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Texas A&M welcomes uranium conversion research
The Texas A&M Engineering Experiment Station (TEES) has signed a research agreement with Quantum Leap Energy (QLE) “to advance and de-risk the commercial production of high-purity uranium hexafluoride (UF6).”
QLE is an Austin, Texas–based subsidiary of ASP Isotopes (ASPI), which is developing an isotope enrichment platform for applications in nuclear energy, nuclear medicine, and semiconductors. QLE specializes in the uranium conversion step of the nuclear fuel cycle—the conversion of yellowcake uranium concentrate (U3O8) into UF6 prior to enrichment.
Re’em Harel, Stanislav Burov, Shay I. Heizler
Nuclear Science and Engineering | Volume 195 | Number 6 | June 2021 | Pages 578-597
Technical Paper | doi.org/10.1080/00295639.2020.1829345
Articles are hosted by Taylor and Francis Online.
In this study, a spatio-temporal approach for the solution of the time-dependent Boltzmann (transport) equation is derived. Finding the exact solution using the Boltzmann equation for the general case is generally an open problem and approximate methods are usually used. One of the most common methods is the spherical harmonics method (the approximation), when the exact transport equation is replaced with a closed set of equations for the moments of the density with some closure assumption. Unfortunately, the classic closure yields poor results with low-order N in highly anisotropic problems. Specifically, the tails of the particles’ positional distribution as attained by the approximation are inaccurate compared to the true behavior. In this work, we present a derivation of a linear closure that even for low-order approximation yields a solution that is superior to the classical approximation. This closure is based on an asymptotic derivation both for space and time of the exact Boltzmann equation in infinite homogeneous media. We test this approximation with respect to the one-dimensional benchmark of the full Green function in infinite media. The convergence of the proposed approximation is also faster when compared to (classic or modified) approximation.