• Journal of Biosystems Engineering
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• v.27 no.1
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• pp.19-24
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• 2002

According to the field test, the transient power transmission efficiency of a tractor driveline fluctuated in a range of 56 to 86% and the mean value was about 72.5%. Therefore, the constant efficiency model commonly used for a simulation of power performance was not proper far predicting such a variable of efficiency. In order to predict power efficiency more accurately, new concepts of the maximum efficiency and drag torque were introduced and a new model based on the these concepts was proposed. The difference between measured and model-predicted efficiencies was about 1.5% in average with a standard deviation of 1.1%. The new power efficiency model was expected to enhance the accuracy of predicting power transmission efficiencies of tractor drivelines.

• Nuclear Engineering and Technology
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• v.30 no.6
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• pp.496-506
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• 1998

This paper deals with the computational modeling of buoyancy-driven turbulent heat transfer involving spatially uniform volumetric heat sources in semicircular geometry. The Launder & Sharma low-Reynolds number k-$\varepsilon$ turbulence model without any modifications and the SIMPLER computational algorithm were used for the numerical modeling, which was incorporated into the new computer code CORE-TNC. This computer code was subsequently benchmarked with the Mini-ACOPO experimental data in the modified Rayleigh number range of 2$\times$10$^{13}$ $\times$10$^{14}$ . The general trends of the velocity and temperature fields were well predicted by the model used, and the calculated isotherm patterns were found to be very similiar to those observed in previous experimental investigations. The deviation between the Mini-ACOPO experimental data and the corresponding numerical results obtained with CORE-TNC for the average Nusselt number was less than 30% using fine grid in the near-wall region and the three-point difference formula for the wall temperature gradient. With isothermal pool boundaries, heat was convected predominantly to the upper and adjacent lateral surfaces, and the bottom surface received smaller heat fluxes.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2610-2615
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• 2021

The prediction of irradiation-induced transition temperature shift for RPV steels is an important method for long term operation of nuclear power plant. Based on the irradiation embrittlement data, an irradiation-induced transition temperature shift prediction model is developed with machine learning method XGBoost. Then the residual, standard deviation and predicted value vs. measured value analysis are conducted to analyze the accuracy of this model. At last, Cu content threshold and saturation values analysis, temperature dependence, Ni/Cu dependence and flux effect are given to verify the reliability. Those results show that the prediction model developed with XGBoost has high accuracy for predicting the irradiation embrittlement trend of RPV steel. The prediction results are consistent with the current understanding of RPV embrittlement mechanism.

• Computers and Concrete
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• v.30 no.1
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• pp.33-42
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• 2022

Experimentally predicting the compressive strength (CS) of concrete (for a mix design) is a time-consuming and laborious process. The present study aims to propose surrogate models based on Support Vector Machine (SVM) and Gaussian Process Regression (GPR) machine learning techniques, which can predict the CS of concrete containing nano-silica. Content of cement, aggregates, nano-silica and its fineness, water-binder ratio, and the days at which strength has to be predicted are the input variables. The efficiency of the models is compared in terms of Correlation Coefficient (CC), Root Mean Square Error (RMSE), Variance Account For (VAF), Nash-Sutcliffe Efficiency (NSE), and RMSE to observation's standard deviation ratio (RSR). It has been observed that the SVM outperforms GPR in predicting the CS of the concrete containing nano-silica.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.550-551
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• 2021

Positioning technology is performing important functions in augmented reality, smart factory, and autonomous driving. Among the positioning techniques, the positioning method using beacons has been considered a challenging task due to the deviation of the RSSI value. In this study, the position of a moving object is predicted by training a neural network that takes the RSSI value of the receiver as an input and the distance as the target value. To do this, the measured distance versus RSSI was collected.

• Economic and Environmental Geology
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• v.48 no.6
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• pp.451-465
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• 2015

The free-air anomalies are computed using a data set from various types of gravity measurements in the Korean Peninsula area. The gravity values extracted from the Earth Gravitational Model 2008 are used in the surrounding region. The upward continuation technique suggested by Dragomir is used in the computation of the external free-air anomalies at various altitudes. The integration radius 10 times the altitude is used in order to keep the accuracy of results and computational resources. The direct geodesic formula developed by Bowring is employed in integration. At the 1-km altitude, the free-air anomalies vary from -41.315 to 189.327 mgal with the standard deviation of 22.612 mgal. At the 3-km altitude, they vary from -36.478 to 156.209 mgal with the standard deviation of 20.641 mgal. At the 1,000-km altitude, they vary from 3.170 to 5.864 mgal with the standard deviation of 0.670 mgal. The predicted free-air anomalies at 3-km altitude are compared to the published free-air anomalies reduced from the airborne gravity measurements at the same altitude. The rms difference is 3.88 mgal. Considering the reported 2.21-mgal airborne gravity cross-over accuracy, this rms difference is not serious. Possible causes in the difference appear to be external free-air anomaly simulation errors in this work and/or the gravity reduction errors of the other. The external gravity field is predicted by adding the external free-air anomaly to the normal gravity computed using the closed form formula for the gravity above and below the surface of the ellipsoid. The predicted external gravity field in this work is expected to reasonably present the real external gravity field. This work seems to be the first structured research on the external free-air anomaly in the Korean Peninsula area, and the external gravity field can be used to improve the accuracy of the inertial navigation system.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.17 no.7
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• pp.649-658
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• 2005

Flow condensation heat transfer coefficients (HTCs) of R22, R410, Propane (R290) were measured inside a horizontal 9 hole aluminum multi-channel flat tube. The main test section in the refrigerant loop was made of a 0.53m long multi-channel flat tube of hydraulic diameter of 1.4 mm. Refrigerant was cooled by passing cold water through an annulus surrounding the test section. Data were obtained in qualities of $0.1\~0.9$ at mass flux of $200\~400kg/m^2s$ and heat flux of $7.3\~7.7kW/m^2$ at the saturation temperature of $40^{\circ}C$. All popular heat transfer correlations in single-phase subcooled liquid flow and flow condensation originally developed for large single tubes predicted the present data of the multi channel flat tube within $25\%$ deviation when effective heat transfer area was used in determining experimental data. This suggests that there is little change in flow characteristics and patterns when the tube diameter is reduced down to 1.4 mm diameter range. Hence, a modified correlation based on the present data was proposed which could be applied to small diameter tubes with effective heat transfer area. The correlation showed a mean deviation of less than $20\%$ for all data.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.9
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• pp.1408-1414
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• 2004

The use of flip-chip technology has many advantages over other approaches for high-density electronic packaging. ACF (anisotropic conductive film) is one of the major flip-chip technologies, which has short chip-to-chip interconnection length, high productivity, and miniaturization of package. In this study, thermal fatigue lift of ACF bonding flip-chip package has been predicted. Elastic and thermal properties of ACF were measured by using DMA and TMA. Temperature dependent nonlinear hi-thermal analysis was conducted and the result was compared with Moire interferometer experiment. Calculated displacement field was well matched with experimental result. Thermal fatigue analysis was also conducted. The maximum shear strain occurs at the outmost located bump. Shear stress-strain curve was obtained to calculate fatigue life. Fatigue model for electronic adhesives was used to predict thermal fatigue life of ACF bonding flip-chip packaging. DOE (Design of Experiment) technique was used to find important design factors. The results show that PCB CTE (Coefficient of Thermal Expansion) and elastic modulus of ACF material are important material parameters. And as important design parameters, chip width, bump pitch and bump width were chose. 2$^{nd}$ DOE was conducted to obtain RSM equation far the choose 3 design parameter. The coefficient of determination ($R^2$) for the calculated RSM equation is 0.99934. Optimum design is conducted using the RSM equation. MMFD (Modified Method for feasible Direction) algorithm is used to optimum design. The optimum value for chip width, bump pitch and bump width were 7.87mm, 430$\mu$m, and 78$\mu$m, respectively. Approximately, 1400 cycles have been expected under optimum conditions. Reliability analysis was conducted to find out guideline for control range of design parameter. Sigma value was calculated with changing standard deviation of design variable. To acquire 6 sigma level thermal fatigue reliability, the Std. Deviation of design parameter should be controlled within 3% of average value.

• Journal of Biosystems Engineering
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• v.34 no.1
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• pp.15-20
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• 2009

This study was conducted to develop a moisture sensor for rapeseed, a bio-diesel material. A typical rapeseed, SUNMANG, was used as a raw material. The rapeseed moisture content sensor consists of three components, such as upper and bottom electrodes, a test material dish, and a fixing housing. To evaluate the performance, a data acquisition system was equipped with the rapeseed moisture sensor, computer, printer, and main board. The findings of this study were: 1) the rapeseed moisture content was inversely proportional to electric resistance, and 2) values of electric resistance were recorded in a range of $10{\sim}100\;M{\Omega}$, depending upon a change of the moisture content. The determination of coefficient ($R^2$) and standard error between rapeseed moisture content and electric resistance were 0.9921 and ${\pm}0.289$, which indicated a highly correlative relationship. The response of rapeseed moisture sensor to temperature change was also observed for further performance test. Satisfying results were obtained, such as the determination of coefficient ($R^2$) of 0.9918, predicted standard error of ${\pm}0.373%$, deviation of 0.103%, measurement error of $0.14{\sim}0.48%$, standard deviation of $0.01{\sim}0.22%$, and measurement time of 28.3 s per point, respectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.7138-7145
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• 2015

In this study, experiments were conducted to investigate the correspondence between the output voltage of the photoelectric sensor and the frost height under heating and defrosting capacity test condition (dry bulb temperature $2^{\circ}C$, wet bulb temperature $1^{\circ}C$) described at KS C 9306, where a real heat exchanger was used as a test rig instead of a large-scale model. A digital microscope and a photoelectric sensor unit consisting of an emitter and a transistor (receiver) were installed in the front of it. A linear correlation is proposed to predict the frost height based on 150 experimental data, approximately 54% of the measured data are consistent with the predicted frost heights within a relative deviation of ${\pm}10%$, it yields good agreement with 90% of the measured data when the frost height larger than 0.3mm with in a relative deviation of ${\pm}10%$. Compared with Xiao's correlation, the slope namely, the change of frost height in accordance with the change of output voltage is consistent within the error of 2.3%. But vertical intercept shows big difference with Xiao's correlation, because it was developed with a large scale model instead of a real heat exchanger.