• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.1-18
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• 2021

The criticality analysis of VVER-1000 mock-up benchmark experiments from the LR-0 research reactor operated by the Research Center Rez in the Czech Republic has been conducted with the MCS Monte Carlo code developed at the Computational Reactor Physics and Experiment laboratory of the Ulsan National Institute of Science and Technology. The main purpose of this work is to evaluate the newest ENDF/B-VIII.0 nuclear data library against the VVER-1000 mock-up integral experiments and to validate the criticality analysis capability of MCS for light water reactors with hexagonal fuel lattices. A preliminary code/code comparison between MCS and MCNP6 is first conducted to verify the suitability of MCS for the benchmark interpretation, then the validation against experimental data is performed with both ENDF/B-VII.1 and ENDF/B-VIII.0 libraries. The investigated experimental data comprises six experimental critical configurations and four experimental pin-by-pin power maps. The MCS and MCNP6 inputs used for the criticality analysis of the VVER-1000 mock-up are available as supplementary material of this article.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.558-567
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• 2024

Diagnostic support systems are being researched to assist operators in identifying and responding to abnormal events in a nuclear power plant. Most studies to date have considered single abnormal events only, for which it is relatively straightforward to obtain data to train the deep learning model of the diagnostic support system. However, cases in which multiple abnormal events occur must also be considered, for which obtaining training data becomes difficult due to the large number of combinations of possible abnormal events. This study proposes an approach to maintain diagnostic performance for multiple abnormal events by training a deep learning model with data on single abnormal events only. The proposed approach is applied to an existing algorithm that can perform feature selection and multi-label classification. We choose an extremely randomized trees classifier to select dedicated monitoring parameters for target abnormal events. In diagnosing each event occurrence independently, two-channel convolutional neural networks are employed as sub-models. The algorithm was tested in a case study with various scenarios, including single and multiple abnormal events. Results demonstrated that the proposed approach maintained diagnostic performance for 15 single abnormal events and significantly improved performance for 105 multiple abnormal events compared to the base model.

• Nuclear Engineering and Technology
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• v.44 no.3
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• pp.323-330
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• 2012

The development of data-based models requires uncertainty analysis to explain the accuracy of their predictions. In this paper, an uncertainty analysis of the support vector regression (SVR) model, which is a data-based model, was performed because previous research showed that the SVR method accurately estimates the collapse moments of wall-thinned pipe bends and elbows. The uncertainty analysis method used in this study was an analytic uncertainty analysis method, and estimates with a 95% confidence interval were obtained for 370 test data points. From the results, the prediction interval (PI) was very narrow, which means that the predicted values are quite accurate. Therefore, the proposed SVR method can be used effectively to assess and validate the integrity of the wall-thinned pipe bends and elbows.

• Korean Journal of Computational Design and Engineering
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• v.6 no.2
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• pp.140-146
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• 2001

A nuclear Power Plant is composed of a number of mechanical components. Maintaining the integrity of these components is one of the most critical issues in nuclear industry. In order to evaluate the integrity of these mechanical components, a lot of data are required including inspection data, geometrical data, material properties, etc. Therefore, an effective database system is essential to manage the integrity of nuclear power plant. For this purpose, an internet based virtual reality environment and web database system was proposed. The developed virtual reality environment provides realistic geometrical configurations of mechanical components using VRML (Virtual Reality Modeling Language). The virtual reality environment was linked with the web database, which can manage the required data for the integrity evaluation. The proposed system is able to share the information regarding the integrity evaluation through internet, and thus, will be suitable for an integrated system for the maintenance of mechanical components.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.6
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• pp.734-743
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• 1999

In this paper, a real-time communication method using a PICNET-NP(Plant instrumentation and Control Network for Nuclear Power plant) is proposed with an analysis of the control network requirements of DCS(Distributed Control System) in nuclear power plants. The method satisfies deadline in case of worst data traffics by considering aperiodic and periodic real-time data and others. In addition, the method was used to analyze the data characteristics of the DCS in existing nuclear power plant. The result shows that use of this method meets the response time requirement(100ms).

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.414-422
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• 2022

Presented in this paper is a methodology for combining a Bayesian statistical approach with Data Quality Objectives (a structured decision-making method) to provide increased levels of confidence in analytical data when approaching a waste boundary. Development of sampling and analysis plans for the characterisation of radioactive waste often use a simple, one pass statistical approach as underpinning for the sampling schedule. Using a Bayesian statistical approach introduces the concept of Prior information giving an adaptive sample strategy based on previous knowledge. This aligns more closely with the iterative approach demanded of the most commonly used structured decision-making tool in this area (Data Quality Objectives) and the potential to provide a more fully underpinned justification than the more traditional statistical approach. The approach described has been developed in a UK regulatory context but is translated to a waste stream from the Fukushima Daiichi Nuclear Power Station to demonstrate how the methodology can be applied in this context to support decision making regarding the ultimate disposal option for radioactive waste in a more global context.

• Nuclear Engineering and Technology
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• v.50 no.1
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• pp.35-42
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• 2018

This article presents clean core criticality calculations and control rod worth calculations for TRIGA (Training, Research, Isotope production-General Atomics) Mark II research reactor benchmark cores using Winfrith Improved Multi-group Scheme-D/4 (WIMS-D/4) and Program for Reactor In-core Analysis using Diffusion Equation (PRIDE) codes. Cores 133 and 134 were analyzed in 2-D (r, ${\theta}$) and 3-D (r, ${\theta}$, z), using WIMS-D/4 and PRIDE codes. Moreover, the influence of cross-section data was also studied using various libraries based on Evaluated Nuclear Data File (ENDF/B-VI.8 and VII.0), Joint Evaluated Fission and Fusion File (JEFF-3.1), Japanese Evaluated Nuclear Data Library (JENDL-3.2), and Joint Evaluated File (JEF-2.2) nuclear data. The simulation results showed that the multiplication factor calculated for all these data libraries is within 1% of the experimental results. The reactivity worth of the control rods of core 134 was also calculated with different homogenization approaches. A comparison was made with experimental and reported Monte Carlo results, and it was found that, using proper homogenization of absorber regions and surrounding fuel regions, the results obtained with PRIDE code are significantly improved.

• Nuclear Engineering and Technology
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• v.40 no.1
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• pp.69-76
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• 2008

Most pressurized water reactors use Venturi flow meters to measure the feedwater flow rate. However, fouling phenomena, which allow corrosion products to accumulate and increase the differential pressure across the Venturi flow meter, can result in an overestimation of the flow rate. In this study, a soft-sensing model based on fuzzy support vector regression was developed to enable accurate on-line prediction of the feedwater flow rate. The available data was divided into two groups by fuzzy c means clustering in order to reduce the training time. The data for training the soft-sensing model was selected from each data group with the aid of a subtractive clustering scheme because informative data increases the learning effect. The proposed soft-sensing model was confirmed with the real plant data of Yonggwang Nuclear Power Plant Unit 3. The root mean square error and relative maximum error of the model were quite small. Hence, this model can be used to validate and monitor existing hardware feedwater flow meters.

• Nuclear Engineering and Technology
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• v.49 no.5
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• pp.996-1005
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• 2017

In this paper, nuclear data for cross sections of the $^{64}Zn(n,2n)^{63}Zn$, $^{64}Zn(n,3n)^{62}Zn$, $^{64}Zn(n,p)^{64}Cu$, $^{66}Zn(n,2n)^{65}Zn$, $^{66}Zn(n,p)^{66}Cu$, $^{67}Zn(n,p)^{67}Cu$, $^{68}Zn(n,p)^{68}Cu$, and $^{68}Zn(n,{\alpha})^{65}Ni$ reactions were studied for neutron energies up to 40 MeV. In the nuclear model calculations, TALYS 1.6, ALICE/ASH, and EMPIRE 3.2 codes were used. Furthermore, the nuclear data for the (n,2n) and (n,p) reaction channels were also calculated using various cross-section systematics at energies around 14-15 MeV. The code calculations were analyzed and obtained using the different level densities in the exciton model and the geometry-dependent hybrid model. The results obtained from the excitation function calculations are discussed and compared with literature experimental data, ENDF/B-VII.1, and the TENDL-2015 evaluated data.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.100-108
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• 2023

In this study, a neural network model inspired by a one-dimensional convolution U-net is developed to automatically accelerate edge localized mode (ELM) detection from big diagnostic data of fusion devices and increase the detection accuracy regardless of the hyperparameter setting. This model recognizes the input signal patterns and overcomes the problems of existing detection algorithms, such as the prominence algorithm and those of differential methods with high sensitivity for the threshold and signal intensity. To train the model, 10 sets of discharge radiation data from the KSTAR are used and sliced into 11091 inputs of length 12 ms, of which 20% are used for validation. According to the receiver operating characteristic curves, our model shows a positive prediction rate and a true prediction rate of approximately 90% each, which is comparable to the best detection performance afforded by other algorithms using their optimized hyperparameters. The accurate and automatic ELM-burst detection methodology used in our model can be beneficial for determining plasma properties, such as the ELM frequency from big data measured in multiple experiments using machines from the KSTAR device and ITER. Additionally, it is applicable to feature detection in the time-series data of other engineering fields.