• 한국원자력학회:학술대회논문집
• /
• 한국원자력학회 1999년도 추계학술발표회요약집
• /
• pp.247-247
• /
• 1999

This paper describes a new mechanistic thermal conductivity model considering the heterogeneous microstructure of MOX fuel. Even though the thermal conductivities of MOX have been investigated numerously by experimental measurements and theoretical analyses, they show the large scattering making the performance analysis of MOX fuel difficult. Therefore, a thermal conductivity model that depends on the heterogeneous microstructure of MOX fuel has been developed by using a general two-phase thermal conductivity model. In order to apply this model for developing the thermal conductivity for heterogeneous MOX fuel, the fuel is assumed to consist of Purich particles and U02 matrix including Pu02 in solid solution. Since little relevant data on Purich particles is available, FIGARO and SiemensKWU results are only used to characterize the microstructure of unirradiated and irradiated fuel. Philliponneaus and HALDEN models are selected for the local thermal conductivities for Purich particles and matrix, respectively. Then by combining the two models, overall thermal conductivity of MOX fuel is obtained. The new proposed model estimates the MOX thermal conductivity about 10% less than the value of U02 fuel, which is in the range of MOX thermal conductivity from HALDEN. The developed thermal conductivity model has been incorporated into KAERIs fuel performance code, COSMOS, and then verified using the measured data in the FIGARO program. Comparison of predicted and measured temperatures shows the reasonable agreement within acceptable error bounds together with satisfactory results for the fission gas release and gap pressure.essure.

• 전기학회논문지
• /
• 제59권11호
• /
• pp.2012-2015
• /
• 2010

In this paper, the mathematical models of the superheater and the desuperheater are derived based on the fundamental laws of physics, mass and energy balance. The parameters of the models are developed for the 500[MW] thermal power plant using the actual data. The simulated model outputs are well matched with the actual ones. It is expected that the proposed models are useful for the temperature controller design of the thermal power plant.

• 천문학회보
• /
• 제35권2호
• /
• pp.74.1-74.1
• /
• 2010

Using two-dimensional numerical hydrodynamic simulations, we investigate the regulation of star ormation rates in turbulent, multiphase, galactic gaseous disks. Our simulation domain is xisymmetric, and local in the radial direction and global in the vertical direction. Our models nclude galactic rotation, vertical stratification, self-gravity, heating and cooling, and thermal onduction. Turbulence in our models is driven by momentum feedback from supernova events ccurring in localized dense regions formed by thermal and gravitational instabilities. Self-onsistent radiative heating, representing enhanced/reduced FUV photons from the star formation, s also taken into account. Evolution of our model disks is highly dynamic, but reaches a quasi-teady state. The disks are overall in effective hydrostatic equilibrium with the midplane thermal ressure set by the vertical gravity. The star formation rate is found to be proportional pproximately linearly to the midplane thermal pressure. These results are in good agreement with the predictions of a recent theory by Ostriker, McKee, and Leroy (2010) for the thermal/dynamic equilibrium model of star formation regulation.

• Journal of Mechanical Science and Technology
• /
• 제17권5호
• /
• pp.668-678
• /
• 2003

For the purpose to facilitate development of high-speed spindle units (SUs) running on rolling bearings, we have developed a beam element model, algorithms, and software for computer analysis of thermal characteristics of SUs. The thermal model incorporates a model of heat generation in rolling bearings, a model of heat transfer from bearings, and models for estimation of temperature and temperature deformations of SU elements. We have carried out experimental test and made quantitative evaluation of the effect of operation conditions on friction and thermal characteristics of the SUs of grinding and turning machines of typical structures. It is found out that the operation conditions make stronger effect on SU temperatures when rpm increases. A comparison between the results of analysis and experiment proves their good mutual correspondence and allows us to recommend application of the models and software developed for design and research of high-speed SUs running on rolling bearings.

• 한국공작기계학회:학술대회논문집
• /
• 한국공작기계학회 1999년도 추계학술대회 논문집 - 한국공작기계학회
• /
• pp.345-350
• /
• 1999

The effectiveness of software error compensation for thermally induced machine tool errors relies on the prediction accuracy of the pre-established thermal error models. The selection of optimal sensor locations is the most important in establishing these empirical models. In this paper, a methodology for the selection of optimal sensor locations is proposed to establish a robust linear model which is not subjected to collinearity. Correlation coefficient and time delay are used as thermal parameters for optimal sensor location. Firstly, thermal deformation and temperatures are measured with machine tools being excited by sinusoidal heat input. And then, after correlation coefficient and time delays are calculated from the measured data, the optimal sensor location is selected through hard c-means clustering and sequential selection method. The validity of the proposed methodology is verified through the estimation of thermal expansion along Z-axis by spindle rotation.

• 문화기술의 융합
• /
• 제9권5호
• /
• pp.737-745
• /
• 2023

본 논문은 열화상 이미지를 활용하여 배전반 화재 발생을 감지하기 위한 인공지능 모델을 개발하는 연구에 대해 다룬다. 연구의 목표는 수집한 열화상 이미지를 전처리하여 객체 탐지 모델에 적합한 데이터로 가공하고, 이를 이용하여 배전반 내 화재 발생 여부를 판단하는 모델을 설계하는 것이다. 연구에서는 AI-HUB의 산업단지 내 학습용 열화상 이미지 데이터를 활용하였으며, CNN 기반 딥러닝 객체 검출 알고리즘 중 대표적인 모델인 Faster R-CNN과 RetinaNet을 사용하여 모델을 구축하고 두 개의 모델을 비교 분석하여 최적의 모델을 제안하고 있다.

• 한국공간구조학회논문집
• /
• 제23권4호
• /
• pp.81-88
• /
• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

• Geomechanics and Engineering
• /
• 제6권4호
• /
• pp.359-376
• /
• 2014

Thermal conductivity of ground has a great influence on the performance of Ground Heat Exchangers (GHEs). In general, the ground thermal conductivity significantly depends on the density (or porosity) and the moisture content since they are decisive factors that determine the interface area between soil particles which is available for heat transfer. In this study, a large number of thermal conductivity experiments were conducted for soils of varying porosity and moisture content, and a database of thermal properties for the weathered granite soils was set up. Based on the database, a 3D Curved Surface Model and an Artificial Neural Network Model (ANNM) were proposed for estimating the thermal conductivity. The new models were validated by comparing predictions by the models with new thermal conductivity data, which had not been used in developing the models. As for the 3D CSM, the normalized average values of training and test data were 1.079 and 1.061 with variations of 0.158 and 0.148, respectively. The predictions became somewhat unreliable in a low range of thermal conductivity values in considering the distribution pattern. As for the ANNM, the 'Logsig-Tansig' transfer function combination with nine neurons gave the most accurate estimates. The normalized average values of training data and test data were 1.006 and 0.954 with variations of 0.026 and 0.098, respectively. It can be concluded that the ANNM gives much better results than the 3D CSM.

• 토지주택연구
• /
• 제9권4호
• /
• pp.25-32
• /
• 2018

Application of geothermal energy in buildings has been gaining popularity as it provides the benefits of both heating and cooling a building. Among the various types of geothermal energy systems, ground-coupled heat pump system is the most commonly applied one in South Korea. A ground heat exchanger plays an important role as a heat source in winter and a heat sink in summer. For the stable operation of a ground-coupled heat pump system, a ground heat exchanger should be sized so that it provides sufficient heating and cooling energy. Heating and cooling energies generated in ground heat exchangers mainly depend on the temperature difference between the heating medium in ground heat exchangers and the surrounding ground. In addition, the performance of ground heat exchangers influences the change in ground temperature. Therefore, it is necessary to consider this interrelation between the change in the ground temperature and the performance of ground heat exchanger for an accurate estimation of its performance. However, previous thermal analysis models for ground heat exchangers are not competent enough to allow a complete understanding of this interrelation. Therefore, this study proposes a three-dimensional equivalent, transient ground heat exchanger analysis model. First, a previous thermal analysis model for ground heat exchangers, including an analytical model, a g-function, and a numerical model are analyzed. Next, to overcome the limitations of the previous models, a three-dimensional equivalent, transient ground heat exchanger model is proposed. Finally, this study validated the proposed model with the measurement data of the thermal response test, sandbox test, and TRNSYS DST model. All validation results showed a good agreement. These findings helped us to investigate the thermal performance of ground heat exchangers more accurately than the analytical models, and faster than the numerical models. Furthermore, the proposed model contributes to the design of ground heat exchangers by considering the different operation conditions of buildings.

• 물과 미래
• /
• 제23권4호
• /
• pp.487-497
• /
• 1990

원자력·화력, 제철소 등의 임해공업시설로부터 온수 방출의 확산에 대한 정성, 정량적 예측은 환경관리 및 냉각수 취·배수로 설계에 매우 중요하다. 본 연구는 난류 및 부력효과가 강한 온배수의 주요 물리적 특성을 규명하고 실무에 많이 사용되고 있는 MIT 및 PDS 적분모델의 비교평가를 실시하였다. 일반적으로 MIT 및 PDS 모델은 성층화되지 않은 수역에서 주위수 및 제트 방출각도를 고려하여 온배수 거동을 산정하는 모델이다. 해석결과는 그 구성의 상이성에 의해 매우 다름이 규명되어, 온배수의 정확한 산정을 위해서는 난류모델을 이용한 수치모델의 개발 및 이의 적용이 요구된다.