• 대한조선학회논문집
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• 제60권4호
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• pp.222-230
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• 2023

In the present work, the scale effect on the Boil-Off Rate (BOR) was investigated based on an analytical method to systematically evaluate the thermal performance of a Liquefied Natural Gas (LNG) Cargo Containment System (CCS). A two-dimensional thermal resistance network model was developed to accurately estimate the heat ingress into the CCS from the outside. The analysis was performed for the KC-1 LNG membrane tank under the IGC and USCG design conditions. The ballast compartment of both the LNG tank and cofferdam was divided into six sections and a thermal resistance network model was made for each section. To check the validity of the developed model, the analysis results were compared with those from existing literature. It was shown that the BOR values under the IGC and USCG design conditions were agreed well with previous numerical results with a maximum error of 1.03% and 0.60%, respectively. A SDR, the scale factor of the LNG CCS was introduced and the BOR, air temperature of the ballast compartment, and the surface temperature of the inner hull were obtained to examine the influence of the SDR on the thermal performance. Finally, a correlation for the BOR was proposed, which could be expressed as a simple formula inversely proportional to the SDR. The proposed correlation could be utilized for predicting the BOR of a full-scale LNG tank based on the BOR measurement data of lab-scale model tanks.

• 대한건축학회논문집:구조계
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• 제36권3호
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• pp.169-176
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• 2020

The authors developed a nodal network model that simulates the flow of moist air and the thermal behavior of a target area. The nodal network model was enhanced using a parameter estimation technique based on the measured temperature, humidity, and schedule data. However, the nodal model is not good enough for predicting humidity of the target space, having 55.6% of CVRMSE. It is because re-evaporation effect could not be modeled due to uncertain factors in the field measurement. Hence, a data-driven model was introduced using an artificial neural network (ANN). It was found that the data-driven model is suitable for predicting the condensation compared to the nodal model satisfying ASHRAE Guideline with 3.36% of CVRMSE for temprature, relative humidity, and surface temperature on average. The model will be embedded in automated devices for real-time predictive control, to minimize the risk of surface condensation at dressroom in an apartment housing.

• 한국지반공학회논문집
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• 제27권9호
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• pp.77-86
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• 2011

지반 물질의 열전도도는 경험식이 제안하는 단위 중량, 간극률 등의 영향 인자 이외에도 하중조건에 따라 크게 좌우된다. 본 논문에서는 개별요소법에 의해 생성된 입자상 지반재료의 열 전달 특성을 열 네트워크 모델로 해석하여 하중이 열전도도에 미치는 영향을 평가하였다. 하중의 변화에 의한 개별 입자들간의 접촉수 및 간극률, 간극수의 전도도에 따른 열전도도를 산출하여 영향 요소들간의 관계를 분석하였다. 전도도의 변화 양상은 전단강성도 분석과 유사하게 열전달 방향 및 하중 크기에 따른 멱함수 형태로 회귀분석이 가능하였다. 해석 결과 하중에 따른 입자간 접촉 면적의 증가 및 간극수의 전도도가 전체 입자상 물질의 열 흐름에 큰 영향을 미침을 알 수 있었다. 열전도도의 이방성은 하중 방향에 의해 좌우되며 입자 스케일에서의 매커니즘이 열 흐름을 좌우하는 중요한 인자임을 보였다.

• 한국공작기계학회논문집
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• 제13권3호
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• pp.16-23
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• 2004

Thermally induced errors of machine tools have been recognized as one of the most important issues in precision machining. This is probably the most formidable obstacle to obtain high level of machining accuracy. To this regard, the experimental compensation methodologies such as software-based method or origin shift of machine tool axes have been suggested. In this research, to cope with thermal deformation, a model based correction was carried out with the function of an external machine coordinate shift. Models with multi-linear regression or neural network were investigated to selected a good one for thermal compensation. Consequently, multi-linear regression model combined with origin shift was verified good enough form the machining of dot matrices of plate with ball end milling.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2008년도 춘계학술대회논문집
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• pp.476-479
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• 2008

Understanding the dynamics of proteins is essential to gain insight into biological functions of proteins. The protein dynamics is delineated by conformational fluctuation (i.e. thermal vibration), and thus, thermal vibration of proteins has to be understood. In this paper, a simple mechanical model was considered for understanding protein's dynamics. Specifically, a mechanical vibration model was developed for understanding the large protein dynamics related to biological functions. The mechanical model for large proteins was constructed based on simple elastic model (i.e. Tirion's elastic model) and model reduction methods (dynamic model condensation). The large protein structure was described by minimal degrees of freedom on the basis of model reduction method that allows one to transform the refined structure into the coarse-grained structure. In this model, it is shown that a simple reduced model is able to reproduce the thermal fluctuation behavior of proteins qualitatively comparable to original molecular model. Moreover, the protein's dynamic behavior such as collective dynamics is well depicted by a simple reduced mechanical model. This sheds light on that the model reduction may provide the information about large protein dynamics, and consequently, the biological functions of large proteins.

• Composites Research
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• 제15권5호
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• pp.27-34
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• 2002

본 연구의 목적은 (주)한국와이바의 CF3327 평직 복합재료의 열전도도를 실험적으로 계측하고 이를 이론적인 예측과 비교하는데 있다. 열전도도 계측을 위하여 비교계측법의 원리를 이용한 실험 장치를 제작하였으며 열전도도가 잘 알려진 그라파이트를 실험함으로써 장비의 정확성을 확인하였다. 미시역학적인 방법은 섬유 및 기지의 물성, 섬유체적비, 직조 형태 등의 변수들이 복합재료의 유효물성치에 미치는 영향을 평가하는데 유용하다. 본 연구에서는 3차원 직-병렬 열저항 개념을 주기적으로 반복되는 평직의 단위구조에 적용하여 열전도도를 예측하였다. 해석 결과를 실험 결과와 비교한 결과 잘 일치함을 확인하였고 섬유체적비가 에폭시 수지 복합재료의 열전도도에 미치는 영향을 고찰하였다.

• 전기학회논문지
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• 제66권10호
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• pp.1445-1453
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• 2017

In this paper, we propose a fault detection model based on multi-layer neural network using data mining technique for faults due to boiler tube leakage in a thermal power plant. Major measurement data related to faults are analyzed using statistical methods. Based on the analysis results, the number of input data of the proposed fault detection model is simplified. Then, each input data is clustering with normal data and fault data by applying K-Means algorithm, which is one of the data mining techniques. fault data were trained by the neural network and tested fault detection for boiler tube leakage fault.

• Nuclear Engineering and Technology
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• 제54권3호
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• pp.834-841
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• 2022

System thermal-hydraulic (STH) code is adopted for nuclear safety analysis. The critical flow model (CFM) is significant for the accuracy of STH simulation. To overcome the defects of current CFMs (low precision or long calculation time), a CFM based on a genetic neural network (GNN) has been developed in this work. To build a powerful model, besides the critical mass flux, the critical pressure and critical quality were also considered in this model, which was seldom considered before. Comparing with the traditional homogeneous equilibrium model (HEM) and the Moody model, the GNN model can predict the critical mass flux with a higher accuracy (approximately 80% of results are within the ±20% error limit); comparing with the Leung model and the Shannak model for critical pressure prediction, the GNN model achieved the best results (more than 80% prediction results within the ±20% error limit). For the critical quality, similar precision is achieved. The GNN-based CFM in this work is meaningful for the STH code CFM development.

• 한국지반공학회논문집
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• 제27권11호
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• pp.39-45
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• 2011

본 연구는 글라스 비드와 고무 혼합재의 부피비와 상대적인 크기 비에 따른 열적 거동에 관해 다루고 있다. 혼합 물질의 열전도도를 측정하기 위하여 비정상면열원법이 사용되었다. 개별요소법과 열 네트워크 모델을 결합하여 입상체 모사 시료에서 입자 단위의 열전달 매커니즘을 분석하였다. 실험 및 해석의 결과는 다음과 같다. 유효 열전도도는 고무의 부피비가 증가할수록 감소한다. 두 물질의 상대적인 크기는 열 전파경로의 대부분을 결정하는 입자간 접촉상태의 공간적 구성을 지배한다. 같은 부피비를 갖는 혼합물질 중에서, 열이 잘 흐르지 않는 물질(여기에서는 고무)의 입자 크기가 큰 경우 열전달이 더 원활하게 이루어진다. 이상의 실험결과와 입자 단위의 관찰은 물질의 열적 거동이 부피비 뿐 아니라 구성 성분의 공간적인 구성에도 영향을 받음을 보여준다.

• Journal of Mechanical Science and Technology
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• 제18권2호
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• pp.230-239
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• 2004

A compact model approach of a network of spring elements for elastic loading is presented for the thermal deformation analysis of BGA package assembly. High-sensitivity moire interferometry is applied to evaluate and calibrated the model quantitatively. Two ball grid array (BGA) package assemblies are employed for moire experiments. For a package assembly with a small global bending, the spring model can predict the boundary conditions of the critical solder ball excellently well. For a package assembly with a large global bending, however, the relative displacements determined by spring model agree well with that by experiment after accounting for the rigid-body rotation. The shear strain results of the FEM with the input from the calibrated compact spring model agree reasonably well with the experimental data. The results imply that the combined approach of the compact spring model and the local FE analysis is an effective way to predict strains and stresses and to determine solder damage of the critical solder ball.