• International conference on construction engineering and project management
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• 2015.10a
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• pp.696-697
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• 2015

Cost, schedule, and quality are the three major performance indicators for any construction project. Under the globalized competition in the nuclear industry, researchers and practitioners have also explored a systemized and integrated management system for cost, schedule, and quality. In order to address this issue, the concept of earned value management system (EVMS) has been often utilized. However, implementing EVMS for a mega-project of nuclear power plant (NPP) construction requires extensive overhead efforts. Though previous studies proposed structures and methods for effective NPP EVMS, there has been no legitimate study for data collection strategy for practical implementation. In this context, the purpose of this paper is to develop an effective data collection strategy for NPP EVMS. Firstly, the barriers to practical NPP EVMS were identified based on literature review and expert interviews. Strategies for data collection were then developed based on different phases of project life cycle. This study focuses on the 'life-cycle integrated progress management system' for NPP construction from an owner's perspective Therefore, results of this study can be used as a guide for preparing request for proposals (RFP) of an NPP owner organization.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2026-2033
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• 2023

Due to Thailand's nuclear energy public acceptance problem, the understanding of nuclear energy public perception was the key factor affecting to re-consideration of the nuclear energy program. Thailand Institute of Nuclear Technology and its alliances together developed the classification model for the nuclear energy public perception from the big data comments on social media using Facebook using deep transfer learning. The objective was to insight into the Thailand nuclear energy public perception on Facebook social media platform using sentiment analysis. The supervised learning was used to generate up-to-date classification model with more than 80% accuracy to classify the public perception on nuclear power plant news on Facebook from 2009 to 2022. The majority of neutral sentiments (80%) represented the opportunity for Thailand to convince people to receive a better nuclear perception. Negative sentiments (14%) showed support for other alternative energies due to nuclear accident concerns while positive sentiments (6%) expressed support for innovative nuclear technologies.

• Nuclear Engineering and Technology
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• v.52 no.2
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• pp.287-295
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• 2020

Group method of data handling (GMDH) is considered one of the earliest deep learning methods. Deep learning gained additional interest in today's applications due to its capability to handle complex and high dimensional problems. In this study, multi-layer GMDH networks are used to perform uncertainty quantification (UQ) and sensitivity analysis (SA) of nuclear reactor simulations. GMDH is utilized as a surrogate/metamodel to replace high fidelity computer models with cheap-to-evaluate surrogate models, which facilitate UQ and SA tasks (e.g. variance decomposition, uncertainty propagation, etc.). GMDH performance is validated through two UQ applications in reactor simulations: (1) low dimensional input space (two-phase flow in a reactor channel), and (2) high dimensional space (8-group homogenized cross-sections). In both applications, GMDH networks show very good performance with small mean absolute and squared errors as well as high accuracy in capturing the target variance. GMDH is utilized afterward to perform UQ tasks such as variance decomposition through Sobol indices, and GMDH-based uncertainty propagation with large number of samples. GMDH performance is also compared to other surrogates including Gaussian processes and polynomial chaos expansions. The comparison shows that GMDH has competitive performance with the other methods for the low dimensional problem, and reliable performance for the high dimensional problem.

• Nuclear Engineering and Technology
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• v.48 no.1
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• pp.114-128
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• 2016

The development of operational performance indicators is of utmost importance for nuclear power plants, since they measure, track, and trend plant operation. Leading indicators are ideal for reducing the likelihood of consequential events. This paper describes the operational data analysis of the information contained in the Corrective Action Program. The methodology considers human error and organizational factors because of their large contribution to consequential events. The results include a tool developed from the data to be used for the identification, prediction, and reduction of the likelihood of significant consequential events. This tool is based on the resilience curve that was built from the plant's operational data. The stress is described by the number of unresolved condition reports. The strain is represented by the number of preventive maintenance tasks and other periodic work activities (i.e., baseline activities), as well as, closing open corrective actions assigned to different departments to resolve the condition reports (i.e., corrective action workload). Beyond the identified resilience threshold, the stress exceeds the station's ability to operate successfully and there is an increased likelihood that a consequential event will occur. A performance indicator is proposed to reduce the likelihood of consequential events at nuclear power plants.

• Nuclear Engineering and Technology
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• v.52 no.10
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• pp.2262-2273
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• 2020

Digital twin technology can provide significant value for the prognostics and health management (PHM) of critical plant components by improving insight into system design and operating conditions. Digital twinning of systems can be utilized for anomaly detection, diagnosis and the estimation of the system's remaining useful life in order to optimize operations and maintenance processes in a nuclear plant. In this regard, a conceptual framework for the application of digital twin technology for the prognosis of Control Element Drive Mechanism (CEDM), and a data-driven approach to anomaly detection using coil current profile are presented in this study. Health management of plant components can capitalize on the data and signals that are already recorded as part of the monitored parameters of the plant's instrumentation and control systems. This work is focused on the development of machine learning algorithm and workflow for the analysis of the CEDM using the recorded coil current data. The workflow involves features extraction from the coil-current profile and consequently performing both clustering and classification algorithms. This approach provides an opportunity for health monitoring in support of condition-based predictive maintenance optimization and in the development of the CEDM digital twin model for improved plant safety and availability.

• Nuclear Engineering and Technology
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• v.52 no.1
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• pp.87-100
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• 2020

Performance shaping factors (PSFs) in a human reliability analysis (HRA) are one that may influence human performance in a task. Most currently applicable HRA methods for nuclear power plants (NPPs) use PSFs to highlight human error contributors and to adjust basic human error probabilities (HEPs) that assume nominal conditions of NPPs. Thus far, the effects of PSFs have been treated independently. However, many studies in the fields of psychology and human factors revealed that there may be relationships between PSFs. Therefore, the inter-relationships between PSFs need to be studied to better reflect their effects on operator errors. This study investigates these inter-relationships using two data sources and also suggests a context-based approach to treat the inter-relationships between PSFs. Correlation and factor analyses are performed to investigate the relationship between PSFs. The data sources are event reports of unexpected reactor trips in Korea and an experiment conducted in a simulator featuring a digital control room. Thereafter, context-based approaches based on the result of factor analysis are suggested and the feasibility of the grouped PSFs being treated as a new factor to estimate HEPs is examined using the experimental data.

• Nuclear Engineering and Technology
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• v.49 no.3
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• pp.484-494
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• 2017

In industrial plants such as nuclear power plants, system operations are performed by embedded controllers orchestrated by Supervisory Control and Data Acquisition (SCADA) software. A targeted attack (also termed a control aware attack) on the controller/SCADA software can lead a control system to operate in an unsafe mode or sometimes to complete shutdown of the plant. Such malware attacks can result in tremendous cost to the organization for recovery, cleanup, and maintenance activity. SCADA systems in operational mode generate huge log files. These files are useful in analysis of the plant behavior and diagnostics during an ongoing attack. However, they are bulky and difficult for manual inspection. Data mining techniques such as least squares approximation and computational methods can be used in the analysis of logs and to take proactive actions when required. This paper explores methodologies and algorithms so as to develop an effective monitoring scheme against control aware cyber attacks. It also explains soft computation techniques such as the computational geometric method and least squares approximation that can be effective in monitor design. This paper provides insights into diagnostic monitoring of its effectiveness by attack simulations on a four-tank model and using computation techniques to diagnose it. Cyber security of instrumentation and control systems used in nuclear power plants is of paramount importance and hence could be a possible target of such applications.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1088-1099
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• 2021

Time-frequency analysis technique is an effective analysis tool for non-stationary processes. In the field of reactor neutron noise, the time-frequency analysis method has not been thoroughly researched and widely used. This work has studied the time-frequency analysis of the reactor neutron noise experimental signals under bubble disturbance and control rod vibration. First, an experimental platform was established, and it could be employed to reactor neutron noise experiment and data acquisition. Secondly, two types of reactor neutron noise experiments were performed, and valid experimental data was obtained. Finally, time-frequency analysis was conducted on the experimental data, and effective analysis results were obtained in the low-frequency part. Through this work, it can be concluded that the time-frequency analysis technique can effectively investigate the core dynamics behavior and deepen the identification of the unstable core process.

• Nuclear Engineering and Technology
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• v.54 no.7
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• pp.2359-2366
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• 2022

Selecting graphite grades with superior irradiation characteristics is important task for designers of graphite moderation reactors. To provide reference information and data for graphite selection, the effects of irradiation on three fine-grained, iso-molded nuclear grade graphites, ETU-10, IG-110, and NBG-25, were compared based on irradiation-induced changes in volume, thermal conductivity, dynamic Young's modulus, and coefficient of thermal expansion. Data employed in this study were obtained from reported irradiation test results in the high flux isotope reactor (HFIR)(ORNL) (ETU-10, IG-110) and high flux reactor (HFR)(NRL) (IG-110, NBG-25). Comparisons were made based on the irradiation dose and irradiation temperature. Overall, the three grades showed similar irradiation-induced property change behaviors, which followed the historic data. More or less grade-sensitive behaviors were observed for the changes in volume and thermal conductivity, and, in contrast, grade-insensitive behaviors were observed for dynamic Young's modulus and coefficient of thermal expansion changes. The ETU-10 of the smallest grain size appeared to show a relatively smaller VC to IG-110 and NBG-25. Drastic decrease in the difference in thermal conductivity was observed for ETU-10 and IG-110 after irradiation. The similar irradiation-induced properties changing behaviors observed in this study especially in the DYM and CTE may be attributed to the assumed similar microstructures that evolved from the similar size coke particles and the same forming method.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3352-3358
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• 2023

Nuclear medicine seems to be a decent choice of medicine in the recent decade. The radioactive isotopes ⁵¹Cr, ⁸⁹Sr, ⁹⁹Tc, ¹³¹I, ¹³³Xe, ¹³⁷Cs and ¹⁵³Sm are extremely essential in nuclear medicine. The excitation functions of the ⁵⁴Fe (n, α) ⁵¹Cr, ⁹²Zr (n, α) ⁸⁹Sr, ¹⁰²Rh (n, α) ⁹⁹Tc, 134Cs (n, α) ¹³¹I, ¹³⁶Ba (n, α) ¹³³Xe, ¹⁴⁰La (n, α) ¹³⁷Cs and ¹⁵⁶Gd (n, α) ¹⁵³Sm reactions were calculated in this study using the EMPIRE 3.2.3 and TALYS 1.95 nuclear codes. Additionally, the cross sections at 14-15 MeV were calculated using empirical formulae and the experimental data. The computer codes were compared to the experimental data and Empirical formulas as well as the evaluated data (TENDL 2021, JENDL 3.3, JENDL 5, JEFF 3.3, EAF 2010, CENDL 3.1, CENDL 3.2, ROSFOND 2010, FENDL 3.2 b, and BROND 3.1).