• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.4
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• pp.555-566
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• 2012

This paper presents a thermal analysis of an interior PM synchronous machine with concentrated winding for electric vehicle. The conventional thermal equivalent network model has been used for a long time for calculation of the temperature rises in electrical machines. In spite of being popular, this method can not be applied correctly for elements with complicated cooling structure like liquid cooled housing. To overcome this drawbacks, in this paper, a hybrid thermal model using the result of CFD analysis partly. Using this method, to improve a thermal performance of PMSM with concentrated winding, the effects of two design parameters are analysed. Finally, the accuracy of this model has been verified by experiments for the developed 21kW motor.

• Journal of Energy Engineering
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• v.17 no.3
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• pp.125-138
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• 2008

In order to diversify energy source and to utilize it effectively, it requires to construct an integrated energy management system in a wide area. This research paper explores the core technology of network construction using wide area energy and applies the technology to the field. In specific, it examines the business model by developing l) construction technology of optimum integrated system for thermal supply on wide area network related IT technology, 2) technology of unutilized energy as heat pump using exhaust gas latent heat, and 3) thermal transportation and storage technology using various sources, and by evaluating the applicability and marketability of the model in the field.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.05a
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• pp.641-646
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• 1998

In order to enhance the dynamic and interactive simulation capability of a system thermal hydraulic code for nuclear power plant, applicability of flow network models in SINDA/FLUIN $T^{™}$ has been tested by modeling feedwater system and coupling to DSNP which is one of a system thermal hydraulic simulation code for a pressurized heavy water reactor. The feedwater system is selected since it is one of the most important balance of plant systems with a potential to greatly affect the behavior of nuclear steam supply system. The flow network model of this feedwater system consists of condenser, condensate pumps, low and high pressure heaters, deaerator, feedwater pumps, and control valves. This complicated flow network is modeled and coupled to DSNP and it is tested for several normal and abnormal transient conditions such turbine load maneuvering, turbine trip, and loss of class IV power. The results show reasonable behavior of the coupled code and also gives a good dynamic and interactive simulation capabilities for the several mild transient conditions. It has been found that coupling system thermal hydraulic code with a flow network code is a proper way of upgrading simulation capability of DSNP to mature nuclear plant analyzer (NPA).

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.7
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• pp.478-485
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• 2008

The extreme steam temperature deviation experienced in the superheater of a tangentially fired boiler can seriously affect its economic and safe operation. This temperature deviation is one of the main causes of boiler tube failures. The steam temperature deviation is mainly due to the thermal load deviation in the lateral direction of the superheater. The thermal load deviation consists of several causes. One of the causes is the non-uniform heat flow distribution of burnt gas on the superheater tube system. This distribution is very difficult to measure in situ using direct experimental techniques. So, we need thermal load model to estimate the tube temperature. In this paper, we propose a thermal load distribution model by using CFD analysis and plant data. We successfully predict the tube temperature and the steam flow rate in a final superheater system from the thermal load model and one dimensional heat-flow system analysis. The proposed model and analysis method would be valuable in preventing the frequent tube failure of the final superheater tubes.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.265-273
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• 2016

A water wall system is one of the most important components of a boiler in a thermal power plant, and it is a nonlinear Multi-Input and Multi-Output (MIMO) system, with 6 inputs and 3 outputs. Three models are developed and comp for the controller design, including a linear model, a multilayer feed-forward neural network (MFNN) model and an Echo State Network (ESN) model. First, the linear model is developed by linearizing a given nonlinear model and is analyzed as a function of the operating point. Second, the MFNN and the ESN are developed by using training data from the nonlinear model. The three models are validated using Matlab with nonlinear input-output data that was not used during training.

• Geomechanics and Engineering
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• v.10 no.1
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• pp.49-57
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• 2016

The high permeability-high strength concrete belongs to the typical of porous materials. It is mainly used in underground engineering for cold area, it can act the role of heat preservation, also to be the bailing and buffer layer. In order to establish a suitable model to predict the thermal conductivity and directly applied for engineering, according to the structure characteristics, the thermal conductivity predicting model was built by resistance network model of parallel three-phase medium. For the selected geometric and physical cell model, the thermal conductivity forecast model can be set up with aggregate particle size and mixture ratio directly. Comparing with the experimental data and classic model, the prediction model could reflect the mixture ratio intuitively. When the experimental and calculating data are contrasted, the value of experiment is slightly higher than predicting, and the average relative error is about 6.6%. If the material can be used in underground engineering instead by the commonly insulation material, it can achieve the basic requirements to be the heat insulation material as well.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2297-2310
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• 2022

A condensation heat transfer model is essential to accurately predict the performance of the passive containment cooling system (PCCS) during an accident in an advanced light water reactor. However, most of existing models tend to predict condensation heat transfer very well for a specific range of thermal-hydraulic conditions. In this study, a new correlation for condensation heat transfer coefficient (HTC) is presented using machine learning technique. To secure sufficient training data, a large number of pseudo data were produced by using ten existing condensation models. Then, a neural network model was developed, consisting of a fully connected layer and a convolutional neural network (CNN) algorithm, DenseNet. Based on the hold-out cross-validation, the neural network was trained and validated against the pseudo data. Thereafter, it was evaluated using the experimental data, which were not used for training. The machine learning model predicted better results than the existing models. It was also confirmed through a parametric study that the machine learning model presents continuous and physical HTCs for various thermal-hydraulic conditions. By reflecting the effects of individual variables obtained from the parametric analysis, a new correlation was proposed. It yielded better results for almost all experimental conditions than the ten existing models.

• Tunnel and Underground Space
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• v.6 no.4
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• pp.335-341
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• 1996

This fundamental study analyzes the relationship between rock thermal properties and psychrometric properties in underground space and has a ultimate goal to develope technologies for predicting major environmental variables. The study is divided into 2 subjects (1) developement of a basic model for predicting temperature and humidity, (2) analysis of the validity of the model through application to a local underground storage space for military supplies. The basic model is built for the network of tunnel-shaped underground spaces. The model takes into account rock thermal properties and changes in moisture content in the air due to condensation/evaporation on the rock surface. Using lumped-parameter analytical method, heat flux from or to the surrounding rock is calculated and then the psychrometric properties(air quantity, pressure, temperature, humidity) are estimated through network simulation. The model can be utilized regardless of the tunnel type. The study site is a local storage space built in rock, mainly granite gneiss and quartz-porphyry. It is a U-shaped tunnel, 593.5m long and 6x6.5m wide. Relative humidity inside has to be strictly controlled under 55% to avoid erosion of a certain types of supplies stored in 6 chambers with the capacity of 300~1.000 ton. The thermal conductivity varies between 2.734 and 2.779W/m$^{\circ}C$ and the thermal diffusivity is in the range of 1.119 and $1.152{\times}10^{-6}\;m^2/s$ the specific heat between 910 and $920\;J/kg^{\circ}C$. Relative errors of the predicted values of dry/wet temperature and relative humidity are 0.8~3.0%, 0~7.5% and 0~7.0%, respectively. Apparent errors associated with the rock surface temperature seems to be partly due to the intrinsic limitations in the infrared thermometer used in this study.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.5
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• pp.502-508
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• 2012

Heliostat, as a concentrator to reflect the incident solar energy to the receiver, is the most important system in the tower-type solar thermal power plant since it determines the efficiency and ultimately the overall performance of solar thermal power plant. Thus, a good sun tracking ability as well as a good optical property of it are required. Heliostat sun tracking system uses usually an open loop control system. Thus the sun tracking error caused by heliostat's geometrical error, optical error and computational error cannot be compensated. Recently use of sun tracking error model to compensate the sun tracking error has been proposed, where the error model is obtained from the measured ones. This work is a development of heliostat sun tracking error measurement and compensation method using BCS (Beam Characterization System). We first developed an image processing system to measure the sun tracking error optically. Then the measured error is modeled in linear polynomial form and neural network form trained by the extended Kalman filter respectively. Finally error models are used to compensate the sun tracking error. We also developed the necessary image processing algorithms so that the heliostat optical properties such as maximum heat flux intensity, heat flux distribution and total reflected heat energy could be analyzed. Experimentally obtained data shows that the heliostat sun tracking accuracy could be dramatically improved using either linear polynomial type error model or neural network type error model. Neural network type error model is somewhat better in improving the sun tracking performance. Nevertheless, since the difference between two error models in compensation of sun tracking error is small, a linear error model is preferred in actual implementation due to its simplicity.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.711-719
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• 2010

Heliostat, as a concentrator reflecting the incident solar energy to the receiver located at the tower, is the most important system in the tower-type solar thermal power plant, since it determines the efficiency and performance of solar thermal plower plant. Thus, a good sun tracking ability as well as its good optical property are required. In this paper, we propose a method to compensate the heliostat sun tracking error. We first model the sun tracking error, which could be measured using BCS (Beam Characterization System), by multilayered neural network. Then the extended Kalman filter was employed to train the neural network. Finally the model is used to compensate the sun tracking errors. Simulated result shows that the method proposed in this paper improve the heliostat sun tracking performance dramatically. It also shows that the training of neural network by the extended Kalman filter provides faster convergence property, more accurate estimation and higher measurement noise rejection ability compared with the other training methods like gradient descent method.