APro, a process-based total system performance assessment (TSPA) tool for a geological disposal system, has a framework for simulating the radionuclide transport affected by thermal, hydraulic, mechanical or geochemical changes occurred in the disposal system. APro aims to be applied for the TSPA to long-term (> 100,000) evolution scenarios in real-world repository having more than 10,000 boreholes. In this large-scale TSPA, it is important not only to develop a high-performance numerical approach, but also to apply an efficient post-processing approach to massive spatiotemporal data. The post-processing refers to validating numerical analysis results, analyzing and evaluating target systems through data processing or visualization. Since APro uses COMSOL interface, the postprocessing function in COMSOL can be used. However, when the data size increases due to largescale numerical analysis, the time for the COMSOL post-processing increases, resulting in a problem that the analysis and evaluation are not performed effectively. In this case, it is possible to extract necessary data using the COMSOL exporting function and importing it into an external postprocessing program for the analysis and evaluation. In this study, the efficiency of external post-processing with extracted data from COMSOL was reviewed. And, we derived a proper data extraction approach (format and structure) that can increase efficiency of external post-processing.

Conducting a TSPA (Total System Performance Assessment) of the entire spent nuclear fuel disposal system, which includes thousands of disposal holes and their geological surroundings over many thousands of years, is a challenging task. Typically, the TSPA relies on significant efforts involving numerous parts and finite elements, making it computationally demanding. To streamline this process and enhance efficiency, our study introduces a surrogate model built upon the widely recognized U-network machine learning framework. This surrogate model serves as a bridge, correcting the results from a detailed numerical model with a large number of small-sized elements into a simplified one with fewer and large-sized elements. This approach will significantly cut down on computation time while preserving accuracy comparable to those achieved through the detailed numerical model.

The most important thing in development of a process-based TSPA (Total System Performance Assessment) tool for large-scale disposal systems (like APro) is to use efficient numerical analysis methods for the large-scale problems. When analyzing the borehole in which the most diverse physical phenomena occur in connection with each other, the finest mesh in the system is applied to increase the analysis accuracy. Since thousands of such boreholes would be placed in the future disposal system, the numerical analysis for the system becomes significantly slower, or even impossible due to the memory problem in cases. In this study, we propose a tractable approach, so called global-local iterative analysis method, to solve the large-scale process-based TSPA problem numerically. The global-local iterative analysis method goes through the following process: 1) By applying a coarse mesh to the borehole area the size of the problem of global domain (entire disposal system) is reduced and the numerical analysis is performed for the global domain. 2) Solutions in previous step are used as a boundary condition of the problem of local domain (a unit space containing one borehole and little part of rock), the fine mesh is applied to the borehole area, and the numerical analysis is performed for each local domain. 3) Solutions in previous step are used as boundary conditions of boreholes in the problem of global domain and the numerical analysis is performed for the global domain. 4) steps 2) and 3) are repeated. The solution derived by the global-local iterative analysis method is expected to be closer to the solution derived by the numerical analysis of the global problem applying the fine mesh to boreholes. In addition, since local problems become independent problems the parallel computing can be introduced to increase calculation efficiency. This study analyzes the numerical error of the globallocal iterative analysis method and evaluates the number of iterations in which the solution satisfies the convergence criteria. And increasing computational efficiency from the parallel computing using HPC system is also analyzed.

방사성폐기물 처분 연구 사업이 법률적인 인허가 뿐만이 아니라 일반 국민의 동의를 얻기 위해서는 처분 사업의 안전성에 대한 신뢰성 획득이 중요하며 이를 위해 투명하게 공개될 수 있는 종합 성능 평가 (TSPA, Total System Performance Assessment)의 수행 이 필요하다. 본 연구에서는 처분 성능 평가의 투명성 증진을 위한 방안의 하나로 처분 종합 성능 평가 전 과정에 대해 품질 보증 원칙을 도입하여 평가 관련 전체 업무에 관한 신뢰성 향상을 꾀하고자 하였다. 이를 위해 처분 종합 성능 평가 수행의 다섯 단계인 (1) 기획, (2) 연구 수행 , (3) 문서화, (4) 내부 검토, (5) 독자적인 외부 검토 과정에 T2R3의 품질 보증 원칙을 적용한 인터넷 기반의 Cyber R&D Platform이 개발되었다. 인터넷을 기반으로 하는 본 시스템의 개발을 통해 안전성 평가 관련 모든 참여자들은 평가 전 과정에서 투명성이 유지된 데이터들에 쉽게 접근하여 이를 이용할 수 있다 Cyber R&D Platform은 안전성 평가를 위한 시나리오 개발 관련 데이터인 FEP 목록과 관련 시나리오 정보, 관련 시나리오 도출 과정 및 평가 체계 등을 체계적으로 구축한 FEAS (FEp to Assessment through Scenario development)프로그램과 안전성 평가에 필요한 입력 데이터들을 분류, 저장해 놓은 PAID (Performance Assessment Input Data) 프로그램, 그리고 이러한 자료들을 품질 보증 원칙과 절차에 의한 승인 과정을 통해 입력, 저장할 수 있는 품질 보증 시스템으로 구성되어 있으며 이를 통합 운영함으로써 도출된 데이터들의 신뢰성을 높이고자 하였다. 향후 연구에서는 Cyber R&D Platform과 평가 software와의 통합 운영으로 웹 기반 시스템에 대한 한 번의 접속만으로 안전성 평가 관련 모든 정보를 확인, 이용할 수 있도록 할 것이다.