• Journal of Energy Engineering
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• v.24 no.1
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• pp.42-50
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• 2015

To evaluate the heat removal capability of a condenser tube in the PCCS of an advanced nuclear power plant, a steam condensation experiment in the presence of noncondensable gas on a vertical tube is performed. The average heat transfer coefficient is measured on a vertical tube of 40 mm in O.D. and 1.0 m in length. The experiments covers the pressures of 2-4 bar, and the mass fraction of air ranges from 0.1 up to 0.7. From the experimental results, the effects of the total pressure and the concentration of air on the condensation heat transfer coefficient are investigated. The measured data are compared with the predictions by Uchida's and Tagami's correlations, and it is revealed that these models underestimate the condensation heat transfer coefficient of the steam-air mixture.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.3
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• pp.1003-1010
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• 2013

In this study, a simulation program was developed, which predicts the performance of cold-air forced circulation type air cooled showcase. The showcase has an excellent display effect in addition to preserving the grocery. In the program, the compressor was analyzed using performance data supplied by the manufacturer and the capillary tube pressure drop was analyzed using a homogeneous model. The evaporator and condenser were analyzed by dividing the heat exchangers into small elements, where energy balance and appropriate heat transfer correlations were used. A showcase model with two 3/4 HP compressors, capillary tubes of 1.6 mm inner diameter, a fin-and-tube evaporator and condenser was tested, and the results are compared with the predicted values. It is shown that both evaporation and condensation temperatures are adequately predicted by the program.

• KIEAE Journal
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• v.17 no.5
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• pp.69-76
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• 2017

Purpose: This study aimed at developing an Artificial Neural Network (ANN) model for predicting the amount of cooling energy consumption of the variable refrigerant flow (VRF) cooling system by the different set-points of the control variables, such as supply air temperature of air handling unit (AHU), condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. Applying the predicted results for the different set-points, the control algorithm, which embedded the ANN model, will determine the most energy efficient control strategy. Method: The ANN model was developed and tested its prediction accuracy by using matrix laboratory (MATLAB) and its neural network toolbox. The field data sets were collected for the model training and performance evaluation. For completing the prediction model, three major steps were conducted - i) initial model development including input variable selection, ii) model optimization, and iii) performance evaluation. Result: Eight meaningful input variables were selected in the initial model development such as outdoor temperature, outdoor humidity, indoor temperature, cooling load of the previous cycle, supply air temperature of AHU, condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. The initial model was optimized to have 2 hidden layers with 15 hidden neurons each, 0.3 learning rate, and 0.3 momentum. The optimized model proved its prediction accuracy with stable prediction results.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.1-6
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• 2020

Road movement pollutant air environment big data is a link between real-time traffic data such as vehicle type, speed, and load using AVC, VDS, WIM, and DTG, which are always traffic volume survey equipment, and road shape (uphill, downhill, turning section) data using GIS. It consists of traffic flow data. Also, unlike general data, a lot of data per unit time is generated and has various formats. In particular, since about 7.4 million cases/hour or more of large-scale real-time data collected as detailed traffic flow information are collected, stored and processed, a system that can efficiently process data is required. Therefore, in this study, an open source-based data parallel processing performance optimization study is conducted for the visualization of big data in the air environment of road transport pollution.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.2
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• pp.121-128
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• 2021

In this paper, we propose a path generation and tracking algorithm of an unmanned air vehicle in a two-dimensional plane given the initial and final points. The path generation algorithm using the Dubins curve proposed in this work has the advantage that it can be applied in real time to an unmanned air vehicle. The path tracking algorithm is an algorithm similar to the line-of-sight induction algorithm. In order to efficiently control the direction angle, a gain related to the look ahead distance concept is introduced. Most of UAVs have the limited maximum curvature due to the structural constraints. A numerical simulation is conducted to follow the path generated by the sliding mode controller considering the angular velocity limit. The path generation and tracking performance is verified by comparing the suggested controller with conventional control techniques.

• Applied Chemistry for Engineering
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• v.33 no.1
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• pp.96-102
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• 2022

Reactants of PEMFC are hydrogen and oxygen in gas phases and fuel cell overpotential could be reduced when reactants are smoothly transported. Numerous studies to modify cathode flow field design have been conducted because oxygen mass transfer in high current density region is dominant voltage loss factor. Among those cathode flow field designs, a block in flow field is used to forced supply reactant gas to porous gas diffusion layer. In this study, the block was installed on a simple fuel cell model. Using computational fluid dynamics (CFD), effects of forced convection due to blocks on a polarization curve and local current density contour were studied when different air flow rates were supplied. The high current density could be achieved even with low air supply rate due to forced convection to a gas diffusion layer and also with multiple blocks in series compared to a single block due to an increase of forced convection effect.

• Atmosphere
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• v.31 no.5
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• pp.473-488
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• 2021

A new physical/statistical diagnostic downscale model has been developed for use to improve near-surface air temperature forecasts. The model includes a series of physical and statistical correction methods that account for un-resolved topographic and land-use effects as well as statistical bias errors in a low-resolution atmospheric model. Operational temperature forecasts of the Local Data Assimilation and Prediction System (LDAPS) were downscaled at 100 m resolution for three months, which were used to validate the model's physical and statistical correction methods and to compare its performance with the forecasts of the Korea Meteorological Administration Post-processing (KMAP) system. The validation results showed positive impacts of the un-resolved topographic and urban effects (topographic height correction, valley cold air pool effect, mountain internal boundary layer formation effect, urban land-use effect) in complex terrain areas. In addition, the statistical bias correction of the LDAPS model were efficient in reducing forecast errors of the near-surface temperatures. The new high-resolution downscale model showed better agreement against Korean 584 meteorological monitoring stations than the KMAP, supporting the importance of the new physical and statistical correction methods. The new physical/statistical diagnostic downscale model can be a useful tool in improving near-surface temperature forecasts and diagnostics over complex terrain areas.

• Journal of Advanced Navigation Technology
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• v.26 no.5
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• pp.281-288
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• 2022

This study quantitatively analyzed the human casualties that can occur when a multicopter-type Urban Air Mobility (UAM) with a weight of about 1 ton and a speed of about 100 km/h falls in an urban area. Based on the population density and building database in Seoul, the population exposed to collisions in the event of a UAM crash was derived. Through the ballistic descent model, the accident impact radius of the UAM fall was calculated. In addition, the change in human casualties on the ground was analyzed when the accident impact radius increased. Finally, the ground risk map was created for Seoul, and it was confirmed that about 1 to 10 people could be injured when a UAM crash.

• Journal of Advanced Navigation Technology
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• v.27 no.4
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• pp.356-364
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• 2023

Sophisticated Air Navigation System has contributed to enhancing the capacity of airspace capacity, leading to an efficient airspace environment. However, it has acted as a factor increasing the probability of collision. When an aircraft fails to maintain vertical separation and instead exhibits lateral positional errors, it does not necessarily lead to a collision. However, as the lateral positional accuracy increases, the randomness of aircraft positions decrease, resulting in an elevated probability of collisions. Consequently, The International Civil Aviation Organization has introduced Strategic Lateral Offset Procedures(SLOP), intentionally deviating aircraft from the centerline of airways. Likewise, South Korea also operates Offset procedure. The Y579 was operated using the Offset before its conversion to a dual airway and the analysis of the Offset track revealed that it was being operated similarly to a unidirectional dual airway. This paper develops a safety assessment methodology applicable to unidirectional dual airways, and applies it to perform a safety assessment of the Y579 Offset procedure.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.2
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• pp.47-54
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• 2024

The global decrease in traditional energy resources has prompted increasing energy demand, necessitating efforts to replace and optimize energy sources. This study focuses on enhancing energy efficiency in manufacturing plants, known for their high energy consumption. Through simulations and analyses, the study proposes a temperature-based control system for HVAC (Heating, Ventilating, and Air Conditioning) operations, utilizing machine learning algorithms to predict and optimize factory temperatures. The results indicate that this approach, particularly the prediction-based free cooling algorithm, can achieve over 10% energy savings compared to existing systems. This paper presents that implementing an efficient HVAC control system can significantly reduce overall factory energy consumption, with plans to apply it to real factories in the future.