• Proceedings of the Korea Society for Simulation Conference
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• 2004.05a
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• pp.101-108
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• 2004

KEPRI has developed an Nuclear Steam Supply System(NSSS) thermal-hydraulics simulation program (called ARTS-KORI), based on the best-estimate system code, RETRAN, as a part of the development project for the KORI unit 1 Nuclear Power Plant Simulator. A number of code modifications, such as simplifications and removing of discontinuities of the physical correlations, were made in order to change the RETRAN code as an nuclear Steam Supply System thermal-hydraulics engine in the simulator. Some simplified models and a backup system were also developed. This paper briefly presents the results of non-integrated standalone test of ARTS-KORI.

• Nuclear Engineering and Technology
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• v.44 no.6
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• pp.611-622
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• 2012

"Scaling" plays an important role for safety analyses in the licensing of water cooled nuclear power reactors. Accident analyses, a sub set of safety analyses, is mostly based on nuclear reactor system thermal hydraulics, and therefore based on an adequate experimental data base, and in recent licensing applications, on best estimate computer code calculations. In the field of nuclear reactor technology, only a small set of the needed experiments can be executed at a nuclear power plant; the major part of experiments, either because of economics or because of safety concerns, has to be executed at reduced scale facilities. How to address the scaling issue has been the subject of numerous investigations in the past few decades (a lot of work has been performed in the 80thies and 90thies of the last century), and is still the focus of many scientific studies. The present paper proposes a "roadmap" to scaling. Key elements are the "scaling-pyramid", related "scaling bridges" and a logical path across scaling achievements (which constitute the "scaling puzzle"). The objective is addressing the scaling issue when demonstrating the applicability of the system codes, the "key-to-scaling", in the licensing process of a nuclear power plant. The proposed "road map to scaling" aims at solving the "scaling puzzle", by introducing a unified approach to the problem.

• Nuclear Engineering and Technology
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• v.38 no.1
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• pp.11-32
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• 2006

System thermal-hydraulic codes have been used in the past decades in the areas of design, operation, licensing and safety of Nuclear Power Plants (NPPs). The development and validation of these codes have reached a high degree of maturity, through the consideration of huge experiments and advanced numerical models. Nowadays, the analyses are based upon realistic approaches rather than the conservative evaluation models. However the applications of these computational tools require preliminary qualification issues. Although huge amounts of financial and human resources have been invested for the development and improvement of codes, the calculation results are still affected by errors. In the sophisticated nuclear technology, design and safety of NPP, these errors must be quantified. An overview of the state of the art of the current thermal-hydraulic system code is developed and the need of uncertainty analysis in code calculations is emphasized. Several sources of uncertainty have been classified and commented, and typical applications of such methods are shown.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3787-3800
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• 2023

Reactivity Initiated Accident (RIA) scenarios require special attention using advanced simulation techniques due to their complexity and importance for nuclear power plant (NPP) safety. While the conservative approach has traditionally been used for safety analysis, it may lead to unrealistic results which calls for the use of best estimate plus uncertainty (BEPU) approach, especially with the current advances in computational power which makes the BEPU analysis feasible. In this work an Uncontrolled Control Element Assembly (CEA) Withdrawal at Full Power accident scenario is analyzed using the BEPU approach by loosely coupling the thermal hydraulics best-estimate system code (RELAP5/SCDAPSIM/MOD3.4) to the statistical analysis software (DAKOTA) using a Python interface. Results from the BEPU analysis indicate that a realistic treatment of the accident scenario yields a larger safety margin and is therefore encouraged for accident analysis as it may enable more economic and flexible operation.

• Journal of the Korean Society of Systems Engineering
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• v.19 no.2
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• pp.18-31
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• 2023

Exploring artificial intelligence and machine learning for nuclear safety has witnessed increased interest in recent years. To contribute to this area of research, a machine learning model capable of accurately predicting nuclear power plant response with minimal computational cost is proposed. To develop a robust machine learning model, the Best Estimate Plus Uncertainty (BEPU) approach was used to generate a database to train three models and select the best of the three. The BEPU analysis was performed by coupling Dakota platform with the best estimate thermal hydraulics code RELAP/SCDAPSIM/MOD 3.4. The Code Scaling Applicability and Uncertainty approach was adopted, along with Wilks' theorem to obtain a statistically representative sample that satisfies the USNRC 95/95 rule with 95% probability and 95% confidence level. The generated database was used to train three models based on Recurrent Neural Networks; specifically, Long Short-Term Memory, Gated Recurrent Unit, and a hybrid model with Long Short-Term Memory coupled to Convolutional Neural Network. In this paper, the System Engineering approach was utilized to identify requirements, stakeholders, and functional and physical architecture to develop this project and ensure success in verification and validation activities necessary to ensure the efficient development of ML meta-models capable of predicting of the nuclear power plant response.

• Nuclear Engineering and Technology
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• v.37 no.5
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• pp.469-478
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• 2005

The subchannel analysis capability of MARS, a multi-dimensional thermal-hydraulic system code, has been enhanced. In particular, the turbulent mixing and void drift models for the flow-mixing phenomena in rod bundles were improved. Then, the subchannel analysis feature was combined with the existing coupled system thermal-hydraulics (T/H) and 3D reactor kinetics calculation capability of MARS. These features allow for more realistic simulations of both the hot channel behavior and the global system T/H behavior. Using the coupled features of MARS, a coupled analysis of a main steam line break (MSLB) is carried out for demonstration purposes. The results of the calculations are very reasonable and promising.

• Nuclear Engineering and Technology
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• v.41 no.6
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• pp.753-764
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• 2009

JAEA started the LSTF experiments in 1985 for the fourth stage of the ROSA Program (ROSA-IV) for the LWR thermal-hydraulic safety research to identify and investigate the thermal-hydraulic phenomena and to confirm the effectiveness of ECCS during small-break LOCAs and operational transients. The LSTF experiments are underway for the ROSA-V Program and the OECD/NEA ROSA Project that intends to resolve issues in thermal-hydraulic analyses relevant to LWR safety. Six types of the LSTF experiments have been done for both the system integral and separate-effect experiments among international members from 14 countries. Results of four experiments for the ROSA Project are briefly presented with analysis by a best-estimate (BE) code and a computational fluid dynamics (CFD) code to illustrate the capability of the LSTF and codes to simulate the thermal-hydraulic phenomena that may appear during SBLOCAs and transients. The thermal-hydraulic phenomena dealt with are coolant mixing and temperature stratification, water hammer up to high system pressure, natural circulation under high core power condition, and non-condensable gas effect during asymmetric SG depressurization as an AM action.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.75-93
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• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.

• Journal of Energy Engineering
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• v.23 no.4
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• pp.112-122
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• 2014

A main steam line break (MSLB) test at the ATLAS facility was simulated using the best-estimate thermal-hydraulic system code, MARS-KS. This has been performed as an activity at the third domestic standard problem for code benchmark (DSP-03) that has been organized by Korea Atomic Energy Research Institute (KAERI). The results of the MSLB experiment and the MARS input data prepared for the previous DSP-02 using the ATLAS facility were provided to participants. The preliminary MSLB simulation using the base input data, however, showed unphysical results in the primary-to-secondary heat transfer. To resolve the problems, some improvements were implemented in the MARS input modelling. These include the use of fine meshes for the bottom region of the steam generator secondary side and proper thermal-hydraulics calculation options. Other input model improvements in the heat loss and the flow restrictor models were also made and the results were investigated in detail. From the results of simulations, the limitations and further improvement areas of the MARS code were identified.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3423-3440
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• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.