• International Journal of Precision Engineering and Manufacturing
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• v.4 no.1
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• pp.56-62
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• 2003

The objective of this study is to adjust the roll gap for the dimension accuracy of bar in hot bar rolling process considering roll wear. In this study hot bar rolling processes for round and oval passes have been investigated. In order to predict the roll wear, the wear model is reformulated as an incremental form and then wear depth of roll is calculated at each deformation step on contact area using the results of finite element analysis, such as relative sliding velocity and normal pressure at contact area. Archard's wear model was applied to predict the roll wear. To know the effects of thermal softening of DCI (Ductile Cast Iron) roll material according to operating conditions, high temperature micro hardness test is executed and a new wear model has been proposed by considering the thermal softening of DCI roll expressed in terms of the main tempering curve. The new technique developed in this study for adjusting roll gap can give more systematically and economically feasible means to improve the dimension accuracy of bar with full usefulness and generality.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.1036-1041
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• 1997

The objective of this study is to adjust the roll gap for the dimension accuracy of bar in hot bar rolling process considering roll wear. In this study hot bar rolling processes for round and oval passes have been investigated. In order to predict the roll wear, the wear model is reformulated as an incremental form and then wear depth of roll is calculated at each deformation step on contact area using the results of finite element analysis, such as relative sliding velocity and normal pressure at contact area. Archard's wear model was applied to predict the roll wear. To know the effects of thermal softening of DCI (Ductile Cast Iron) roll material according to operating conditions, high temperature micro hardness test is executed and a new wear model has been proposed by considering the thermal softening of DCI roll expressed in terms of the main tempering curve. The new technique developed in this study for adjusting roll gap can give more systematically and economically feasible means to improve the dimension accuracy of bar with full usefulness and generality.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.67-77
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• 2020

Traditionally nuclear thermal hydraulic and nuclear safety has relied on numerical simulations to predict the system response of a nuclear power plant either under normal operation or accident condition. However, this approach may sometimes be rather time consuming particularly for design and optimization problems. To expedite the decision-making process data-driven models can be used to deduce the statistical relationships between inputs and outputs rather than solving physics-based models. Compared to the traditional approach, data driven models can provide a fast and cost-effective framework to predict the behavior of highly complex and non-linear systems where otherwise great computational efforts would be required. The objective of this work is to develop an AI algorithm to predict the peak fuel cladding temperature as a metric for the successful implementation of FLEX strategies under extended station black out. To achieve this, the model requires to be conditioned using pre-existing database created using the thermal-hydraulic analysis code, MARS-KS. In the development stage, the model hyper-parameters are tuned and optimized using the talos tool.

• Advanced Composite Materials
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• v.18 no.3
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• pp.233-249
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• 2009

Safe installation and operation of high-pressure composite cylinders for hydrogen storage are of primary concern. It is unavoidable for the cylinders to experience temperature variation and significant thermal input during service. The maximum failure pressure that the cylinder can sustain is affected due to the dependence of composite material properties on temperature and complexity of cylinder design. Most of the analysis reported for high-pressure composite cylinders is based on simplifying assumptions and does not account for complexities like thermo-mechanical behavior and temperature dependent material properties. In the present work, a comprehensive finite element simulation tool for the design of hydrogen storage cylinder system is developed. The structural response of the cylinder is analyzed using laminated shell theory accounting for transverse shear deformation and geometric nonlinearity. A composite failure model is used to evaluate the failure pressure under various thermo-mechanical loadings. A back-propagation neural network (NNk) model is developed to predict the maximum failure pressure using the analysis results. The failure pressures predicted from NNk model are compared with those from test cases. The developed NNk model is capable of predicting the failure pressure for any given loading condition.

• KIEAE Journal
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• v.17 no.4
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• pp.83-88
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• 2017

Purpose: The objective of this study is to develop a predictive model for calculating the amount of cooling load for the different setback temperatures during the setback period. An artificial neural network (ANN) is applied as a predictive model. The predictive model is designed to be employed in the control algorithm, in which the amount of cooling load for the different setback temperature is compared and works as a determinant for finding the most energy-efficient optimal setback temperature. Method: Three major steps were conducted for proposing the ANN-based predictive model - i) initial model development, ii) model optimization, and iii) performance evaluation. Result:The proposed model proved its prediction accuracy with the lower coefficient of variation of the root mean square errors (CVRMSEs) of the simulated results (Mi) and the predicted results (Si) under generally accepted levels. In conclusion, the ANN model presented its applicability to the thermal control algorithm for setting up the most energy-efficient setback temperature.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.21 no.3
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• pp.457-472
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• 1997

We studied the temperature distribution and heat transfer characteristics of TEFC induction motor with thermal network program for more efficient design and better cooling performance of it. We knew the characteristics and the windage loss of outer cooling fan from fan test experiments. Frame axial and peripheral heat transfer coefficients and endwinding heat transfer coefficient were measured by various model experiments and then, compared with other experimental results. Frame was the main heat transfer surface, load-side and fan-side surface were not thermally symmetric from the heat flux distribution analysis. Steady and unsteady temperature distributions were measured by real motor experiments. From the results, we knew that rotor surface temperature was higher than coil temperature and the hottest spot in the coil was loadside endwinding outside surface. We compared the simulation results with those of real motor test and the two results showed a good agreement.

• Journal of the Korean earth science society
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• v.30 no.3
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• pp.294-304
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• 2009

A statistical prediction model for the typhoon intensity and track in the Northwestern Pacific area was developed based on the artificial neural network scheme. Specifically, this model is focused on the 5-day prediction after tropical cyclone genesis, and used the CLIPPER parameters (genesis location, intensity, and date), dynamic parameters (vertical wind shear between 200 and 850hPa, upper-level divergence, and lower-level relative vorticity), and thermal parameters (upper-level equivalent potential temperature, ENSO, 200-hPa air temperature, mid-level relative humidity). Based on the characteristics of predictors, a total of seven artificial neural network models were developed. The best one was the case that combined the CLIPPER parameters and thermal parameters. This case showed higher predictability during the summer season than the winter season, and the forecast error also depended on the location: The intensity error rate increases when the genesis location moves to Southeastern area and the track error increases when it moves to Northwestern area. Comparing the predictability with the multiple linear regression model, the artificial neural network model showed better performance.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.10 no.2
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• pp.227-237
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• 1998

A numerical simulation has been carried out for the automotive air conditioning system. The purpose of this simulation is to present the methods for simulating car air conditioning components, systems and cool-down performance by computerized mathematical model and to analyze the performance of A/C system. In analyzing the heat exchanger(evaporator and condenser), the finite volume model which has a merit in predicting the temperature field in detail because it can consider partial variation of thermal property and heat transfer coefficient is used. In analyzing the compressor, the polytropic approach which regards the actual compression process as a reversible polytropic process is employed. In analyzing vehicle passenger compartment, the thermal network is employed to simulate the car cool down process. This A/C system program can be used for analyzing a component performance when a component is alternated or designed and for analyzing the engine cooling system when A/C system is operated.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.257-262
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• 1997

A compact measurement system is developed to measure in-process errors due to thermal deformation of a machine tool, this system is composed of a gauge, 5gap-sensors and a PC. The gauge is made of invar, which is a material that has a very low thermal expansion coefficient. A new neural network model is constructed to estimate thermally induced machine tool errors based on the measured data,the given data,that is a width of cut,a depth of cut, a feed rate, a spindel speed etc, and the calculated data. The detail of the model proposed is described in the paper together with the experimental methodologies using a proposed compact measurement system to examine the validity of the proposed approach.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.24 no.8
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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.