Han Gyu Joo
Seoul National University · 工学
研究室紹介
Professor Han Gyu Joo's research lab specializes in advanced reactor physics and computational nuclear engineering, focusing on high-fidelity whole-core nuclear core analysis using deterministic and stochastic methods. The lab develops innovative transport and diffusion methodologies, including the DeCART and nTRACER codes, for accurate and efficient simulation of light water reactors under various operating conditions. Key research directions include advanced acceleration techniques such as CMFD and preconditioned Krylov solvers, resonance treatment with spectral superhomogenization, and dynamic feedback modeling for improved core performance prediction.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15DeCART 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