• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.339-352
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• 2023

Prediction of the time-related traits of pebble flow inside pebble-bed HTGRs is of great significance for reactor operation and design. In this work, an image-driven approach with the aid of a convolutional neural network (CNN) is proposed to predict the remaining time of initially loaded pebbles and the time interval of paired flow images of the pebble bed. Two types of strategies are put forward: one is adding FC layers to the classic classification CNN models and using regression training, and the other is CNN-based deep expectation (DEX) by regarding the time prediction as a deep classification task followed by softmax expected value refinements. The current dataset is obtained from the discrete element method (DEM) simulations. Results show that the CNN-aided models generally make satisfactory predictions on the remaining time with the determination coefficient larger than 0.99. Among these models, the VGG19+DEX performs the best and its CumScore (proportion of test set with prediction error within 0.5s) can reach 0.939. Besides, the remaining time of additional test sets and new cases can also be well predicted, indicating good generalization ability of the model. In the task of predicting the time interval of image pairs, the VGG19+DEX model has also generated satisfactory results. Particularly, the trained model, with promising generalization ability, has demonstrated great potential in accurately and instantaneously predicting the traits of interest, without the need for additional computational intensive DEM simulations. Nevertheless, the issues of data diversity and model optimization need to be improved to achieve the full potential of the CNN-aided prediction tool.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.8
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• pp.82-90
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• 2019

We propose an equivalent model of a sintered metal mesh filter calculated by Ergun's equation and polynomial regression for the CFD analysis of breakaway devices at a hydrogen fueling station. CFD analysis of filters that cause high pressure loss is essential because breakaway devices in high-pressure hydrogen conditions require low pressure loss. A differential pressure experiment with a filter was performed in a low-pressure air condition considering similarities. An equivalent model was developed by deriving the resistance value by the polynomial regression using the experimental results. The results of CFD analysis using the equivalent model show that there was almost no error in the operating condition of the breakaway device compared to the experimental results. Through this work, we believe that the proposed equivalent model of a filter can be applied to the analysis of breakaway devices in hydrogen fueling stations. We will study how to optimize the shape and position of the filter in breakaway devices using the developed equivalent model.

• The Journal of Korean Physical Therapy
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• v.33 no.5
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• pp.224-230
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• 2021

Purpose: The purpose of this study was to investigate the effects of the fascia distortion model (FDM), one of the fascia treatments, on unstable ankle subjects. This was done through the chronic ankle instability tool (CAIT) questionnaire on maximum isometric muscle strength, proprioception, dynamic balance, and maximum angle. Methods: An experiment was conducted using the chronic ankle instability tool questionnaire on males and females in their twenties who suffered from ankle instability. Before the experiment, maximum isometric strength, proprioceptive, dynamic balance, and maximum angle were measured. The fascia distortion model was applied and then measurements were taken again to compare and analyze the changes. Analysis was carried out using the paired t-test. Results: After applying the fascia distortion model, maximum isometric strength, proprioceptive, dynamic balance, and maximum angle significantly improved (p<0.05). Conclusion: This study found that the fascia distortion model method was effective in improving maximum isometric strength, proprioceptive, dynamic balance, and maximum angle. The results suggest that the fascia distortion model method is a new intervention that could be used for subjects with chronic ankle instability.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.38-46
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• 2020

The accurate measurement of critical heat flux (CHF) in flow boiling is important for the safety requirement of the nuclear power plant to prevent sharp degradation of the convective heat transfer between the surface of the fuel rod cladding and the reactor coolant. In this paper, a System Engineering approach is used to develop a model that predicts the CHF using machine learning. The model is built using artificial neural network (ANN). The model is then trained, tested and validated using pre-existing database for different flow conditions. The Talos library is used to tune the model by optimizing the hyper parameters and selecting the best network architecture. Once developed, the ANN model can predict the CHF based solely on a set of input parameters (pressure, mass flux, quality and hydraulic diameter) without resorting to any physics-based model. It is intended to use the developed model to predict the DNBR under a large break loss of coolant accident (LBLOCA) in APR1400. The System Engineering approach proved very helpful in facilitating the planning and management of the current work both efficiently and effectively.

• Corrosion Science and Technology
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• v.22 no.1
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• pp.44-54
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• 2023

The rupture disk corrosion test (RDCT) method was recently developed to evaluate stress corrosion cracking (SCC) and was found to have great potential for the real-time detection of SCC initiation in a high temperature and pressure environment, simulating the primary water coolant of pressurized water reactors. However, it is difficult to directly measure the stress applied to a disk specimen, which is an essential factor in SCC initiation. In this work, finite element analysis (FEA) was performed using ABAQUS^TM to calculate the stress and deformation of a disk specimen. To determine the best mesh design for a thin disk specimen, hexahedron, hex-dominated, and tetrahedron models were used in FEA. All models revealed similar dome-shaped deformation behavior of the disk specimen. However, there was a considerable difference in stress distribution in the disk specimens. In the hex-dominated model, the applied stress was calculated to be the maximum at the dome center, whereas the stress was calculated to be the maximum at the dome edge in the hexahedron and tetrahedron models. From a comparison of the FEA results with deformation behavior and SCC location on the disk specimen after RDCT, the most proper FE model was found to be the tetrahedron model.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.910-917
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• 2011

CBTC(Communication Based Train Control) car-borne equipment, a part of the communication based train control system, mainly consists of automatic train protection(ATP) functions, automatic train operation(ATO) functions as well as the interface functions with other equipment including CBTC wayside equipment and train control management system etc. The CBTC car-borne software implementing ATP/ATO functions is a real-time embedded software requiring a high level of safety and reliability. To satisfy the requirements of the CBTC car-borne software, the model-based design techniques are applied with SCADE(Safety-Critical Application Development Environment) to the development of the CBTC car-borne software. In this paper, we illustrate the process modeling the car-borne ATP/ATO functions satisfying system requirement specification with system requirement management, modeling and document generation tools etc. supported by SCADE. In addition, the developed models corresponding to the ATP/ATO functions are applied to the train with CBTC car-borne equipment through its corresponding EN-50128 standards-compliant C code generated by the code generator. It is shown from the test result that the ATP/ATO models developed by SCADE work well while the trains are running in driverless operation mode.

• Journal of the Korean Society of Systems Engineering
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• v.12 no.2
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• pp.101-114
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• 2016

A model-based systems engineering (MBSE) approach to implementing hardware-based network cybersecurity controls for APR1400 non-safety data network is presented in this work. The proposed design was developed by implementing packet filtering and deep packet inspection functions to control the unauthorized traffic and malicious contents. Denial-of-Service (DoS) attack was considered as a potential cybersecurity issue that may threaten the data availability and integrity of DCS gateway servers. Logical design architecture was developed to simulate the behavior of functions flow. HDL-based physical architecture was modelled and simulated using Xilinx ISE software to verify the design functionality. For effective modelling process, enhanced function flow block diagrams (EFFBDs) and schematic design based on FPGA technology were together developed and simulated to verify the performance and functional requirements of network security controls. Both logical and physical design architectures verified that hardware-based cybersecurity controls are capable to maintain the data availability and integrity. Further works focus on implementing the schematic design to an FPGA platform to accomplish the design verification and validation processes.

• Proceedings of the Safety Management and Science Conference
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• 2013.11a
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• pp.393-400
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• 2013

Globally, smart phones have been rapidly distributed, which has led to changes in people's life cycle. Most people who are under 60 are supposed to use smart phones. Additionally, as the ratio of people who are interested in physical exercise is increasing, some applications for smart phones can manage dividual's exercise with the web servers. However, most of them can only check how much individual works out and cannot compare other's body type and life environment. Moreover, users cannot share their own data with others. This paper proposed the system which can resolve those kinds of problems through data mining techniques. The suggested model will have ability to figure out the relation between body type and the amount of exercise, find out if his work is proper from the result of classification and can pick out the features which is common to people who have similar body type and the amount of workout by applying data mining techiques. This model also will be able to recommend the proper amount of workout to each individual in order that they keep good health state efficiently.

• KIEAE Journal
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• v.7 no.6
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• pp.99-106
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• 2007

Tower crane is a large construction equipment which is extremely tall for its section when it is erected, with its high slenderness ratio, and it has a heavy load by itself due to large lifting stuff to handle. In line with the construction projects in these days which increasingly tend to become higher, larger and complex, the stuff and height subject to lifting are also getting larger and higher, which has also increased the risk of disastrous accidents. A stable foundation design thus to deal with the increasing self load becomes more important. When a typhoon Maemi swept the nation in 2003, as many as 43 tower cranes fell down or collapsed, causing a severe damage to the people and the properties. Considering such fatal damages, a technical evaluation of the stability to prevent the safety accident with the tower crane must be very crucial. Tower cranes operation in domestic construction sites, in fact, have been simply dependent on personal experience and intuition of the engineers. Particularly when it comes to the foundation design, it mostly depends on manufacturer's recommendation. The study hence was intended to develop the fundamental measures for granting the objective stability, instead of following the individual's experience only. The simulation model recommended in the study is expected to make a good commitment to achieving an effective lifting work as well as preventing the safety accident.

• Journal of the Korea Safety Management & Science
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• v.19 no.4
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• pp.189-208
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• 2017

This study is for figuring out major components of construction quality infrastructure and suggesting improvement plan by benchmarking global best practice model and analyzing gap between the model and sample practice. Based on improvement plan, researcher could derive major characters and components of construction quality infrastructure, and also about the priority among core competency components of construction quality experts. The result showed that the 'human resource' was ranked the best priority followed by 'method' 'environment', 'equipment' among major characters and components of construction quality infrastructure. Also, for core competency components of construction quality experts, 'number of years in work places' was the best priority, and the 'professional knowledge on engineering' was another priority ranked. Far from general perception on competency components, 'academic education' ranked the last. It may allude that the current education system in this country is not effective in developing competency of quality engineers. In summary, this study shows professionalism of quality engineer is the most important thing in all the components.