• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.663-670
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• 2019

Corrective thermal performance analysis is required for power plants' turbine cycles to determine the performance status of the cycle and improve the economic operation of the power plant. We developed a sectional classification method for the main feed-water flow to make precise corrections for the performance analysis based on the Performance Test Code (PTC) of the American Society of Mechanical Engineers (ASME). The method was developed for the estimation of the turbine cycle performance in a classified section. The classification is based on feature identification of the correlation status of the main feed-water flow measurements. We also developed predictive algorithms for the corrected main feed-water through a Kernel Regression (KR) model for each classified feature area. The method was compared with estimation using an Artificial Neural Network (ANN). The feature classification and predictive model provided more practical and reliable methods for the corrective thermal performance analysis of a turbine cycle.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1495-1504
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• 2021

An ultra-compact binary power generation plant converts thermal energy into electric power using temperature difference between heat source and cooling source. In the actual power generation environment, the characteristic value of the plant changes due to any negative effects such as environmental condition or corrosion of related equipment. If the characteristic value of the plant changes, it may lead to unstable output of the turbine in a conventional PID control system with fixed PID parameters. A Neuro PID control system based on Neural Network adaptively to adjust the PID parameters according to the change in the characteristic value of the plant is proposed in this paper. Discrete-time transfer function models to represent the dynamic characteristics near the operating point of the investigated plant are deduced, and a design strategy of the proposed control system is described. The proposed Neuro PID control system is compared with the conventional PID control system, and its effectiveness is demonstrated through the simulation results.

• Korean Journal of Materials Research
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• v.20 no.4
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• pp.223-227
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• 2010

Recently, one-dimensional semiconducting nanomaterials have attracted considerable interest for their potential as building blocks for fabricating various nanodevices. Among these semiconducting nanomaterials,, $SnO_2$ nanostructures including nanowires, nanorods, nanobelts, and nanotubes were successfully synthesized and their electrochemical properties were evaluated. Although $SnO_2$ nanowires and nanobelts exhibit fascinating gas sensing characteristics, there are still significant difficulties in using them for device applications. The crucial problem is the alignment of the nanowires. Each nanowire should be attached on each die using arduous e-beam or photolithography, which is quite an undesirable process in terms of mass production in the current semiconductor industry. In this study, a simple process for making sensitive $SnO_2$ nanowire-based gas sensors by using a standard semiconducting fabrication process was studied. The nanowires were aligned in-situ during nanowire synthesis by thermal CVD process and a nanowire network structure between the electrodes was obtained. The $SnO_2$ nanowire network was floated upon the Si substrate by separating an Au catalyst between the electrodes. As the electric current is transported along the networks of the nanowires, not along the surface layer on the substrate, the gas sensitivities could be maximized in this networked and floated structure. By varying the nanowire density and the distance between the electrodes, several types of nanowire network were fabricated. The $NO_2$ gas sensitivity was 30~200 when the $NO_2$ concentration was 5~20ppm. The response time was ca. 30~110 sec.

• Journal of Hydrogen and New Energy
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• v.33 no.5
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• pp.515-524
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• 2022

In this study, the on-site hydrogen production process for refueling stations that were not energy-optimized was improved through exergy analysis and heat exchange network synthesis. Furthermore, the process was scaled up from 30 Nm3/h to 150 Nm3/h to improve hydrogen production capacity. Exergy analysis results show that exergy destruction in the SMR reactor and the heat exchanger accounts for 58.1 and 19.8%, respectively. Thus, the process is improved by modifying the heat exchange network to reduce the exergy loss in these units. As a result of the process simulation analysis, thermal and exergy efficiency is improved from 75.7 to 78.6% and 68.1 to 70.4%, respectively. In conclusion, it is expected to improve the process efficiency when installing on-site hydrogen refueling stations.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.4
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• pp.259-264
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• 2023

In this study, we devised a parametric analysis workflow for efficiently analyzing the material properties of 3D woven materials. The parametric model uses wire spacing in the woven materials as a design parameter; we generated 2,500 numerical models with various combinations of these design parameters. Using MATLAB and ANSYS software, we obtained various material properties, such as bulk modulus, thermal conductivity, and fluid permeability of the woven materials, through a parametric batch analysis. We then used this large dataset of material properties to perform a regression analysis to validate the relationship between design variables and material properties, as well as the accuracy of numerical analysis. Furthermore, we constructed an artificial neural network capable of predicting the material properties of 3D woven materials on the basis of the obtained material database. The trained network can accurately estimate the material properties of the woven materials with arbitrary design parameters, without the need for numerical analyses.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3423-3440
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• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.

• Journal of the Korean Ceramic Society
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• v.49 no.4
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• pp.301-307
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• 2012

SiAlON glasses are silicates or alumino-silicates, containing Mg, Ca, Y or rare earth (RE) ions as modifiers, in which nitrogen atoms substitute for oxygen atoms in the glass network. These glasses are found as intergranular films and at triple point junctions in silicon nitride ceramics and these grain boundary phases affect their fracture behaviour. This paper provides an overview of the preparation of M-SiAlON glasses and outlines the effects of composition on properties. As nitrogen substitutes for oxygen in SiAlON glasses, increases are observed in glass transition temperatures, viscosities, elastic moduli and microhardness. These property changes are compared with known effects of grain boundary glass chemistry in silicon nitride ceramics. Oxide sintering additives provide conditions for liquid phase sintering, reacting with surface silica on the $Si_3N_4$ particles and some of the nitride to form SiAlON liquid phases which on cooling remain as intergranular glasses. Thermal expansion mismatch between the grain boundary glass and the silicon nitride causes residual stresses in the material which can be determined from bulk SiAlON glass properties. The tensile residual stresses in the glass phase increase with increasing Y:Al ratio and this correlates with increasing fracture toughness as a result of easier debonding at the glass/${\beta}-Si_3N_4$ interface.

• Journal of Ocean Engineering and Technology
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• v.28 no.5
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• pp.452-465
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• 2014

The application of friction stir welding (FSW) technology has been extended to all industries, including shipbuilding. A heat transfer analysis evaluates the weldability of a welded work piece, and elasto-plastic analysis predicts the residual stress and deformation after welding. A thermal elasto-plastic analysis based on the heat transfer analysis results is most frequently used today. However, its application to large objects such as offshore structures and hulls is impractical owing to its long computational time. This paper proposes a new method, namely an equivalent strain method using the inherent strain, to overcome the disadvantages of the extended analysis time. In the present study, a residual stress analysis of FSW was performed using this equivalent strain method. Additionally, in order to reflect the external constraints in FSW, the reaction force was predicted using a neural network, Finally, the approach was verified by comparing the experimental results and thermal elasto-plastic analysis results for the calculated residual stress distribution.

• Journal of information and communication convergence engineering
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• v.12 no.3
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• pp.186-192
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• 2014

Monolithic three-dimensional integrated chips (3D ICs) are an emerging technology that offers an integration density that is some orders of magnitude higher than the conventional through-silicon-via (TSV)-based 3D ICs. This is due to a sequential integration process that enables extremely small monolithic inter-tier vias (MIVs). For a monolithic 3D memory, we first explore the static random-access memory (SRAM) design. Next, for digital logic, we explore several design styles. The first is transistor-level, which is a design style unique to monolithic 3D ICs that are enabled by the ultra-high-density of MIVs. We also explore gate-level and block-level design styles, which are available for TSV-based 3D ICs. For each of these design styles, we present techniques to obtain the graphic database system (GDS) layouts, and perform a signoff-quality performance and power analysis. We also discuss various challenges facing monolithic 3D ICs, such as achieving 50% footprint reduction over two-dimensional (2D) ICs, routing congestion, power delivery network design, and thermal issues. Finally, we present design techniques to overcome these challenges.

• 한국태양에너지학회:학술대회논문집
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• 2008.04a
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• pp.209-214
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• 2008

Since the direct normal insolation is a main factor for designing any solar thermal power system, it is necessary to evaluate its characteristics all over the country. We have begun collecting direct normal insolation data since December 1992 at 16 different locations and considerable effort has been made for constructing a standard value from measured data at each station. KIER(Korea Institute of Energy Research)'s new data will be extensively used by solar thermal concentrating system users or designers as well as by research institutes. From the results, we can conclude that 1) Yearly mean 2.67 kWh/$m^2$/day of the direct normal insolation was evaluated for all days all over the 16 areas in Korea. 2) All day's direct normal insolation of spring and summer were 2.91 kWh/$m^2$/day and 2.23 kWh/$m^2$/day, and for fall and winter their values were 2.78 kWh/$m^2$/day and 2.77 kWh/$m^2$/day respectively. So, spring, fall and winter were higher, and summer was lower than the yearly mean value