which can be an in-kind contribution.
Thomas E. Booth, James E. Gubernatis
Nuclear Science and Engineering | Volume 165 | Number 3 | July 2010 | Pages 283-291
Technical Paper | doi.org/10.13182/NSE09-62
Articles are hosted by Taylor and Francis Online.
Recently, we proposed a modified power iteration method that simultaneously determines the dominant and subdominant eigenvalues and eigenfunctions of a matrix or a continuous operator. One advantage of this method is the convergence rate to the dominant eigenfunction being [vertical bar]k3[vertical bar]/k1 instead of [vertical bar]k2[vertical bar]/k1, a potentially significant acceleration. One challenge for a Monte Carlo implementation of this method is that the second eigenfunction is represented by particles of both positive and negative weights that somehow must sum (cancel) to estimate the second eigenfunction faithfully. Our previous Monte Carlo work has demonstrated the improved convergence rate by using a point flux estimator method and a binning method to effect this cancellation. This paper presents an exact method that cancels over a region instead of at points or in small bins and has the potential of being significantly more efficient than the other two.