• Journal of Bio-Environment Control
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• v.29 no.3
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• pp.285-292
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• 2020

Because the inner environment of greenhouse has a direct impact on crop production, many studies have been performed to develop technologies for controlling the environment in the greenhouse. However, it is difficult to apply the technology developed to all greenhouses because those studies were conducted through empirical experiments in specific greenhouses. It takes a lot of time and cost to develop the models that can be applicable to all greenhouse in real situation. Therefore studies are underway to solve this problem using computer-based simulation techniques. In this study, a model was developed to predict the inner environment of glass greenhouse using CFD simulation method. The developed model was validated using primary and secondary heating experiment and daytime greenhouse inner temperature data. As a result of comparing the measured and predicted value, the mean temperature and uniformity were 2.62℃ and 2.92%p higher in the predicted value, respectively. R² was 0.9628, confirming that the measured and the predicted values showed similar tendency. In the future, the model needs to improve by applying the shape of the greenhouse and the position of the inner heat exchanger for efficient thermal energy management of the greenhouse.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.40 no.12
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• pp.801-807
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• 2016

This paper aims to compare and study the cooling performance of a battery system in accordance with the inlet and outlet geometry of the air passage in an EV. The arrangement and the heat source of the battery module were fixed, and the inlet/outlet area and its geometry were varied with the analysis of the cooling performance. The results of this study provide suggestions for the air flow stream line inside of a battery, the velocity field, and the temperature distributions. It was confirmed that the volume flow rate of air should be over $400m^3/h$, in order to satisfy conditions under $50^{\circ}C$, which is the limit condition for stable operation. It was also revealed that the diffuser outlet geometry can improve the cooling performance of battery system.

• The Journal of Bigdata
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• v.3 no.2
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• pp.59-70
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• 2018

In order to provide stable district heat supplying service to the certain limited residential area, it is the most important to forecast the short-term future demand more accurately and produce and supply heat in efficient way. However, it is very difficult to develop a universal heat demand forecasting model that can be applied to general situations because the factors affecting the heat consumption are very diverse and the consumption patterns are changed according to individual consumers and regional characteristics. In particular, considering all of the various variables that can affect heat demand does not help improve performance in terms of accuracy and versatility. Therefore, this study aims to develop a demand forecasting model using deep learning based on only limited information that can be acquired in real time. A demand forecasting model was developed by learning the artificial neural network of the Tensorflow using past data consisting only of the outdoor temperature of the area and date as input variables. The performance of the proposed model was evaluated by comparing the accuracy of demand predicted with the previous regression model. The proposed heat demand forecasting model in this research showed that it is possible to enhance the accuracy using only limited variables which can be secured in real time. For the demand forecasting in a certain region, the proposed model can be customized by adding some features which can reflect the regional characteristics.

• The Journal of Bigdata
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• v.4 no.1
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• pp.63-72
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• 2019

For power energy, optimal generation and distribution plans based on accurate demand forecasts are necessary because it is not recoverable after they have been delivered to users through power generation and transmission processes. Failure to predict power demand can cause various social and economic problems, such as a massive power outage in September 2011. In previous studies on forecasting power demand, ARIMA, neural network models, and other methods were developed. However, limitations such as the use of the national average ambient air temperature and the application of uniform criteria to distinguish seasonality are causing distortion of data or performance degradation of the predictive model. In order to improve the performance of the power demand prediction model, we divided Korea into five major regions, and the power demand prediction model of the linear regression model and the neural network model were developed, reflecting seasonal characteristics through regional characteristics and migration period learning techniques. With the proposed approach, it seems possible to forecast the future demand in short term as well as in long term. Also, it is possible to consider various events and exceptional cases during a certain period.

• Fire Science and Engineering
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• v.26 no.5
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• pp.92-98
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• 2012

This study analyzed the effect of zoning on the distribution of pressure differentials caused by stack effect and air pressurization in a center core type of 80 story super-tall building. The results showed that maximum pressure difference more than 250 Pa can be generated by stack effect without zoning. Zoning of stairwell only resulted in 10 Pa reduction of maximum pressure difference, however, zoning of both stairwell and EV shaft especially at the same floor revealed 50 % reduction in stack effect. It was also analysed that the minimum required air flow rate occurred when the stairwell temperature reached 50 % of temperature difference between indoor and outdoor.

• Food Engineering Progress
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• v.21 no.4
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• pp.312-317
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• 2017

Weight loss that influences quality and farmer incomes is affected by the storage environment of agricultural products. The interior of storage should be maintained at high humidity to prevent the weight loss of products which contain a lot of moisture. The research had constantly proceeded with change in the heat exchanger surface areas, humidity systems, and weight loss forecast to maintain high humidity within storage. Relative humidity that exerts an effect weight loss of crop is influenced by storage temperature, leak state, and volume of product. When weight loss is predicted, different conditions of these factors are derived. In case of CA storage, ways of forecasting the weight loss become easier compared to cold storage due to sealed storage with external environment during storage period. In this study, apples were stored in purge-type CA storage and weight loss has been predicted by using operating characteristics and environmental conditions. As a result, humidity variation in the storage fluctuates with the operation of the unit-cooler. Furthermore, unit-cooler operation factor is influenced by outside temperature and respiration heat. Prediction value of weight loss according to temperature and humidity has been most accurately predicted. Prediction value through defrosting water measured shows unit-cooler work quality. K-value needs verification to calculate the VPD method.

• Journal of the Korean Society of Propulsion Engineers
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• v.21 no.5
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• pp.29-36
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• 2017

In this study, a numerical analysis is made of the fluid flow and heat transfer characteristics in the solar arrays of HALE (High Altitude Lond Endurance) UAV. In the stratosphere where UAV operates, high level solar radiation is induced, heat transfer decreases due to natural convection and forced convection is dominated by ambient flow. In order to predict the solar array temperature range in this environment condition, the conjugate heat transfer analysis was carried out for the solar arrays on the main wing. The investigation focused on the temperature distribution of solar array and heat transfer characteristics according to influence of solar energy, flight condition as vehicle speed, air density, temperature.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.326-331
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• 2018

Evaporative humidification using a humidifying element is used widely for the humidification of a building or a data center. The performance of a humidifying element is commonly expressed as the humidification efficiency, which is assumed to be independent of the air temperature or humidity. To verify this assumption, a series of tests were conducted under two air conditions - data center ($25^{\circ}C$ DBT, $15^{\circ}C$ WBT) and commercial building ($35^{\circ}C$ DBT, $21^{\circ}C$ WBT) - using humidifying elements made from cellulose/PET and changing the frontal air velocity from 1.0 m/s to 4.5 m/s. Three samples having a 100 mm, 200 mm, or 300 mm depth were tested. The results showed that the humidification efficiency is dependent on the air condition. Indeed, even dehumidification occurred at the inlet of the humidifying element at the air condition of commercial building. This suggests that a proper thermal model should account for the inlet area, where the amount of moisture transfer may be different from the other part of the humidification element. As the depth of the element increased from 100 mm to 200 mm, the humidification efficiency increased by 29%. With further increases to 300 mm, it increased by 42%. On the other hand, the pressure drop also increased by 47% and 86%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.892-897
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• 2020

Previous plastic heat exchangers are expensive because the mold must be newly manufactured depending on the air conditioning space. On the other hand, danpla is so thin that the heat exchange performance is excellent. Moreover, danpla can be used easily in ventilation systems in view of fabrication. This study proposes correlations for the cooling performance of an indirect evaporative cooling system. The experimental apparatus consisted of a heat exchanger, spray nozzle, fan, thermostat, pump, and measuring sensors for temperature, humidity, and airflow rate. The results showed that the effectiveness decreased gradually as the airflow rate increased. In addition, there was an optimal condition in terms of effectiveness. The performance prediction correlations were determined using the experimental data from various conditions. The proposed correlations showed performance accuracies within 4 % error.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.2
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• pp.175-182
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• 2018

Knowledge of the optimum cultivation period for Chinese cabbage would help growers especially in spring in Korea. Growth and yield of Chinese cabbage in a temperature gradient chamber was evaluated for the growing periods of 64 days from three set of transplanting dates including March 6, March 20, and April 3 in 2017. Air temperature in the chamber was elevated step-by-step, by $2^{\circ}C$ above the ambient temperature. This increment was divided into three phases; i.e. low (ambient+$2^{\circ}C$, A), medium (ambient+$4^{\circ}C$, B), and high temperature (ambient+$6^{\circ}C$, C). The fresh weight of Chinese cabbage was greater under B and C conditions in the first period and A in the second period, which indicated that GDDs affected the fresh weight considerably. However, leaf growth (number, area, length, and width) did not differ by GDDs. Bolting appeared under A condition in the first period, which was caused by low temperature in the early growth stage. Soft rot was developed under C condition in the second period and all temperature conditions in the third period, which resulted from high temperature in the late stage. Fresh weight increased when GDDs ranged from 587 to 729. However, it decreased when GDDs > 729. The maximum expected yield (16.3 MT/10a) was attained for the growing period of 64 days from transplanting date during which GDDs reached 601. The GDDs for optimum cultivation ranged from 478-724 under which the yield was about 95% (15.5 MT/10a) of maximum fresh weight. Such an optimum condition for GDDs was validated at five main cultivation regions including Jindo, Haenam, Naju, Seosan, and Pyeongtaek in Korea. In these regions, GDDs ranged from 619-719. This suggested that the optimum GDDs for Chinese cabbage cultivation would range from 478-724, which would give the useful information to expect the cultivation periods for ensuring maximum yield.