• Journal of the Society of Naval Architects of Korea
• /
• v.60 no.4
• /
• pp.222-230
• /
• 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.

• Journal of the Architectural Institute of Korea Structure & Construction
• /
• v.36 no.3
• /
• pp.169-176
• /
• 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.

• Journal of the Korean Geotechnical Society
• /
• v.27 no.9
• /
• pp.77-86
• /
• 2011

The stress condition mainly dominates the thermal conductivity of soils whereas governing factors such as unit weight and porosity suggested by empirical correlations are still valid. The 3D thermal network model enables evaluation of the stress-dependent thermal conductivity of particulate materials generated by discrete element method (DEM). The relationship among dominant factors is analyzed based on the coordination number and porosity determined by stress condition and thermal conductivity of pore fluid. Results show that the variation of thermal conductivity is strongly attributed to the enlargement of inter-particle contact area by loading history and pore fluid conductivity. This study highlights that the anisotropic evolution of thermal conductivity depends on the directional load and that the particle-scale mechanism mainly dictates the heat transfer in soils.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.13 no.3
• /
• pp.16-23
• /
• 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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2008.04a
• /
• pp.476-479
• /
• 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
• /
• v.15 no.5
• /
• pp.27-34
• /
• 2002

The purpose of this study is to measure and predict the thermal conductivity of CF3327 plain-weave fabric composite made by Hankuk Fiber, Co. An experiment apparatus based on the comparative method has been made to measure the thermal conductivities of the composite material. Its accuracy was proved by measuring the thermal conductivity of graphite which is well-known. Micro-mechanical approaches are useful to assess the effect of parameters such as fiber and matrix material properties, fiber volume fraction and fabric geometric parameters on the effective material properties of composites. In this study, prediction was based on the concept of three dimensional series-parallel thermal resistance network. Thermal resistance network was applied to unit ceil model that characterized the periodically repeated pattern of a plain weave. The numerical results were compared with experimental one and good agreement was observed. Also, the effects of fiber volume fraction on the thermal conductivity of several composites has been investigated.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.10
• /
• pp.1445-1453
• /
• 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
• /
• v.54 no.3
• /
• pp.834-841
• /
• 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.

• Journal of the Korean Geotechnical Society
• /
• v.27 no.11
• /
• pp.39-45
• /
• 2011

This study presents the thermal behaviors of glass beads-rubber mixtures depending on the volumetric fraction of each constituent and relative size between them. The transient plane source method is used to measure the effective thermal conductivity of mixtures. The discrete element method (DEM) and the thermal network model are integrated to investigate the particle-scale mechanism of heat transfer in granular packings. Results show that 1) the effective thermal conductivity decreases as the rubber fraction increases, and 2) the relative size between two solid particles dominates the spatial configuration of inter-particle contact condition that in tum determines the majority of heat propagation path through particle contacts. For the mixtures whose volumetric fraction of rubber is identical, the less conductive materials (e.g., rubber particles) with a large size facilitate heat transfer in granular materials. The experimental results and particle-scale observation highlight that the thermal conduction behavior is dominated not only by the volumetric fraction but also the spatial configuration of each constituent.

• Korea-Australia Rheology Journal
• /
• v.14 no.2
• /
• pp.49-55
• /
• 2002

To understand the large amplitude oscillatory shear (LAOS) behavior of complex fluids, we have investigated the flow behavior of a network model in the LAOS environment. We applied the LAOS flow to the model proposed by Vaccaro and Marrucci (2000), which was originally developed to describe the system of associating telechelic polymers. The model was found to predict at least three different types of LAOS behavior; strain thinning (G' and G" decreasing), strong strain overshoot (G' and G" increasing followed by decreasing), and weak strain overshoot (G' decreasing, G" increasing followed by decreasing). The overshoot behavior in the strain sweep test, which il often observed in some complex fluid systems with little explanation, could be explained in terms of the model parameters, or in terms of the overall balance between the creation and loss rates of the network junctions, which are continually created and destroyed due to thermal and flow energy. This model does not predict strain hardening behavior because of the finitely extensible nonlinear elastic (FENE) type nonlinear effect of loss rate. However, the model predicts the LAOS behavior of most of the complex fluids observed in the experiments.he experiments.