• 한국재료학회지
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• 제24권7호
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• pp.381-387
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• 2014

The ${\beta}$-transus temperature in titanium alloys plays an important role in the design of thermo-mechanical treatments. It primarily depends on the chemical composition of the alloy and the relationship between them is non-linear and complex. Considering these relationships is difficult using mathematical equations. A feed-forward neural-network model with a back-propagation algorithm was developed to simulate the relationship between the ${\beta}$-transus temperature of titanium alloys, and the alloying elements. The input parameters to the model consisted of the nine alloying elements (i.e., Al, Cr, Fe, Mo, Sn, Si, V, Zr, and O), whereas the model output is the ${\beta}$-transus temperature. The model developed was then used to predict the ${\beta}$-transus temperature for different elemental combinations. Sensitivity analysis was performed on a trained neural-network model to study the effect of alloying elements on the ${\beta}$-transus temperature, keeping other elements constant. Very good performance of the model was achieved with previously unseen experimental data. Some explanation of the predicted results from the metallurgical point of view is given. The graphical-user-interface developed for the model should be very useful to researchers and in industry for designing the thermo-mechanical treatment of titanium alloys.

• 산업경영시스템학회지
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• 제42권1호
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• pp.129-136
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• 2019

In this study, we proposed a model for forecasting power energy demand by investigating how outside temperature at a given time affected power consumption and. To this end, we analyzed the time series of power consumption in terms of the power spectrum and found the periodicities of one day and one week. With these periodicities, we investigated two time series of temperature and power consumption, and found, for a given hour, an approximate linear relation between temperature and power consumption. We adopted an exponential smoothing model to examine the effect of the linearity in forecasting the power demand. In particular, we adjusted the exponential smoothing model by using the variation of power consumption due to temperature change. In this way, the proposed model became a mixture of a time series model and a regression model. We demonstrated that the adjusted model outperformed the exponential smoothing model alone in terms of the mean relative percentage error and the root mean square error in the range of 3%~8% and 4kWh~27kWh, respectively. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric energy together with the outside temperature.

• 제어로봇시스템학회논문지
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• 제15권10호
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• pp.1009-1013
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• 2009

In this paper, a robust fault detection method for varying temperature based on fuzzy model is proposed. To develop a robust force estimation model, it needs temperature information because the output of force sensor is affected by a temperature variation. The nonlinear dynamic system, such as the parking force of the EPB (Electronic Parking Brake) system is necessary to have a higher order equation model. But, because of the calculation time, the higher order equation model is hard to be used in real application. In case of the lower order equation model, the result is not as accurate as acceptable. To solve this problem, the robust fuzzy model-based fault detection is developed. A proposed fault detection method for varying temperature is verified by HILS (hardware in the loop simulation).

• 한국대기환경학회지
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• 제33권1호
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• pp.45-53
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• 2017

A high resolution model is proposed for calculating the temperature field of a large city, based upon a Lagrangian particle model. Utilizing the analogy between the heat and mass transport phenomena in turbulent flows, a Lagrangian particle model, originally developed for air pollutant dispersion problems, is adapted for simulating heat transport. In the model conceptual heat particles are released into the atmosphere from the heat sources and move along with the turbulent winds in accordance with the Markov process. The potential temperature assumed to be conserved along with heat particles serves as a tag, so the temperature fields can be deduced from the distribution of particles. The wind fields are constructed from a diagnostic meteorology model incorporating a morphological model designed for building flows. Test run shows the robustness of the modeling system.

• Journal of Mechanical Science and Technology
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• 제15권11호
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• pp.1482-1489
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• 2001

Thermally-induced errors originating from machine tool errors have received significant attention recently because high speed and precise machining is now the principal trend in manufacturing proce sses using CNC machine tools. Since the thermal error model is generally a function of temperature, the thermal error compensation system contains temperature sensors with the same number of temperature variables. The minimization of the number of variables in the thermal error model can affect the economical efficiency and the possibility of unexpected sensor fault in a error compensation system. This paper presents a thermal error model with minimum number of variables using a fuzzy logic strategy. The proposed method using a fuzzy logic strategy does not require any information about the characteristics of the plant contrary to numerical analysis techniques, but the developed thermal error model guarantees good prediction performance. The proposed modeling method can also be applied to any type of CNC machine tool if a combination of the possible input variables is determined because the error model parameters are only calculated mathematically-based on the number of temperature variables.

• 환경영향평가
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• 제20권4호
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• pp.435-442
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• 2011

In this study, a regression model was developed for prediction of inflow temperature to support an effective thermal stratification simulation of Yongdam Reservoir, using the relationship between gaged inflow temperature and air temperature. The effect of reproductability for thermal stratification was evaluated using EFDC model by gaged vertical profile data of water temperature(from June to December in 2005) and ex-developed regression models. Therefore, in the development process, the coefficient of correlation and determination are 0.96 and 0.922, respectively. Moreover, the developed model showed good performance in reproducing the reservoir thermal stratification. Results of this research can be a role to provide a base for building of prediction model for water quality management in near future.

• 한국전기전자재료학회논문지
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• 제14권4호
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• pp.297-301
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• 2001

In this paper, the temperature distribution and thermal stress analysis of 50kVA pole cast resin transformer for power distribution are investigated by FEM program. The one body molding model (Model 1) and air duct model (Model 2) are designed and their temperature distribution are analysed. The temperature rise value is about 105.5 deg in the model 1 and 65.28 de in the model 2. The temperature change of secondary winding is more than primary winding according to load ratio. The concentration part of Von Mises Stress occurs at interface between glass fiber and epoxy.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 C
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• pp.1611-1613
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• 2000

In this study in order to find out the trends and the residual lifetime for electric connections/contacts using infrared image camera, "lifetime temperature rise model" is theoretically proposed on the base of "lifetime resistance model" and to prove this theory, experiments have been performed for various kinds of electric connections/contacts. Two suggestions have been builded up or the "lifetime temperature rise model" ; one is the linear relationship between the temperature rise $\Delta K$ and contact resistance, and the other is the functional relationship between the temperature of electric connections/contacts and the operating time which ascribed in the "lifetime resistance model". From the experimental results, measured values were quite similar to the theoretical value so that two suggestions in "lifetime temperature rise model" were appeared to be correct.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(2)
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• pp.5-8
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• 2001

This paper describes the development of SiC MOSFET model for high temperature applications. The temperature dependence of the threshold voltage and mobility of SiC MOSFET is quite different from that of silicon MOSFET. We developed the empirical temperature model of threshold voltage and mobility of SiC MOSFET and implemented into HSPICE. Using this model the MOSFET Id-Vds characteristics as a function of temperature are simillated. Also the SiC CMOS operational amplifieris designed using this model and the temperature dependence of the frequency response, transfer characteristics and slew rate as a function of temperature are analyzed.

• 국제강구조저널
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• 제18권5호
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• pp.1754-1760
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• 2018

The present article showcases a temperature dependent cohesive zone model (CZM)-based fi nite element simulation of drop weight tear test (DWTT), to analyse fracture behavior of pipeline steel (PS) at different temperatures. By co-relating the key CZM parameters with known mechanical properties of PS at varying temperature, a temperature dependent CZM for PS is proposed. A modified form of Johnson and Cook model has been used for the true stress-strain behavior of PS. The numerical model, using Abaqus/CAE 6.13, has been validated by comparing the predicted results with load-displacement curves obtained from test data. During steady-state crack propagation, toughness parameters (such as CTOA and CTOD) were found to remain fairly constant at a given temperature. These toughness parameters, however, show an exponential increase with increase in temperature. The present paper offers a plausible approach to numerically analyze fracture behavior of PS at varying temperature using a temperature dependent CZM.