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Transient Multilevel Scheme with One-Group CMFD Acceleration

Qicang Shen, Brendan Kochunas, Yunlin Xu, Sooyoung Choi, Thomas Downar

Nuclear Science and Engineering / Volume 195 / Number 7 / July 2021 / Pages 741-765

Technical Paper / dx.doi.org/10.1080/00295639.2020.1866388

Received:June 29, 2020
Accepted:December 14, 2020
Published:June 2, 2021

The Transient Multilevel (TML) scheme in the MPACT code has reduced the computational burden for three-dimensional, full-core, time-dependent reactor simulations with pin-resolved detail. However, the total computational cost is still large for practical applications. In this paper, we present a new method that uses a one-group coarse mesh finite difference (1GCMFD) approach to further accelerate the TML scheme. Since multigroup coarse mesh finite difference (MGCMFD) calculations in TML dominate the simulation run time, the 1GCMFD method developed here is shown to reduce the overall computational time by as much as 50% for some large-scale applications. The 1GCMFD method accelerated the calculation primarily by accelerating the convergence of the source for MGCMFD calculations through 1G/MGCMFD iteration. A new 1GCMFD level was implemented in the TML scheme (TML-4) assuming that the energy distribution of the scalar flux shape varies more slowly than the energy-integrated amplitude of the scalar flux. Various numerical cases were used to investigate the practicality of the 1G/MGCMFD iteration and TML-4 scheme. Numerical results show that using the 1G/MGCMFD iteration with a dynamic iteration strategy alone does better capture the evolution of the amplitude function when the scalar flux distribution in energy space varies rapidly and thus provides more accurate results. However, TML-4 is more efficient in capturing the variation of the energy-integrated amplitude when cross-section changes are small and feedback dominates the change of the reactivity. For smaller problems, the 1G/MGCMFD iteration and the new TML-4 scheme can reduce the run time of the coarse mesh finite difference (CMFD) solver by 50% and the total run time by at least 16%. For large-scale, full-core problems, the run time of the CMFD solver can be reduced by 78% and the total run time by as much as 47%.