• Wind and Structures
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• v.2 no.4
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• pp.325-345
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• 1999

In recent years, rainscreen walls based on the pressure equalization principle are often used in building construction. To improve the understanding of the influence of several design parameters on the pressure equalization performance of such wall systems, a theoretical consideration of the problem may be more appropriate. On this basis, this paper presents two theoretical models, one based on mass balance and the other based on the Helmholtz resonator theory, for the prediction of cavity pressure in rigid rainscreen walls. New measures to assess the degree of pressure equalization of rainscreen walls are also suggested. The results show that the model based on mass balance is sufficiently accurate and efficient in predicting the cavity pressure variations. Further, the performance of the proposed model is evaluated utilizing the data obtained from full-scale tests and the results are discussed in detail.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.11
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• pp.113-118
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• 1991

Hypertension is a medical problem with no permanent cure. Extended hypertension can cause various cardio vascular diseases, cerebral vascular diseases, and circulatory system trouble. Medical treatment at present does not consider circadian variation of blood pressure in patients ; therefore, the problem of over-reduction of blood pressure through drugs sometimes occurs. This paper presents a prediction model of circadian variation or moon blood pressure employing the endocrine grand and the autonomic nervous system.

• Wind and Structures
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• v.36 no.6
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• pp.367-377
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• 2023

This study proposes a few-shot learning model for extrapolating the wind pressure of scaled experiments to full-scale measurements. The proposed ML model can use scaled experimental data and a few full-scale tests to accurately predict the remaining full-scale data points (for new specimens). This model focuses on extrapolating the prediction to different scales while existing approaches are not capable of accurately extrapolating from scaled data to full-scale data in the wind engineering domain. Also, the scaling issue observed in wind tunnel tests can be partially resolved via the proposed approach. The proposed model obtained a low mean-squared error and a high coefficient of determination for the mean and standard deviation wind pressure coefficients of the full-scale dataset. A parametric study is carried out to investigate the influence of the number of selected shots. This technique is the first of its kind as it is the first time an ML model has been used in the wind engineering field to deal with extrapolation in wind performance prediction. With the advantages of the few-shot learning model, physical wind tunnel experiments can be reduced to a great extent. The few-shot learning model yields a robust, efficient, and accurate alternative to extrapolating the prediction performance of structures from various model scales to full-scale.

• Journal of Powder Materials
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• v.28 no.6
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• pp.478-482
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• 2021

The effect of the process conditions of high-velocity oxygen fuel (HVOF) thermal spray coating on the porosity of the coating layer is investigated. HVOF coating layers are formed by depositing amorphous FeMoCrBC powder. Oxygen pressure varies from 126 to 146 psi and kerosene pressure from 110 to 130 psi. The Microstructural analysis confirms its porosity. Data analysis is performed using experimental data. The oxygen pressure-kerosene pressure ratio is found to be a key contributor to the porosity. An empirical model is proposed using linear regression analysis. The proposed model is then validated using additional test data. We confirm that the oxygen pressure-kerosene pressure ratio exponentially increases porosity. We present a porosity prediction model relationship for the oxygen pressure-kerosene pressure ratio.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.3
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• pp.44-50
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• 2004

The purpose of this study is to improve the prediction ability of the atomization and vaporization processes of GDI spray under high-pressure and high-temperature conditions. Several models have been introduced and compared. The atomization process was modeled using hybrid breakup model that is composed of Conical Sheet Disintegration (CSD) model and Aerodynamically Progressed TAB(APTAB) model. The vaporization process was modeled using Spalding model, modified Spalding model and Abramzon ＆ Sirignano model. Exciplex fluorescence method was used for comparing the calculated with the experimental results. The experiment and calculation were performed at the ambient pressure of 0.5 MPa and 1.0 MPa and the ambient temperature of 473k. Comparison of caldulated and experimental spray characteristics was carried out and Abramzon ＆ Sirignano model and modified Spalding model had the better prediction ability for vaporization process than Spalding model.

• Journal of ILASS-Korea
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• v.13 no.3
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• pp.117-125
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• 2008

The correlations for the prediction of maximum liquid-phase penetration in diesel spray are reviewed in this study. The existing models developed for the prediction of maximum liquid-phase penetration can be categorized as the zero-dimensional (empirical) model, the multi-dimensional model and the other model. The existing zero-dimensional model can be classified into four groups and the existing multidimensional models can be classified into three groups. The other model includes holistic hydraulic and spray model. The maximum liquid-phase penetration is mainly affected by nozzle diameter, fuel volatility, injection pressure, ambient gas pressure, ambient gas density and fuel temperature. In the case of empirical correlations incorporated with spray angle, the predicted results will be different according to the selection of correlation for spray angle. The research for the effect of boiling point temperatures on maximum liquid-phase penetration is required. In the case of multidimensional model, there exist problems of the grid and spray sub-models dependency effects.

• Journal of Biosystems Engineering
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• v.23 no.3
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• pp.219-228
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• 1998

This study was conducted to develop the mathematical model and computer simulation program(TPPMTV98) for predicting the tractive performance of tracked vehicles. It takes into account major design parameters of the vehicle as well as the pressure-sinkage and shearing characteristics of the soil, and the response of the soil to repetitive loading. Structural analysis and numerical iterative method were used for the derivation of mathematical model. The simulatiom model TPPMTV98 can predict the ground pressure distribution and the shear stress under a track, the motion resistance, the tractive effort and the drawbar pull of the vehicles as functions of slip. Predicted tractive performance results obtained by the simulation model were validated by comparing the results firm the Wong's model, the offectiveness of Wong's model validated by many of the experiment. It was found that there is fairy close agreement between the prediction by TPPMTV98 and the results from Wong's model. The computer simulation model TPPMTV98 can be used for the optimization of tracked vehicle design or for the evaluation of vehicle candidates for a given mission and environment.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.834-841
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• 2022

System thermal-hydraulic (STH) code is adopted for nuclear safety analysis. The critical flow model (CFM) is significant for the accuracy of STH simulation. To overcome the defects of current CFMs (low precision or long calculation time), a CFM based on a genetic neural network (GNN) has been developed in this work. To build a powerful model, besides the critical mass flux, the critical pressure and critical quality were also considered in this model, which was seldom considered before. Comparing with the traditional homogeneous equilibrium model (HEM) and the Moody model, the GNN model can predict the critical mass flux with a higher accuracy (approximately 80% of results are within the ±20% error limit); comparing with the Leung model and the Shannak model for critical pressure prediction, the GNN model achieved the best results (more than 80% prediction results within the ±20% error limit). For the critical quality, similar precision is achieved. The GNN-based CFM in this work is meaningful for the STH code CFM development.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.09a
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• pp.107-114
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• 2003

There are many problems in the prediction of dynamic behaviors of saturated soils because undrained excess pore water pressure builds up and then the strain softening behavior is occurred simultaneously. A few analytical constitutive models based on the effective stress concept have been proposed but most models hardly predict the excess pore water pressure and strain softening behaviors correctly In this study, the disturbed state concept (DSC) model proposed by Dr, Desai was modified to predict the saturated soil behaviors under the dynamic loads. Also, back-prediction program was developed for verification of modified DSC model. Cyclic triaxial tests were carried out to determine DSC parameters and test result was compared with the result of back-prediction. Through this research, it is proved that the proposed model based on the modified disturbed state concept can predict the realistic soil dynamic characteristics such as stress degradation and strain softening behavior according to dynamic process of excess pore water pressure.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4042-4051
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• 2021

In this work, we applied a multilevel modeling technique to estimate the diametral creep in the pressure tubes of Korean Canada Deuterium Uranium (CANDU) units. Data accumulated from in-service inspections were used to develop the model. To confirm the strength of the multilevel models, a 2-level multilevel model considering the relationship between channels for a CANDU unit was compared with existing linear models. The multilevel model exhibited a very robust prediction accuracy compared to the linear models with different data pooling methods. A 3-level multilevel model, which considered individual bundles, channels, and units, was also implemented. The influence of the channel installation direction was incorporated into the three-stage multilevel model. For channels that were previously measured, the developed 3-level multilevel model exhibited a very good predictive power, and the prediction interval was very narrow. However, for channels that had never been measured before, the prediction interval widened considerably. This model can be sufficiently improved by the accumulation of more data and can be applied to other CANDU units.