• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.7 no.2
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• pp.184-195
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• 1995

A theoretical one-dimensional model for the charging process in stratified thermal storage tanks is established presuming that the fluid ensuing from the tank inlet creates a perfectly mixed, layer above the thermocline. Both the generic and asymptotic closed-form solutions are obtained via the Laplace transformation. The asymptotic solution describes the nature of the charging pertaining to the case of no thermal diffusion, whereas the generic solution is of practical importance to understand the role of operating parameters on the stratification. The present model is validated through comparison with available experimental data, where they agree well with each other within a reasonable limit. An interpretation of the exact solution entails two important features associated with the charging process. The first is that an in-crease in the mixing depth $h_m$ causes a relatively slow temperature rise in the perfectly mixed region, but on the other hand it results in a faster decay of the overall temperature gradient across the thermocline. Next is the predominance of the mixing depth in the presence of the prefectly mixed region. In such a case the effect of the Peclet number is marginal and there-fore the thermal characteristics are solely dependent on the mixing depth paticularly for large $h_m$. The Peclet number affects significantly only for the case without mixing. Variation of the storage efficiency in response to the change in the mass flow rate agrees favorably with the published experimental results, which confirms the utility of the present study.

• 한국해양학회지
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• v.30 no.6
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• pp.543-549
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• 1995

The inverse method has been used to estimate a surface velocity field from sequential AVHRR/SST data. In the model, equation system was composed of heat equation and horizontal divergence minimization and the velocity field contained in the advective term of the heat equation, which was linearized in grid system, was estimated. A constraint was the minimization of horizontal divergence with weighting factor and introduced to compensate the null space(Menke, 1984) of the velocity solutions for the heat equation. The experiments were carried out to set up the range of weighting factor and the matrix equation was solved by SVD(Singular Value Decomposion). In the experiment, the scales of horizontal temperature gradient and divergence of synthetic velocity field were approximated to those of real field. The neglected diffusive effect and the horizontal variation of heat flux in the heat equation were regarded as random temperature errors. According to the result of experiments, the minimum of relative error was more desirable than the minimum of misfit as the criteria of setting up the weighting factor and the error of estimated velocity field became small when the weighting factor was order of $10^{-1}$

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.4
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• pp.162-174
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• 2012

To visualize the characteristics of vertical seawater temperature data, in the ocean having 3D spatial characteristics, 2D thematic maps like horizontal seawater temperature distribution map at each depth layer and 3D volume model using 3D spatial interpolation are used. Although these methods are useful to understand oceanographic phenomena visually, there is a limit to analyze the spatial pattern of vertical temperature distribution or the relationship between vertical temperature characteristics and other oceanic factors (seawater chemistry, marine organism, climate change, etc). Therefore, this study aims to determine the spatial distribution characteristics of vertical temperature profiles in the South Sea of Jeju by quantifying the characteristics of vertical temperature profiles by using an algorithm that can extract the thermocline parameters, such as mixed layer depth, maximum temperature gradient and thermocline thickness. For this purpose spatial autocorrelation index (Moran's I) was calculated including mapping of spatial distribution for three parameters representing the vertical temperature profiles. Also, after grouping study area as four regions by using cluster analysis with three parameters, the characteristics of vertical temperature profiles were defined for each region.

• Economic and Environmental Geology
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• v.39 no.5 s.180
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• pp.525-535
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• 2006

A study on stream-groundwater exchange was performed using head and temperature data of stream water, streambed, and groundwater. Groundwater level and temperature were obtained from multi-depth monitoring wells in small-scale watershed. During the summer and winter season, time series of temperature data at streambed and groundwater were monitored for six months. In the winter time, we measured the temperature gradient between stream water and streambed. The observed data showed three typical types of temperature characteristics. First, the temperature of streambed was lower than that of stream water; second, the temperature of streambed and stream water was similar; and the last, the temperature of streambed was higher than that of stream water. The interconnections between the stream and the streambed were not homogeneously distributed due to weakly developed sediments and heterogeneous bedrock exposed as bed of the stream. The temperature data may be used in formal solutions of the inverse problems to estimate groundwater flow and hydraulic conductivity.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.25 no.2
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• pp.101-108
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• 2013

This paper is presented an experimental study of flow boiling heat transfer characteristics of ammonia, and is focused on pressure gradient and heat transfer coefficient of the refrigerant flow inside horizontal small tube with inner diameter of 3.0 mm and length of 2000 mm. The direct heating method is applied for supplying heat to the refrigerant, where the test tube is uniformly heated by electric current. The local heat transfer coefficients were obtained over a heat flux range of 20 to $80kW/m^2$, a mass flux range of 50 to $500kg/m^2s$, a saturation temperature range of 0 to $10^{\circ}C$, and quality up to 1.0. The pressure drops increase with increasing mass flux and heat flux, and with decreasing saturation temperature. The heat transfer coefficients increase with increasing mass flux and saturation temperature in middle and high quality region. And the local heat transfer coefficient increase with increasing heat flux in low quality region. The heat transfer coefficient of the experimental result was compared with six existing heat transfer coefficient correlation. A new boiling heat transfer coefficient correlation based on the superposition model for ammonia in small tubes is developed average deviation of -0.17% and mean deviation of 10.85%.

• Steel and Composite Structures
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• v.33 no.4
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• pp.509-523
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• 2019

In the present work, the buckling analysis of micro sandwich plate with an isotropic/orthotropic cores and piezoelectric/polymeric nanocomposite face sheets is studied. In this research, two cases for core of micro sandwich plate is considered that involve five isotropic Devineycell materials (H30, H45, H60, H100 and H200) and an orthotropic material also two cases for facesheets of micro sandwich plate is illustrated that include piezoelectric layers reinforced by carbon and boron-nitride nanotubes and polymeric matrix reinforced by carbon nanotubes under temperature-dependent and hydro material properties on the elastic foundations. The first order shear deformation theory (FSDT) is adopted to model micro sandwich plate and to apply size dependent effects from modified strain gradient theory. The governing equations are derived using the minimum total potential energy principle and then solved by analytical method. Also, the effects of different parameters such as size dependent, side ratio, volume fraction, various material properties for cores and facesheets and temperature and humidity changes on the dimensionless critical buckling load are investigated. It is shown from the results that the dimensionless critical buckling load for boron nitride nanotube is lower than that of for carbon nanotube. It is illustrated that the dimensionless critical buckling load for Devineycell H200 is highest and lowest for H30. Also, the obtained results for micro sandwich plate with piezoelectric facesheets reinforced by carbon nanotubes (case b) is higher than other states (cases a and c).The results of this research can be used in aircraft, automotive, shipbuilding industries and biomedicine.

• Journal of the Korean earth science society
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• v.28 no.3
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• pp.343-356
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• 2007

This paper explored the characteristics of the interannual sea surface temperature (SST) variability in the equatorial Pacific by analyzing the simulated data from a newly coupled general circulation model (CGCM). The CGCM simulates well the realistic ENSO variability as well as the mean climatologies including SST, seasonal cycle, precipitation, and subsurface structures. It is argued that the zonal gradient of SST in the equatorial Pacific is responsible for the over-energetic SST variability near the equatorial western boundary in the model. This variability could also be related to the strong westward propagation of SST anomalies which resulted from the enhanced the zonal advection feedback. The simple two-strip model supports this by sensitivity tests. Analysis of the relationship between zonal mean thermocline depth and NINO3 SST index suggested that the ENSO variability is controlled by the recharge-discharge oscillator of the model. The lead-lag regression result reveals that heat buildup process in the western equatorial Pacific associated with the increase of the barrier layer thickness (BLT) is a precedent condition for El $Ni\widetilde{n}o$ to develop.

• Horticultural Science & Technology
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• v.34 no.2
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• pp.236-241
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• 2016

The common ice plant (Mesembryanthemum crystallinum L.) has some medicinal uses and recommended plant in closed-type plant factory. The objective of this study was to estimate the cardinal temperatures for seed germination of the common ice plant using bilinear, parabolic, and beta distribution models. Seeds of the common ice plant were germinated in the dark in a growth chamber at four constant temperatures: 16, 20, 24, and $28^{\circ}C$. For this, four replicates of 100 seeds were placed on two layers of filter paper in a 9-cm petri dish and radicle emergence of 0.1 mm was scored as germination. The times to 50% germination were 4.3, 2.5, 2.0, and 1.8 days at 16, 20, 24, and $28^{\circ}C$, respectively, indicating that the germination of this warm-weather crop increased with temperature. Next, the time course of germination was modeled using a logistic function. For the selection of an accurate model, seeds were germinated in the dark at constant temperatures of 6, 12, 32, and $36^{\circ}C$. Germination started earlier and increased rapidly at temperatures above $20^{\circ}C$. The minimum, optimal, and maximum temperatures were estimated by regression of the inverse of time to 50% germination rate, as a function of the temperature gradient. The different functions estimated differing minimum, optimal and maximum temperatures, with 5.7, 27.7, and $36.5^{\circ}C$, respectively for the bilinear function, 13.4, 25.0, and $36.6^{\circ}C$, respectively, for the parabolic function and 7.8, 25.9, and $36.0^{\circ}C$, respectively, for the beta distribution function. The models estimated that the inverse of time to 50% germination rate was 0 at 6 and $36^{\circ}C$. The observed final germination rates at 12 and $32^{\circ}C$ were 62 and 97%, respectively. Our data show that a beta distribution function provides a useful model for estimating the cardinal temperatures for germination of seed from the common ice plant.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Journal of the Korean Geotechnical Society
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• v.36 no.8
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• pp.49-60
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• 2020

A fully coupled thermo-hydro-mechanical model is established to evaluate frost heave behaviour of saturated frost-susceptible soils. The method is based on mass conservation, energy conservation, and force equilibrium equations, which are fully coupled with each other. These equations consider various physical phenomena during one-dimensional soil freezing such as latent heat of phase change, thermal conductivity changes, pore water migration, and the accompanying mechanical deformation. Using the thermo-hydro-mechanical model, a sensitivity analysis study is conducted to examine the effects of the geotechnical parameters and external conditions on the amount of frost heave and frost heaving rate. According to the results of the sensitivity analysis, initial void ratio significantly affects each objective as an individual parameter, whereas soil particle thermal conductivity and temperature gradient affect frost heave behaviour to a greater degree when applied simultaneously. The factors considered in this study are the main factors affecting the frost heaving amount and rate, which may be used to determine the frostbite sensitivity of a new sample.