• 설비공학논문집
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• 제24권8호
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• pp.627-635
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• 2012

This study aims at developing an artificial neural network(ANN)-based predictive and adaptive temperature control method to control the openings at internal and external skins, and heating systems used in a building with double skin envelope. Based on the predicted indoor temperature, the control logic determined opening conditions of air inlets and outlets, and the operation of the heating systems. The optimization process of the initial ANN model was conducted to determine the optimal structure and learning methods followed by the performance tests by the comparison with the actual data measured from the existing double skin envelope. The analysis proved the prediction accuracy and the adaptability of the ANN model in terms of Root Mean Square and Mean Square Errors. The analysis results implied that the proposed ANN-based temperature control logic had potentials to be applied for the temperature control in the double skin envelope buildings.

• 시스템엔지니어링학술지
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• 제19권2호
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• pp.18-31
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• 2023

Exploring artificial intelligence and machine learning for nuclear safety has witnessed increased interest in recent years. To contribute to this area of research, a machine learning model capable of accurately predicting nuclear power plant response with minimal computational cost is proposed. To develop a robust machine learning model, the Best Estimate Plus Uncertainty (BEPU) approach was used to generate a database to train three models and select the best of the three. The BEPU analysis was performed by coupling Dakota platform with the best estimate thermal hydraulics code RELAP/SCDAPSIM/MOD 3.4. The Code Scaling Applicability and Uncertainty approach was adopted, along with Wilks' theorem to obtain a statistically representative sample that satisfies the USNRC 95/95 rule with 95% probability and 95% confidence level. The generated database was used to train three models based on Recurrent Neural Networks; specifically, Long Short-Term Memory, Gated Recurrent Unit, and a hybrid model with Long Short-Term Memory coupled to Convolutional Neural Network. In this paper, the System Engineering approach was utilized to identify requirements, stakeholders, and functional and physical architecture to develop this project and ensure success in verification and validation activities necessary to ensure the efficient development of ML meta-models capable of predicting of the nuclear power plant response.

• 한국수소및신에너지학회논문집
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• 제26권3호
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• pp.294-299
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• 2015

Free piston linear engine (FPLE) is a promising concept being explored in the mid-20th century. On the other hand, Arficial neural networks (ANNs) are non-linear computer algorithms and can model the behavior of complicated non-linear processes. Some researchers already studied this method to predict internal combustion engine characteristics. However, no investigation to predict the performance of a FPLE using ANN approach appears to have been published in the literature to date. In this study, the ability of an artificial neural network model, using a back propagation learning algorithm has been used to predict the in-cylinder pressure, frequency, maximum stroke length of a free piston linear engine. It is advised that, well-trained neural network models can provide fast and consistent results, making it an easy-to-use tool in preliminary studies for such thermal engineering problems.

• 한국태양에너지학회 논문집
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• 제22권4호
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• pp.68-76
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• 2002

This paper discusses thermal and ventilation performance which might be caused by the adoption of one of specific building facade techniques, Double Skin System(DSS). One building with a prototypical DSS was selected and systematically investigated through field monitoring and computer simulation techniques. A network model of ventilation was successfully made using COMIS to evaluate ventilation performance of the system which can hardly be done by field measurements. Various operating conditions of air conditioning on/off and window opening were implemented in this type of building. Through the appropriate operation of the DSS in summer, simulation-based and experimental results implicate that it can lead to cooling energy savings.

• Bulletin of the Korean Chemical Society
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• 제21권6호
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• pp.557-561
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• 2000

Various enaryloxynitriles-terminated reactive polymer precursors containing rigid aromatic units were prepared from various diamines and 1-(p-formylphenyl)-1-phenyl-2,2-dicyanoethene (1). Arylate end-capped model compounds linked with azomethine bond were also prepared by reacting p-formylphenyl benzoate with diamines to compare the curing ability. The oligomers were highly soluble in polar aprotic solvents such as N,N-dimethylformamide, dimethylsulfoxide and N-methyl-2 -pyrrolidinone. They generally showed an exothermic curing process between $280-350^{\circ}C$, attributable to the thermal crosslinking of the dicyanovinyl group in DSC analysis, and no weight loss at curing temperature. Upon heating the polymer precursors, heat-resistant and insoluble network polymers were obtained. Thermogravimetric analyses of the precursors containing rigid aromatic units showed thermal stability with a 77-92% residual weight at $500^{\circ}C$ under nitrogen.

• 한국산학기술학회논문지
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• 제20권12호
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• pp.663-670
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• 2019

터빈 사이클 보정 열 성능 분석은 발전소의 현재 성능을 결정하고 향상된 경제성 운전을 위해 요구된다. 본 연구에서는 신뢰성있는 성능 분석을 위해서 산업 표준인 ASME(American Society of Mechanical Engineers) PTC(Performance Test Code)를 기본으로 성능 분석에서 우선적으로 중요하게 적용되는 주급수 유량을 대상으로 영역별 판정 알고리즘을 개발하고 각 영역별로 현재의 터빈 사이클 성능을 추정하는 알고리즘을 개발하였다. 추정 알고리즘은 측정 상태량의 상관 관계를 기반으로 영역별로 형상 분류를 제시하고, 이를 기반으로 커널 회귀 모델을 이용하여 학습된 추정 모델을 구성하였으며, 커널 회귀 모델링의 우수성을 검증하기 위하여 신경 회로망 모델의 학습 결과와 비교하였다. 주급수 유량의 형상 특성에 따른 분류 및 추정 모델은 터빈 사이클에서 정확한 보정 열 성능 분석을 제공함으로써 성능 분석의 신뢰도를 증가시킬 수 있었으며 다른 성능 결정 변수에 대한 학습 및 검증 모델로 사용될 수 있다.

• 설비공학논문집
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• 제28권5호
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• pp.178-185
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• 2016

This study describes the analysis of a 20 MW chilled water plant used for the IT cooling of a recently constructed data center in Korea. The CFD model was developed with the aim of evaluating the impact of problems such as chiller failure on the water and air temperatures in the cooling system. The numerical model includes the chilled water hydraulic network and individual water-to-air CRAC units. The coupling between the IT server room air temperature levels and the cooling plant has enabled a full assessment of the cooling system design in response to system fault conditions to be performed. The paper examines an emergency situation involving the failure of the cooling plant, and shows how the inherent thermal inertia of the system along with additional inertia achieved through buffer systems allowed a suitable design to be achieved.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2010년도 추계학술대회 초록집
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• pp.123.1-123.1
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• 2010

수평형 지중열교환기의 최적설계를 위해서는 되메움재의 광물특성 및 입자크기, 열전도도(thermal conductivity), 열용량(heat capacity)등과 같은 열적 특성을 파악 하는 것은 중요하다. 수평형 지중 열교환기용 되메움재의 열전도도를 파악하기 위해 비정상 열선법을 적용한 QTM-500을 사용하여 포화도에 따른 천연규사-물-공기 혼합물의 열전도도를 측정하였다. 측정된 열전도도를 개별요소법(Discrete Element Mothod)에 근거한 2차원 수치해석 프로그램인 PFC2D(Particle Flow Code in 2 Dimension)를 이용하여 비교 분석하였다. 수치해석에서는 혼합물의 건조밀도를 일정하게 유지한 상태에서 포화도에 따라 가상의 물 입자 개수를 변화시켰다. 개별요소법을 이용한 열전달 수치해석에서는 입자의 접촉을 통해 발생한 thermal pipe에 의해 열전달이 이루어진다. 이러한 thermal pipe의 열전도도는 접촉된 두 입자의 열전도도와 접촉면의 평균 열전도도를 고려하여 적용하였다.

• 한국정밀공학회지
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• 제31권4호
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• pp.297-301
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• 2014

The biggest factors, which lower the machining accuracy of machine, are thermal deformation and chatter vibration. In this article, we introduce the development case of a device and technology that can automatically compensate thermal deformation errors of machine during long-time processing on the machine tool's CNC (Computerized Numerical Controller) in real time. In machine processing, the data acquisition of temperature signal in real time and auto-compensation of the machine origin of machine tools depending on thermal deformation have significant influence on improving the machining accuracy and the rate of operation. Thus, we attempts to introduce the related contents of the development we have made in this article : The development of a device that embedded the acquisition part of temperature data, linear regression to get compensation value, compensation model of neural network and a system that compensates the machine origin of machine tool automatically during manufacturing process on the CNC.

• International Journal of Air-Conditioning and Refrigeration
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• 제11권4호
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• pp.159-169
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• 2003

Five two-phase closed loop thermosyphons (TLTs) specially designed and constructed for the present study are one small scale loop, two medium scale loops (MSLI and MSLII) and two large scale loops (LSLI and LSLII). Two simulation models based on thermal resistance network, lumped and sectorial, are presented. In the Lumped model, the evaporator section is dealt as one lumped boiling section. Whereas, in the Sectorial model, all possible phenomena which would occur in the evaporator section due to the two-phase boiling process are considered in detail. Flow regimes, the flow transitions between flow regimes and other two-phase parameters involved in two-phase flows are carefully analyzed. In the present study, the results of two different simulation models are compared with experimental results. The comparisons showed that the simulation results by the Lumped model and by the Sectorial model did not show any partiality for the model used for the simulation. The simulation results according to the correlations show the various results in the large different range.