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Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Startup looks to commercialize inertial fusion energy
Another startup hoping to capitalize on progress the Department of Energy’s Lawrence Livermore National Laboratory has made in realizing inertial fusion energy has been launched. On August 27, San Francisco–based Inertia Enterprises, a private fusion power start-up, announced the formation of the company with the goal of commercializing fusion energy.
Ang Zhu, Brendan Kochunas, Yunlin Xu, Michael Jarrett, Edward Larsen, Thomas Downar
Nuclear Science and Engineering | Volume 186 | Number 3 | June 2017 | Pages 224-238
Technical Paper | doi.org/10.1080/00295639.2017.1293408
Articles are hosted by Taylor and Francis Online.
The lower bounds for the theoretical convergence rate of variants of the Coarse Mesh Finite Difference (CMFD) method for neutron transport acceleration are studied in this paper by generalization of the method into three categories: artificially diffusive CMFD, flux relaxation, and higher-order spatial prolongation operators. A Fourier analysis of the methods demonstrates that artificial diffusion and flux relaxation are mathematically equivalent and arbitrarily scale the coarse mesh to fine mesh projection (CMP) vector. The high-order spatial prolongation method is shown to affect the shape of the CMP vector. As a result, any of the CMFD variants based on these three sets of modifications correspond to a specific CMP vector. The optimization process is performed for the multidimensional vector, and the minimum spectral radius among all possible CMP vectors is shown to be the theoretical lower bound for the CMFD convergence rate. The spectral radius associated with the CMFD convergence rate lower bound is found to be slightly smaller (less than 0.04) than optimally diffusive CMFD(odCMFD), and the difference between odCMFD to the CMFD lower bound is much smaller than the difference between both standard CMFD and partial current–based CMFD to the CMFD lower bound. In addition, the odCMFD method has a distinct advantage in ease of implementation and minimal overhead. Conversely, the implementation necessary to achieve the CMFD lower bound would be very complicated, especially for two- and three-dimensional problems.