• Nuclear Engineering and Technology
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• v.3 no.2
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• pp.77-97
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• 1971

The experimental fission cross-section data of U-238, Np-237 and Th-232, published up to the end of 1970, are reviewed and analyzed between their respective thresholds and 20.0 MeV. The results of a statistical analysis of the available data, performed with a weighted Least-squares Orthogonal Polynomial Pitting computer programme are presented in the form of point-wise cross-section values together with their uncertainties, and in the form of graphs of the fitted curves with an indication of a region of 95% statistical confidence level. An estimate of the fission spectrum weighted average cross-sections and their respective uncertainties is also given.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.13 no.1
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• pp.48-53
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• 2017

We diagnosed the defect using the data obtained from the nuclear power plant simulator. In this paper, we diagnosed faults in the nuclear power plant system for discovery instead of the traditional single-component or device unit. We created the six fault scenarios and used a fault simulator to obtain the fault data. It was extracted pattern from acquired failure data. Neural network model was trained and simple pattern matching algorithm was applied. We presented a simulation result and confirmed that the applied algorithm works correctly.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2017.11a
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• pp.185-186
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• 2017

Even large construction projects of nuclear power plant construction and production data is increasing dramatically due to the introduction of ICT technologies, such as 3D scanning technology, wireless communication technology, virtual construction management technology. There are various attributes and types of data to be produced and managed because the documents generated by the contract method are different from the cost processing method. According to the requirements of the international nuclear bid, it is required to present the cost that is calculated based on resource quantity. This research considers ways in which the cost management based on the resource quantity.

• ALGAE
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• v.19 no.2
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• pp.79-90
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• 2004

Nuclear DNA contents of spermatia from eight ceramiacean and four dasyacean algae (Ceramiales, Rhodophyta) and microspores from two land plants were estimated by fluorescence microscopic image processing and their nuclear SSU rDNA sequence data were analyzed. In frequency distribution patterns, the DAPI-stained nuclear volume (NV) of spermatia showed two peaks corresponding to 1C and 2C. Nuclear 2C DNA contents estimated from NV were 0.45-2.31 pg in ceramiacean and 0.40-0.57 pg in dasyacean algae and 8.42-9.51 pg in two land plants, Capsicum annuum and Nicotiana tabacum. By nuclear patterning of vegetative cells derived from an apical cell, 2C DNA contents of spermatia were 2.31 pg in an alga having uninucleate and non-polyploid nucleus (Aglaothamnion callophyllidicola), 0.45-1.94 pg in algae having uninucleate and polyploid nucleus (Antithamnion spp. and Pterothamnion yezoense), and 0.40-0.62 pg in algae having multinucleate and non-polyploid nuclei (Griffithsia japonica and dasyacean algae). Each mature spermatium and microspore (pollen grain) seemed to have a 2C nucleus, which may provide a genetic buffering system to protect the genetic content of a spermatium and microspore from potentially lethal mutations. Nuclear DNA content and SSU rDNA sequence of Antithamnion sparsum from Korea were reasonably different from those of Antithamnion densum from France. The data did not support the previous taxonomic studies that these two taxa could be conspecific.

• Nuclear Engineering and Technology
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• v.52 no.7
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• pp.1436-1442
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• 2020

Nuclear infrastructure systems play an important role in national security. The functions and missions of nuclear infrastructure systems are vital to government, businesses, society and citizen's lives. It is crucial to design nuclear infrastructure for scalability, reliability and robustness. To do this, we can use machine learning, which is a state of the art technology used in various fields ranging from voice recognition, Internet of Things (IoT) device management and autonomous vehicles. In this paper, we propose to design and develop a machine learning algorithm to perform predictive maintenance of nuclear infrastructure. Support vector machine and logistic regression algorithms will be used to perform the prediction. These machine learning techniques have been used to explore and compare rare events that could occur in nuclear infrastructure. As per our literature review, support vector machines provide better performance metrics. In this paper, we have performed parameter optimization for both algorithms mentioned. Existing research has been done in conditions with a great volume of data, but this paper presents a novel approach to correlate nuclear infrastructure data samples where the density of probability is very low. This paper also identifies the respective motivations and distinguishes between benefits and drawbacks of the selected machine learning algorithms.

• Nuclear Engineering and Technology
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• v.52 no.2
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• pp.366-372
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• 2020

Using several accessible published data sets, we analyzed the temporal change of ¹³⁷Cs radioactivity (per unit mass of sample) in marine sediments and investigated the effect of the water content of sediment on the ¹³⁷Cs radioactivity, to understand the behavior of ¹³⁷Cs present in marine environments. The ¹³⁷Cs radioactivity in sediments decreased more slowly in the Baltic Sea (near the Chernobyl nuclear power plant) than in the ocean near the Fukushima Daiichi nuclear power plant (FDNPP). The ¹³⁷Cs radioactivity in the sediment near the FDNPP tended to increase as the water content increased, and the water content decreased at certain sampling sites near the FDNPP for several years. Additionally, the decrease in the water content contributed to 51.2% of the average ¹³⁷Cs radioactivity decrease rate for the same period. Thus, it may be necessary to monitor both the ¹³⁷Cs radioactivity and the water content for marine sediments to track the ¹³⁷Cs that was discharged from the sites of Chernobyl and Fukushima nuclear power plants where severe accidents occurred.

• Nuclear Engineering and Technology
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• v.50 no.1
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• pp.151-158
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• 2018

The neutron powder diffractometer (NPD) is used to study a variety of technologically important and scientifically driven materials such as superconductors, multiferroics, catalysts, alloys, ceramics, cements, colossal magnetoresistance perovskites, magnets, thermoelectrics, zeolites, pharmaceuticals, etc. Monte Carlo-based codes are powerful tools to evaluate the neutronic behavior of the NPD. In the present study, MCNPX 2.6.0 and Vitess 3.3a codes were applied to simulate NPD facilities, which could be equipped with different optic devices such as pyrolytic graphite or neutron chopper. So, the Monte Carlo-based codes were used to simulate the NPD facility of the 5 MW Tehran Research Reactor. The simulation results were compared to the experimental data. The theoretical results showed good conformity to experimental data, which indicates acceptable performance of the Vitess 3.3a code in the neutron optic section of calculations. Another extracted result of this work shows that application of neutron chopper instead of monochromator could be efficient to keep neutron flux intensity higher than $10^6n/s/cm^2$ at sample position.

• Nuclear Engineering and Technology
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• v.56 no.1
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• pp.64-69
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• 2024

According to the market research report, the nuclear decommissioning services market is currently experiencing considerable growth, with a projected Compound Annual Growth Rate (CAGR) of nearly 13% during the 2020-2024 forecast period. This expansion is primarily fueled by the advancement of Industry 4.0, in conjunction with the emergence of cutting-edge technologies such as the Internet of Things, big data, artificial intelligence, and 5G. Even though the fact that robots have already been utilized in the nuclear industry, their adoption has been hindered by conservative regulations. However, the nuclear decommissioning market presents an opportunity for the advancement of robotics technology. The British have already invested heavily in encouraging the use of intelligent robots for nuclear decommissioning, and other countries, such as Taiwan, should follow suit. Taiwan's flourishing robotics development industry in manufacturing, logistics, and other domains can be leveraged to introduce advanced robotics in the decommissioning of its nuclear power plants. By doing so, Taiwan can establish itself as a competitive player in the nuclear decommissioning services market for the next two decades.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2600-2609
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• 2021

Low cycle fatigue behaviors of SA508-3 low-alloy steel were investigated in room-temperature air, high-temperature air and in light water reactor (LWR) water environments. The fatigue mean curve and design curve for the low-alloy steel are developed based on the fatigue data in room-temperature and high-temperature air. The environmental fatigue model for low-alloy steel is developed by the environmental fatigue correction factor (F_en) methodology based on the fatigue data in LWR water environments with the consideration of effects of strain rate, temperature, and dissolved oxygen concentration on the fatigue life.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3275-3285
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• 2021

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies.