• The KSFM Journal of Fluid Machinery
• /
• v.20 no.2
• /
• pp.63-68
• /
• 2017

This paper predicts the rotordynamic force coefficients of tilting pad journal bearings (TPJBs) with ball-socket pivot and compares the predictions to the published test data obtained under load-between-pad (LBP) configuration. The present TPJB model considers the pivot stiffness calculated based on the Hertzian contact stress theory. Due to the compliance of the pivot, the predicted journal eccentricity agree well with the measured journal center trajectory for increasing static loads, while the early prediction without pivot model consideration underestimates it largely. The predicted pressure profile shows the significant pressure development even on the unloaded pads along the direction opposite to the loading direction. The predicted stiffness coefficients increase as the static load and the rotor speed increase. They agree excellently with test data from open literature. The predicted damping coefficients increase as the static load increases and the rotor speed decreases. The prediction underestimates the test data slightly. In general, the current predictive model including the pivot stiffness improves the accuracy of the rotordynamic performance predictions when compared to the previously published predictions.

• Transactions of the Korean Society of Pressure Vessels and Piping
• /
• v.19 no.2
• /
• pp.93-101
• /
• 2023

In this study, a deep learning algorithm was used to diagnose electric potential signals obtained through CIPS and DCVG, used indirect inspection methods to confirm the soundness of buried pipes. The deep learning algorithm consisted of CNN(Convolutional Neural Network) model for diagnosing the electric potential signal and Grad CAM(Gradient-weighted Class Activation Mapping) for showing the flaw prediction point. The CNN model for diagnosing electric potential signals classifies input data as normal/abnormal according to the presence or absence of flaw in the buried pipe, and for abnormal data, Grad CAM generates a heat map that visualizes the flaw prediction part of the buried pipe. The CIPS/DCVG signal and piping layout obtained from the 3D finite element model were used as input data for learning the CNN. The trained CNN classified the normal/abnormal data with 93% accuracy, and the Grad-CAM predicted flaws point with an average error of 2m. As a result, it confirmed that the electric potential signal of buried pipe can be diagnosed using a CNN-based deep learning algorithm.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.27 no.12
• /
• pp.1744-1749
• /
• 2003

The purpose of this study is to obtain the information about the development process of GDI spray. To acquire the characteristics of GDI spray, the computational study of hollow cone spray for high-pressure swirl injectors was performed. Several hybrid models using the modified KIVA code have been introduced and compared. WB model and LISA model were used for the primary breakup, and DDB and APTAB models were used for secondary breakup. To compare with the calculated results, the experimental results such as cross-sectional images and SMD distribution were acquired by laser Mie scattering technique and Phase Doppler Analyzer respectively. The results show that LISA+APTAB hybrid model has the best prediction for spray formation process.

• Tribology and Lubricants
• /
• v.14 no.1
• /
• pp.85-93
• /
• 1998

The Bair & Winer's limiting shear stress rheological model is incorporated into the Reynolds equation to successfully predict the traction and film thickness for an isothermal line contact using the primary rheological properties. The modified WLF viscosity model and Barus viscosity model are also adapted for the realistic prediction of EHD tractional behavior. The influences of the limiting shear stress and slide-roll ratio on the pressure spike, film thickness, distribution of shear stress and nonlinear variation of traction are examined. A good agreement between the disc machine experiments and numerical traction prediction has been established. The film thickness due to non-Newtonian effects does not deviate significantly from the fdm thicknesss with Newtonian lubricant.

• Proceedings of the SAREK Conference
• /
• 2005.11a
• /
• pp.288-293
• /
• 2005

The pressure distribution and heat transfer coefficient were measured at room temperature in the high suspension density CFB heat exchanger with multiple vertical tubes and the effective density of CFB was determined. The theoretical model for predicting heat transfer coefficient was developed in this study. The model predictions were compared with the measured heat transfer coefficient to show relatively good agreement between them.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.41 no.6
• /
• pp.44-53
• /
• 1999

The statistical characteristics of the factors related to the daily rainfall prediction model are analyzed . Records of daily precipitation, mean air temperature, relative humidity , dew-point temperature and air pressure from 1973∼1998 at 8 meteorological sttions in south-western part of Korea were used. 1. Serial correlatino of daily precipitaiton was significant with the lag less than 1 day. But , that of other variables were large enough until 10 day lag. 2. Crosscorrelation of air temperature, relative humidity , dew-point temperature showed similar distribution wiht the basin contrours and the others were different. 3. There were significant correlation between the meteorological variables and precipitation preceded more than 2 days. 4. Daily preciption of each station were treated as a truncated continuous random variable and the annual periodic components, mean and standard deviation were estimated for each day. 5. All of the results could be considered to select the input variables of regression model or neural network model for the prediction of daily precipitation and to construct the stochastic model of daily precipitation.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.19 no.3
• /
• pp.263-268
• /
• 2007

The pressure drop and heat transfer coefficient were measured at room temperature in a CFB heat exchanger with multiple vertical tubes. The circulation rate of solid particles was also measured. The theoretical model for predicting heat transfer coefficient using the solid flowrate was developed in this study. The model predictions were compared with the measured heat transfer coefficient to show relatively good agreement.

• Atmosphere
• /
• v.16 no.1
• /
• pp.19-31
• /
• 2006

The purpose of this study is to develop the statistical model to predict sea level pressure of typhoon period in south coast of the Korean Peninsula. Seven typhoons, which struck south coast of the Korean Peninsula, are selected for this study, and the data for analysis include the central pressure and location of typhoon, and sea level pressure and location of 19 observing site. Models employed in this study are the first order regression, the second order regression and the neural network. The dependent variable of each model is a 3-hr interval sea level pressure at each station. The cause variables are the central pressure of typhoon, distance between typhoon center and observing site, and sea level pressure of 3 hrs before, whereas the indicative variable reveals whether it is before or after typhoon passing. The data are classified into two groups - one is the full data obtained during typhoon period and the other is the data that sea level pressure is less than 1000 hPa. The stepwise selection method is used in the regression model while the node number is selected in the neural network by the Schwarz's Bayesian Criterion. The performance of each model is compared in terms of the root-mean square error. It turns out that the neural network shows better performance than other models, and the case using the full data produces similar or better results than the case using the other data.

• Journal of Biomedical Engineering Research
• /
• v.33 no.3
• /
• pp.148-154
• /
• 2012

Hemorrhagic shock is a primary cause of deaths resulting from injury in the world. Although many studies have tried to diagnose accurately hemorrhagic shock in the early stage, such attempts were not successful due to compensatory mechanisms of humans. The objective of this study was to construct a survival prediction model of rats in acute hemorrhagic shock using a random forest (RF) model. Heart rate (HR), mean arterial pressure (MAP), respiration rate (RR), lactate concentration (LC), and peripheral perfusion (PP) measured in rats were used as input variables for the RF model and its performance was compared with that of a logistic regression (LR) model. Before constructing the models, we performed 5-fold cross validation for RF variable selection, and forward stepwise variable selection for the LR model to examine which variables were important for the models. For the LR model, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (ROC-AUC) were 0.83, 0.95, 0.88, and 0.96, respectively. For the RF models, sensitivity, specificity, accuracy, and AUC were 0.97, 0.95, 0.96, and 0.99, respectively. In conclusion, the RF model was superior to the LR model for survival prediction in the rat model.

• 한국전산유체공학회:학술대회논문집
• /
• 2007.04a
• /
• pp.134-137
• /
• 2007

Aero-Heating phenomenon is one of the severe problems occurring in high speed missile flight. in the high speed flight, not only stagnation point but also aft body parts encounter high temperature related structural problems. But the phenomenon is not easy to predict accurately because unsteady calculation according to a flight trajectory is needed, and takes much time. In this Paper, a fast and precise scheme is introduced, which calculates heat flow and temperature by simple pressure field prediction on a missile.