Seoul National University · 工学
Professor Han Gyu Joo's research lab specializes in advanced reactor physics and computational nuclear engineering, focusing on high-fidelity whole-core transport calculations, efficient numerical methods for neutron transport and kinetics, and innovative acceleration techniques for reactor simulation. The lab develops state-of-the-art deterministic codes such as DeCART and nTRACER, emphasizing direct whole-core calculations without reliance on pre-homogenized cross sections, while advancing methods like the method of characteristics (MOC), coarse mesh finite difference (CMFD), and resonance treatment with spectral superhomogenization. Key research directions include dynamic homogenization, subpin-level thermal feedback, and parallel computing for large-scale reactor simulations, with strong validation against Monte Carlo benchmarks and experimental data.
Figures are computed from collected data and may differ slightly.
DeCART is a three-dimensional whole-core transport code capable of performing direct core calculations at power generating conditions without involving a priori homogenized few-group constant generation. In this paper, the methods of DeCART, which are characterized by the planar method of characteristics (MOC) solutions, the cell based coarse mesh finite difference (CMFD) formulation, the subgroup method for resonance treatment and subpin level thermal feedback, are presented as a whole. The per
The BEAVRS (Benchmark for Evaluation and Validation of Reactor Simulation) benchmark is solved by the nTRACER direct whole core calculation code to assess its accuracy and to examine the solution dependence on modeling parameters.A sophisticated nTRACER core model representing the BEAVRS core is prepared after a series of sensitivity study to ensure solution accuracy.The resulting solutions for several hot-zero-power (HZP) states are compared first with the corresponding Monte Carlo solutions, w
The coarse mesh finite difference (CMFD) formulation is applied to the heterogeneous whole transport calculation as a means of efficient acceleration as well as simplified representation. A pin cell is chosen as the base coarse mesh which is much coarser than the intra-cell flat source regions. The CMFD formulation enables dynamic homogenization of the cells during the iterative solution process such that the heterogeneous transport solution can be preserved. Dynamic group condensation is also p
Methods are proposed for the efficient parallel solution of nonlinear nodal kinetics equations. Because the two-node calculation in the nonlinear nodal method is naturally parallelizable, the majority of the effort is devoted to the development of parallel methods for solving the coarse-mesh finite difference (CMFD) problem. A preconditioned Krylov subspace method (biconjugate gradient stabilized) is chosen as the iterative algorithm for the CMFD problem, and an efficient parallel preconditionin
Based on the observation that ignoring the angle dependency of multigroup resonance cross sections within a fuel pellet would result in nontrivial underestimation of the spatial self-shielding of flux, a parametrized spectral superhomogenization (SPH) factor library (PSSL) method is developed as a practical means of resolving the problem. Region-wise spectral SPH factors are calculated by the normal and transport corrected SPH iterations after ultrafine group slowing down calculations over vario
The coarse mesh finite difference (CMFD) method is applied to the discontinuous finite element method based discrete ordinate calculation for source convergence acceleration. The three-dimensional (3-D) DFEM-Sn code FEDONA is developed for general geometry applications as a framework for the CMFD implementation. Detailed methods for applying the CMFD acceleration are established, such as the method to acquire the coarse mesh flux and current by combining unstructured tetrahedron elements to rect
A domain decomposition (DD) scheme for GPU-based Monte Carlo (MC) calculation which is essential for whole-core depletion is introduced within the framework of the modified history-based tracking algorithm. Since GPU-offloaded MC calculations suffer from limited memory capacity, employing DDMC is inevitable for the simulation of depleted cores which require large storage to save hundreds of newly generated isotopes. First, an automated domain decomposition algorithm named wheel clustering is dev
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