• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.18 no.12
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• pp.947-954
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• 2006

Building energy consumption according to the ventilation has been considered to be an important subject. The purpose of this study is to investigate the cooling and heating loads in a test space with a forced ventilating system. In the test space, on/off controlled air-conditioning and forced ventilating facility were operated between 8 : 30 to 21 : 00 during 4 days and some important data like temperatures and energy consumption were measured to obtain actual thermal loads. The simulation was carried out in a mode of temperature level control using a TRNSYS 15.3 with a precisely measured air change amount and performance data of air-conditioner. Heating load and cooling load including sensible and latent were compared between by experiment and by simulation. Both of thermal loads associated with ventilation show a close agreement within an engineering tolerance.

• The KIPS Transactions:PartD
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• v.15D no.2
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• pp.263-270
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• 2008

Research of ubiquitous computing becomes more popular topic along with the rapid development of wireless technologies. Firstly, research and development on RFID focuses on manufacturing and retail sectors, because it can improve supply chain efficiency. But, it changes to USN (Ubiquitous Sensor Network) by adding a sensor and wireless network technologies on it. In this research, we design and implement the electric facility management service framework to collect real time information of electric facility using RFID/USN. In electric power industry, it is important the supply of energy must be guaranteed. So many power utilities control and supervise the transmission line to avoid power failures. Utilities install many types of sensor to monitor important facilities by wired network such as optical cable and PLC. In this research, we develop the sensor node which is small, easy to install and using wired network. We design the service framework for electric facility management to collect data using RFID tag, reader and wireless sensor nodes and implement the electric facility management service.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.39-44
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• 2023

Renewable energy such as solar and wind power is a resource that is sensitive to weather conditions and environmental changes. Since the amount of power generated by a facility can vary depending on the installation location and structure, it is important to accurately predict the amount of power generation. Using meteorological data, a data preprocessing process based on principal component analysis was conducted to monitor the relationship between features that affect energy production prediction. In addition, in this study, the prediction was tested by reconstructing the dataset according to the sensitivity and applying it to the machine learning model. Using the proposed model, the performance of energy production prediction using random forest regression was confirmed by predicting energy production according to the meteorological environment for new and renewable energy, and comparing it with the actual production value at that time.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.85-92
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• 2021

Ambient Air Vaporizer (AAV) is an essential facility in the process of generating natural gas that uses air in the atmosphere as a medium for heat exchange to vaporize liquid natural gas into gas-state gas. AAV is more economical and eco-friendly in that it uses less energy compared to the previously used Submerged vaporizer (SMV) and Open-rack vaporizer (ORV). However, AAV is not often applied to actual processes because it is heavily affected by external environments such as atmospheric temperature and humidity. With insufficient operational experience and facility operations that rely on the intuition of the operator, the actual operation of AAV is very inefficient. To address these challenges, this paper proposes an artificial intelligence-based model that can intelligent AAV operations based on operational big data. The proposed artificial intelligence model is used deep neural networks, and the superiority of the artificial intelligence model is verified through multiple regression analysis and comparison. In this paper, the proposed model simulates based on data collected from real-world processes and compared to existing data, showing a 48.8% decrease in power usage compared to previous data. The techniques proposed in this paper can be used to improve the energy efficiency of the current natural gas generation process, and can be applied to other processes in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.9
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• pp.348-354
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• 2018

This study proposes MF (Model Filter)-based M-BDL (MF-based BIM Data Linkage), which is a model filter-based data mapping method for BIM (Building Information Modeling)-BEMS linkage. Recently, BEMS (Building Energy Management System) is actively utilizing 3D spatial information. This allows the user to intuitively manage the facility energy linked to spatial information. To use BIM data in energy management systems, it is essential to link BEMS with BIM data only in terms of the user requirements. On the other hand, if the BIM is a rich dataset and is linked as it is, the user will need to manage the unnecessary information. By mapping only the data required for BEMS in heavy BIM data through M-BDL, the BIM data can be lightened and the amount of data required for maintenance can be reduced. This technology proposes a mapping method that can link the BIM data with the filtered BIM data.

• Journal of Sensor Science and Technology
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• v.5 no.6
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• pp.43-50
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• 1996

It is very important to improve the combustion efficiency and reduce pollutant emission in order to save energy and environment. Especially, thermal NOx has been reduced through monitoring burner flame, because the thermal NOx is strongly related to flame characteristics. In this work, a flame-monitoring system was fabricated with photodiode, optical fiber, interference filter and data acquisition system, and it was applied to a lab-scale methane combustion system and a testing facility. Flame intensity and mean frequency increased with increasing turbulent intensity and fuel loading. The sensor signal from flame fluctuations differed from that without flame, which showed the availability af the flame scanner to find the presence of flame. NOx emissions increased with flame intensity.

• Nuclear Engineering and Technology
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• v.45 no.7
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• pp.895-910
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• 2013

The reflood test data from the rod bundle heat transfer (RBHT) test facility showed that the grids in the upper portion of the rod bundle could become wet well before the arrival of the quench front and that the sizes of liquid droplets downstream of a wet grid could not be predicted by the droplet breakup models for a dry grid. To investigate the water droplet generation from a wet grid spacer, a viscous linear temporal instability model of the water sheet issuing from the trailing edge of the grid with the surrounding steam up-flow is developed in this study. The Orr-Sommerfeld equations along with appropriate boundary conditions for the flow are solved using Chebyshev series expansions and the Tau-Galerkin projection method. The effects of several physical parameters on the water sheet oscillation are studied by determining the variation of the temporal growth rate with the wavenumber. It is found that a larger relative steam velocity to water velocity has a tendency to destabilize the water sheet with increased dynamic pressure. On the other hand, a larger ratio of steam boundary layer to the half water sheet thickness has a stabilizing effect on the water sheet oscillation. Droplet diameters downstream of the spacer grid predicted by the present model are found to compare reasonably well with the data obtained at the RBHT test facility as well as with other data recently reported in the literature.

• Journal of Energy Engineering
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• v.27 no.2
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• pp.26-31
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• 2018

This paper introduces a study to improve the thermal efficiency of the district heating user control facility based on reinforcement learning. As an example, it is proposed a general method of constructing a deep Q learning network(DQN) using deep Q learning, which is a reinforcement learning algorithm that does not specify a model. In addition, it is also introduced the big data platform system and the integrated heat management system which are specialized in energy field applied in processing huge amount of data processing from IoT sensor installed in many thermal energy control facilities.

• Journal of computational fluids engineering
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• v.17 no.3
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• pp.75-86
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• 2012

We developed a preliminary CFD analysis methodology to predict a pressure build up due to hydrogen flame acceleration in the APR1400 IRWST on the basis of CFD analysis results for test data of hydrogen flame acceleration in a scaled-down test facility performed by Korea Atomic Energy Research Institute. We found out that ANSYS CFX-13 with a combustion model of the so-called turbulent flame closure and a model constant of A = 5.0, a grid model with a hexahedral cell length of 5.0 mm, and a time step size of $1.0{\times}10^{-5}$ s can be a useful tool to predict the pressure build up due to the hydrogen flame acceleration in the test results. Through the comparison of the simulated results with the test results, we found out that the proposed CFD analysis methodology enables us to predict the peak pressure within an error range of about ${\pm}29%$ for the hydrogen concentration of 19.5%. However, the error ranges of the peak pressure for the hydrogen concentration of 15.4% and 18.6% were about 66% and 51%, respectively. To reduce the error ranges in case of the hydrogen concentration of 15.4% and 18.6%, some uncertainties of the test conditions should be clarified. In addition, an investigation for a possibility of flame extinction in the test results should be performed.

• Nuclear Engineering and Technology
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• v.44 no.7
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• pp.709-718
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• 2012

The pressurized water reactor APR1400 adopts DVI (Direct Vessel Injection) for the emergency cooling water in the upper downcomer annulus. The International Standard Problem number 50 (ISP-50) was launched with the aim to investigate thermal hydraulic phenomena during a 50% DVI line break scenario with best estimate codes making use of the experimental data available from the ATLAS facility located at KAERI. The present work describes the calculation results obtained for the ISP-50 using the RELAP5/MOD3.3 system code. The work aims at validation and assessment of the code to reproduce the observed phenomena and investigate about its limitations to predict complicated mixing phenomena between the subcooled emergency cooling water and the two-phase flow in the downcomer. The obtained results show that the overall trends of the main test variables are well reproduced by the calculations. In particular, the pressure in the primary system show excellent agreement with the experiment. The loop seal clearance phenomenon was observed in the calculation and it was found to have an important influence on the transient progression. Moreover, the collapsed water levels in the core are accurately reproduced in the simulations. However, the drop in the downcomer level before the activation of the DVI from safety injection tanks was underestimated due to multi-dimensional phenomena in the downcomer that are not properly captured by one-dimensional simulations.