Uncertainty assessment for loss of flow accident of a 5MW pool-type research reactor

Yum, Soo-been,Park, Su-ki Elsevier 2017 Annals of nuclear energy Vol.109 No.-

<P><B>Abstract</B></P> <P>Best estimate plus uncertainty (BEPU) is a promising approach to the safety analysis of nuclear reactors, and the uncertainty calculation is a very important concern about it. BEPU ensures realistic safety margins and secures higher reactor effectiveness by adopting best-estimate codes and realistic input data with uncertainties, whereas the previous conservative analysis generates excessive conservatism by considering each input parameter separately. A loss of flow accident (LOFA) of a 5MW open-pool type research reactor was selected as a sample problem for a BEPU uncertainty assessment. We selected the failures of all primary cooling system (PCS) pumps, which would cause the abrupt reduction of flow and the reversal of core flow. The significant contributors to the reactor safety were identified and then input sets were sampled. For the uncertainty evaluation, 124 calculations were performed. This is the number of code runs required for a 95%/95% level with the 3rd order Wilk’s formula. The MOSAIQUE software developed by Korean Atomic Energy Research Institute (KAERI) was used for automated sampling of the uncertainty parameters, a global uncertainty calculation, and post processing of the results. The critical heat flux ratio (CHFR) and the fuel centerline temperature (FCT) were calculated at the 95%/95% level and were compared with those from conservative analyses. In addition, the impact of each design variables on the safety parameters was estimated by sensitivity analysis.</P> <P><B>Highlights</B></P> <P> <UL> <LI> BEPU analysis were performed with a scenario of PCS pumps fail simultaneously. </LI> <LI> The results from BEPU and conservative analysis were compared. </LI> <LI> The comparing result shows the applicability and advantages of a BEPU safety analysis. </LI> </UL> </P>

Uncertainty and sensitivity analyses for fuel temperature evaluations of U-Mo/Al plate-type dispersion fuel

Sweidan, Faris B.,Tahk, Young-wook,Yim, Jeong-Sik,Ryu, Ho Jin Elsevier 2018 Annals of nuclear energy Vol.120 No.-

<P><B>Abstract</B></P> <P>U-Mo/Al plate-type dispersion fuel is a promising candidate for the conversion of research reactor fuels from highly enriched to low-enriched uranium due to its high uranium density. The fuel temperature is a very important parameter, as it affects the performance of the fuel through various aspects, such as the formation of an interaction layer (IL) between the fuel particles and the matrix, swelling, and the release of fission gas. For these reasons, the fuel temperature as a function of the fission density was calculated for two representative heat flux profiles using best-estimate values and Monte Carlo simulations. Uncertainty and sensitivity analyses which utilized the uncertainties of the critical parameters were then conducted to determine the upper (maximum) and lower (minimum) bounds of the fuel temperature for the selected heat flux profiles. The uncertainty analysis used common uncertainty propagation approaches and a probabilistic sensitivity analysis (Monte Carlo simulation), randomly sampling numbers following a Gaussian distribution. Lastly, the Pearson correlation coefficient was used to identify the input uncertainties which influence the fuel temperature most in the sensitivity analysis. These analyses contribute to safety analyses and to the licensing process, as they are used in best-estimate approaches that apply realistic assumptions complemented with uncertainty analyses, such as the Best Estimate Plus Uncertainty (BEPU) approach.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Uncertainty and sensitivity analyses on U-Mo/Al dispersion fuel were conducted. </LI> <LI> Uncertainties of key parameters on the fuel temperature were evaluated. </LI> <LI> The fuel temperature as a function of the fission density was calculated. </LI> <LI> Pearson correlation coefficient was used to identify the influence of the input uncertainties. </LI> </UL> </P>

Uncertainties impact on the major FOMs for severe accidents in CANDU 6 nuclear power plant

R.M. Nistor-Vlad,D. Dupleac,G.L. Pavel Korean Nuclear Society 2023 Nuclear Engineering and Technology Vol.55 No.7

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.

Numerical experiments on order statistics method based on Wilks' formula for best-estimate plus uncertainty methodology

Han, Seola,Kim, Taewan Elsevier 2019 Journal of environmental management Vol.235 No.-

<P><B>Abstract</B></P> <P>The best-estimate plus uncertainty method has been widely used in safety analysis for nuclear power plants. In this method, it is very important to have a sound uncertainty propagation method which should model the propagation of both epistemic and aleatory uncertainties and provide clear interpretation of the result. A typical example of such methods for nuclear application is the order statistics method based on Wilks' formula. This method requires practically small number of simulations to obtain the desired tolerance limit and the number is independent of the number of uncertainty parameters. In addition, since the method is based on non-parametric statistics, the conclusions drawn by the method should have distribution-free characteristics. This study aims at assessing the characteristics of the order statistics method based on Wilks' formula by means of numerical experiments and focuses on one-sided test which is typically used for safety evaluation. The distribution-free characteristics have been assessed by using 21 trial distributions and fully random samples out of all trial distributions. The effect of the order of Wilks' formula on the estimated tolerance limit is investigated by examining the characteristics of the results from various orders. The analysis results clearly confirm the order-free and distribution-free characteristics of the tolerance limit estimated by the order statics method based on Wilks' formula. In addition, it is found that the application of higher-order Wilks' formula is recommended in order to achieve less conservative and sensitive tolerance limit.</P> <P><B>Highlights</B></P> <P> <UL> <LI> The validity of Wilks' formula was assessed by means of numerical experiments. </LI> <LI> Order- and distribution-free characteristics is confirmed. </LI> <LI> A lower-order formula is more likely to result in a more conservative tolerance limit. </LI> </UL> </P>

Using machine learning to forecast and assess the uncertainty in the response of a typical PWR undergoing a steam generator tube rupture accident

Nguyen Tran Canh Hai,Diab Aya 한국원자력학회 2023 Nuclear Engineering and Technology Vol.55 No.9

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

A Systems Engineering Approach to Predict the Success Window of FLEX Strategy under Extended SBO Using Artificial Intelligence

Alketbi, Salama Obaid,Diab, Aya The Korean Society of Systems Engineering 2020 시스템엔지니어링학술지 Vol.16 No.2

On March 11, 2011, an earthquake followed by a tsunami caused an extended station blackout (SBO) at the Fukushima Dai-ichi NPP Units. The accident was initiated by a total loss of both onsite and offsite electrical power resulting in the loss of the ultimate heat sink for several days, and a consequent core melt in some units where proper mitigation strategies could not be implemented in a timely fashion. To enhance the plant's coping capability, the Diverse and Flexible Strategies (FLEX) were proposed to append the Emergency Operation Procedures (EOPs) by relying on portable equipment as an additional line of defense. To assess the success window of FLEX strategies, all sources of uncertainties need to be considered, using a physics-based model or system code. This necessitates conducting a large number of simulations to reflect all potential variations in initial, boundary, and design conditions as well as thermophysical properties, empirical models, and scenario uncertainties. Alternatively, data-driven models may provide a fast tool to predict the success window of FLEX strategies given the underlying uncertainties. This paper explores the applicability of Artificial Intelligence (AI) to identify the success window of FLEX strategy for extended SBO. The developed model can be trained and validated using data produced by the lumped parameter thermal-hydraulic code, MARS-KS, as best estimate system code loosely coupled with Dakota for uncertainty quantification. A Systems Engineering (SE) approach is used to plan and manage the process of using AI to predict the success window of FLEX strategies under extended SBO conditions.

A Systems Engineering Approach for Predicting NPP Response under Steam Generator Tube Rupture Conditions using Machine Learning

Tran Canh Hai Nguyen,Aya Diab 한국시스템엔지니어링학회 2022 시스템엔지니어링학술지 Vol.18 No.2

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

A SE Approach for Machine Learning Prediction of the Response of an NPP Undergoing CEA Ejection Accident

Ditsietsi Malale,Aya Diab The Korean Society of Systems Engineering 2023 시스템엔지니어링학술지 Vol.19 No.2

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.

A SE Approach for Real-Time NPP Response Prediction under CEA Withdrawal Accident Conditions

Felix Isuwa Wapachi,Aya Diab 한국시스템엔지니어링학회 2022 시스템엔지니어링학술지 Vol.18 No.2

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.

SCALING ANALYSIS IN BEPU LICENSING OF LWR

FRANCESCO D’AURIA,MARCO LANFREDINI,NIKOLAUS MUELLNER 한국원자력학회 2012 Nuclear Engineering and Technology Vol.44 No.6

“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.