• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1008-1011
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• 1996

Most control systems of power plants are using classical PID controllers for their process control. In order to get the desired control performances, the correct tuning of PID controllers is very important. Sometimes, it is necessary to retune PID controllers after the change of system operating condition and system design change, etc. Commercial auto-tuning controllers such as relay feedback controller can be used for this purpose. However, using these controllers to the safety-critical systems of nuclear power plants may be cause of unsafe operation, because they are using test signals for tuning. A new system identification auto-tuning method without using test signal has been developed in this paper. This method uses process input/output signals for system identification of unknown control process. From the model information of control process which was obtained from system identification approach, the optimal PID parameters can be calculated. The method can be used in the safety-critical systems because it is not using test signals during system modeling process.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.460-465
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• 2003

The modeling of PEM (Proton Exchange Membrane) fuel cell system consisting of fuel cell stack and BOP (Balance of Plant) is presented in this paper. The effects of temperature, pressure (air, hydrogen), and humidity on the fuel cell system performance were mainly investigated using thermo-dynamical and electro-chemical equations. To understand the power distribution characteristics of fuel cell system, the effects of operating temperature and air pressure on maximum power and system power were also demonstrated. Through this study, we can get the basic insight into the fuel cell stack and BOP component sizing and it can be used effectively for the optimization of the practical fuel cell systems in purpose.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.6
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• pp.5-14
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• 1998

In this paper we propose a new approach to the development of the automatic vision system to examine and repair the steam generator tubes at remote distance. In nuclear power plants, workers are reluctant of works in steam generator because of the high radiation environment and limited working space. It is strongly recommended that the examination and maintenance works be done by an automatic system for the protection of the operator from the radiation exposure. Digital signal processors are used in implementing real time recognition and examination of steam generator tubes in the proposed vision system. Performance of proposed digital vision system is illustrated by simulation and experiment for similar steam generator model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.672-682
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• 2020

The Korea Nowadays, which is research on digital twin technology for efficient operation in various industrial/manufacturing sites, is being actively conducted, and gradual depletion of fossil fuels and environmental pollution issues require new renewable/eco-friendly power generation methods, such as wave power plants. In wave power generation, however, which generates electricity from the energy of waves, it is very important to understand and predict the amount of power generation and operational efficiency factors, such as breakdown, because these are closely related by wave energy with high variability. Therefore, it is necessary to derive a meaningful correlation between highly volatile data, such as wave height data and sensor data in an oscillating water column (OWC) chamber. Secondly, the methodological study, which can predict the desired information, should be conducted by learning the prediction situation with the extracted data based on the derived correlation. This study designed a workflow-based training model using a machine learning framework to predict the pressure of the OWC. In addition, the validity of the pressure prediction analysis was verified through a verification and evaluation dataset using an IoT sensor data to enable smart operation and maintenance with the digital twin of the wave generation system.

• Journal of the Korean Applied Science and Technology
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• v.33 no.3
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• pp.529-539
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• 2016

Modeling can be used to understand the atmospheric dispersion of air pollutants scientifically. Recent development of model computation enabled to simulate more diverse area. As flowing out from the emission source, the concentration profiles of air pollutants could be estimated in three dimensional space. This study used CALPUFF diffusion model to predict the diffusion of discharged NO2 and TSP on the atmosphere near a combined heat power plant and incinerator. It was investigated contribution concentration of the surrounding area by sources by comparing the actual measurement results and the results of the modeling. Contribution of emission sources to the local level of NO2 was found quite high particularly at the site, A-3. The estimated results by modelling revealed more significant effect on TSP at A-5.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3409-3416
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• 2023

Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling and Simulation (M&S) to investigate system response to many operational conditions, identify possible accidental scenarios and predict their evolution to undesirable consequences that are to be prevented or mitigated via the deployment of adequate safety barriers. Deep Learning (DL) and Artificial Intelligence (AI) can support M&S computationally by providing surrogates of the complex multi-physics high-fidelity models used for design. However, DL and AI are, generally, low-fidelity 'black-box' models that do not assure any structure based on physical laws and constraints, and may, thus, lack interpretability and accuracy of the results. This poses limitations on their credibility and doubts about their adoption for the safety assessment and licensing of novel reactor designs. In this regard, Physics Informed Neural Networks (PINNs) are receiving growing attention for their ability to integrate fundamental physics laws and domain knowledge in the neural networks, thus assuring credible generalization capabilities and credible predictions. This paper presents the use of PINNs as surrogate models for accidental scenarios simulation in Nuclear Power Plants (NPPs). A case study of a Loss of Heat Sink (LOHS) accidental scenario in a Nuclear Battery (NB), a unique class of transportable, plug-and-play microreactors, is considered. A PINN is developed and compared with a Deep Neural Network (DNN). The results show the advantages of PINNs in providing accurate solutions, avoiding overfitting, underfitting and intrinsically ensuring physics-consistent results.

• Advances in Energy Research
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• v.1 no.2
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• pp.127-146
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• 2013

Torrefaction technologies convert assorted biomass feedstocks into energy-concentrated, carbon neutral fuel that is economically transported and easily ground for blending with fossil coals at numerous power plants around the world without needs to retrofit. Utilization of torrefied biomass in conventional electric generating units may be an increasingly attractive alternative for electricity generation as aging power plants in the world need to be upgraded or improved. This paper examines the economic feasibility of torrefaction in different scenarios by modeling torrefaction plants producing 136,078 t/year (150,000 ton/year) biocoal from wood and corn stover. The utilization of biocoal blends in existing coal-fired power plants is modeled to determine the demand for this fuel in the context of emerging policies regulating emissions from coal in the U.S. setting. Opportunities to co-locate torrefaction facilities adjacent to corn ethanol plants and coal-fired power plants are explored as means to improve economics for collaborating businesses. Life cycle analysis was conducted in parallel to this economic study and was used to determine environmental impacts of converting biomass to biocoal for blending in coal-fired power plants as well as the use of substantial flows of off-gasses produced in the torrefaction process. Sensitivity analysis of the financial rates of return of the different businesses has been performed to measure impacts of different factors, whether input prices, output prices, or policy measures that render costs or rewards for the businesses.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.1-15
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• 2020

Nuclear power plants in South Korea are operated to cover the baseload demand. Hence they are operated at 100% rated power and do not deploy power tracking control except for startup, shutdown, or during transients. However, as the contribution of renewable energy in the energy mix increases, load follow operation may be needed to cover the imbalance between consumption and production due to the intermittent nature of electricity produced from the conversion of wind or solar energy. Load follow operation may be quite challenging since the operators need to control the axial power distribution and core reactivity while simultaneously conducting the power maneuvering. In this paper, a systems engineering approach for multi-physics load follow simulation of APR1400 is performed. RELAP5/SCDAPSIM/MOD3.4/3DKIN multi-physics package is selected to simulate the Korean Advanced Power Reactor, APR1400, under load follow operation to reflect the impact of feedback signals on the system safety parameters. Furthermore, the systems engineering approach is adopted to identify the requirements, functions, and physical architecture to provide a set of verification and validation activities that guide this project development by linking each requirement to a validation or verification test with predefined success criteria.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2696-2707
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• 2021

The purpose of this study is to investigate the efficiency of various structural modeling schemes for evaluating seismic performances and fragility of the reactor containment building (RCB) structure in the advanced power reactor 1400 (APR1400) nuclear power plant (NPP). Four structural modeling schemes, i.e. lumped-mass stick model (LMSM), solid-based finite element model (Solid FEM), multi-layer shell model (MLSM), and beam-truss model (BTM), are developed to simulate the seismic behaviors of the containment structure. A full three-dimensional finite element model (full 3D FEM) is additionally constructed to verify the previous numerical models. A set of input ground motions with response spectra matching to the US NRC 1.60 design spectrum is generated to perform linear and nonlinear time-history analyses. Floor response spectra (FRS) and floor displacements are obtained at the different elevations of the structure since they are critical outputs for evaluating the seismic vulnerability of RCB and secondary components. The results show that the difference in seismic responses between linear and nonlinear analyses gets larger as an earthquake intensity increases. It is observed that the linear analysis underestimates floor displacements while it overestimates floor accelerations. Moreover, a systematic assessment of the capability and efficiency of each structural model is presented thoroughly. MLSM can be an alternative approach to a full 3D FEM, which is complicated in modeling and extremely time-consuming in dynamic analyses. Specifically, BTM is recommended as the optimal model for evaluating the nonlinear seismic performance of NPP structures. Thereafter, linear and nonlinear BTM are employed in a series of time-history analyses to develop fragility curves of RCB for different damage states. It is shown that the linear analysis underestimates the probability of damage of RCB at a given earthquake intensity when compared to the nonlinear analysis. The nonlinear analysis approach is highly suggested for assessing the vulnerability of NPP structures.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.34 no.8
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• pp.308-316
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• 1985

This paper proposes an analytic tool for long-term generation expansion planning based on the maximum principle. Many research works have been performed in the field of generation expansion planning. But few works can be found with the maxinmum principle. A recently published one worked by professor Young Moon Park et al. shows remarkable improvements in modeling and computation. But this modeling allows only thermal units. This paper has extended Professor Park's model so that the optimal pumped-storage operation is taken into account. So the ability for practical application is enhanced. In addition, the analytic supply-shortage cost function is included. The maximum principle is solved by gradient search due to its simplicity. Every iteration is treated as if mathematical programming such that all controls from the initial to the terminal time are manipulated within the same plane. Proposed methodology is tested in a real scale power system and the simulation results are compared with other available package. Capability of proposed method is fully demonstrated. It is expected that the proposed method can be served as a powerful analytic tool for long-term generation expansion planning.