• 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.

• 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.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2670-2677
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• 2023

In the nuclear safety studies, a new trend refers to the evaluation of uncertainties as a mandatory component of best-estimate safety analysis which is a modern and technically consistent approach being known as BEPU (Best Estimate Plus Uncertainty). The major objectives of this study consist in performing a study of uncertainties/sensitivities of the major analysis results for a generic CANDU 6 Nuclear Power Plant during Station Blackout (SBO) progression to understand and characterize the sources of uncertainties and their effects on the key figure-of-merits (FOMs) predictions in severe accidents (SA). The FOMs of interest are hydrogen mass generation and event timings such as the first fuel channel failure time, beginning of the core disassembly time, core collapse time and calandria vessel failure time. The outcomes of the study, will allow an improvement of capabilities and expertise to perform uncertainty and sensitivity analysis with severe accident codes for CANDU 6 Nuclear Power Plant.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2821-2835
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• 2020

The phenomenon identification and ranking table (PIRT) is an important basis in the nuclear power plant (NPP) thermal-hydraulic analysis. This study focuses on the importance ranking of the input parameters when lacking the PIRT, and the target scenario is the small break loss of coolant accident (SBLOCA) in a pressurized water reactor (PWR) CPR1000. A total of 54 input parameters which might have influence on the figure of merit (FOM) were identified, and the sensitivity measure of each input on the FOM was calculated through an optimized moment-independent global sensitivity analysis method. The importance ranking orders of the parameters were transformed into the Savage scores, and the parameters were categorized based on the Savage scores. A parameter importance ranking table for the SBLOCA scenario of the CPR1000 reactor was obtained, and the influences of some important parameters at different break sizes and different accident stages were analyzed.

• Nuclear Engineering and Technology
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• v.55 no.4
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• pp.1448-1459
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• 2023

A key element of the safety analysis is Loss of Coolant Analysis (LOCA) which must be performed using system thermal-hydraulic codes. These codes are extensively validated against separate effect and integral experiments. RELAP/SCDAPSIM is one such code that may be used to predict LBLOCA response in a CANDU reactor. The RD-14M experiment selected for the Best Estimate Plus Uncertainty study is a 44 mm (22.7%) inlet header break test with no Emergency Coolant Injection. This work has two objectives first is to simulate pipe break with RELAP and compare these results to those available from experiment and from comparable TRACE calculations. The second objective is to quantify uncertainty in the fuel element sheath (FES) temperature arising from model coefficient as well as input parameter uncertainties using Integrated Uncertainty Analysis package. RELAP calculated results are found to be in good agreement with those of TRACE and with those of experiments. The base case maximum FES temperature is 335.5 ℃ while that of 95% confidence 95^th percentile is 407.41 ℃ for the first order Wilk's formula. The experimental measurements fall within the predicted band and the trends and sensitivities are similar to those reported for the TRACE code.

• 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.