• Structural Engineering and Mechanics
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• 제57권2호
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• pp.265-280
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• 2016

A NLFE model was proposed to investigate the mechanical behavior of short columns, cast using plain or fibrous lightweight aggregate concrete (LWAC), and subjected to elevated temperatures of up to $700^{\circ}C$. The model was validated, before its predictions were extended to study the effect of other variables, not studied experimentally. The three-dimensional NLFE model was developed using ANSYS software and involved rational simulation of thermal mechanical behavior of plain and fibrous LWAC as well as longitudinal and lateral steel reinforcement. The prediction from the NLFE model of columns' mechanical behavior, as represented by the stress-strain diagram and its characteristics, compared well with the experimental results. The predictions of the proposed models, considering wide range of lateral reinforcement ratios, confirmed the behaviors observed experimentally and stipulated the importance of steel confinement in preserving post-heating mechanical properties of plain and fibrous LWAC columns, being subjected to high temperature.

• 대한기계학회논문집
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• 제19권11호
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• pp.3014-3021
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• 1995

This study is carried out in order to evaluate the performances of the Reynolds stress turbulence models such as SSG and GL models in the calculation of separated flow over backward-facing stepp.In addition, two slow return-to-isotropy models, YA and Rotta models combined with rapid part of SSG model are also tested. The finite volume method is used to discretize the governing differential equations, and the power-law scheme is used to approximate the convection terms. The SIMPLE algorithm is used for pressure correction in the governing equations. The results show that SSG model gives the better prediction near the reattachment point than GL model. In cases that the rapid term of SSG model is combined with Rotta and YA slow models, the results show the better predictions of stress components in recirculation zone, but indicate inaccuracy in the predictions of mean velocity.

• International Journal of Advanced Culture Technology
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• 제7권4호
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• pp.327-333
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• 2019

Wind speed data constitute important weather information for aircrafts flying at low altitudes, such as drones. Currently, the accuracy of low altitude wind predictions is much lower than that of high-altitude wind predictions. Deep neural networks are proposed in this study as a method to improve wind speed forecast information. Deep neural networks mimic the learning process of the interactions among neurons in the brain, and it is used in various fields, such as recognition of image, sound, and texts, image and natural language processing, and pattern recognition in time-series. In this study, the deep neural network model is constructed using the wind prediction values generated by the numerical model as an input to improve the wind speed forecasts. Using the ground wind speed forecast data collected at the Boseong Meteorological Observation Tower, wind speed forecast values obtained by the numerical model are compared with those obtained by the model proposed in this study for the verification of the validity and compatibility of the proposed model.

• International Journal of Aeronautical and Space Sciences
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• 제18권1호
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• pp.165-173
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• 2017

The aeromechanics predictions of HART I rotor obtained using a computational structural dynamics (CSD) code are evaluated against the wind tunnel test data. The flight regimes include low speed descending flight at an advance ratio of ${\mu}=0.151$ and cruise condition at ${\mu}=0.229$. A lifting-line based unsteady airfoil theory with C81 table look-up is used to calculate the aerodynamic loads acting on the blade. Either rolled-up free wake or multiple-trailer wake with consolidation (MTC) model is employed for the free vortex wake representation. The measured blade properties accomplished recently are used to analyze the rotor for the up-to-date computations. The comparison results on airloads and structural loads of the rotor show good agreements for descent flight and fair for cruise flight condition. It is observed that MTC model generally improves the correlation against the measured data. The structural loads predictions for all measurement locations of HART I rotor are investigated. The dominant harmonic response of the structural loads is clearly captured with MTC model.

• Tribology and Lubricants
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• 제38권2호
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• pp.53-62
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• 2022

This study presents experimental measurements of the rotordynamic performance of a motor-driven small turbocompressor supported by gas beam foil journal bearings (GBFJBs) and compares the test results with the predictions of a computational model. The experiments confirmed that the rotational synchronous frequency component dominates the behavior of the overall rotor vibrations, whereas the nonsynchronous components are insignificant, indicating the rotor-bearing system remains stable up to 100 krpm. The undamped natural frequency and imbalanced response of the rotor-bearing system are predicted when integrating the finite element model of the rotor-bearing system with the predictions of the bearing dynamic coefficients. The results are in good agreement with the experimental results. In addition, base excitation test results show that the small turbocompressor can endure large external forces and demonstrate limited rotor amplitudes. A simple single degreeof-freedom rotor model using the nonlinear stiffness of the GBFJBs can effectively predict the test results.

• 한국과학교육학회지
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• 제12권3호
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• pp.61-76
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• 1992

The purpose of this study was to develop the instructional model and teaching material to change the middle school students'conceptions of electric current into the scientific ones and to investigate the effects of the model in actual classrooms. We identified the students' ideas and their misunderstanding about the concept of eIectic current through reviewing the literatures and our in this study. Based on the above results, we developed the instructional model and designed the teaching sequence and prepare the learning materials about the unit of the electric current in middle school Our instructional model was based on 'learning cycle' developed by Lawson, but the new stage called "exploration through qualitative questions" to elicit the students' own conceptions was inserted to it. To investigate the effects or the new teaching model, the pre- and post-test using the POE type were administered to experimental group(52 students) taught with learning cycles and control group(52 students) taught with traditional styles. The results are as follows; 1) The rates of correct. predictions was varying according to the kinds of problems. And the rates of the correct. reasons of their predictions were lower than those of the predictions. 2) The mean scores of the post-test of both groups were significantly higher than those of the pre-test. We could not find statistically significant difference in theme an score between experimental group and control group after implementation of the model. But the experimental group gained higher scores than those of the control group on two problem. Therefore, although we cannot show the prominent effects of our teaching model based on learning cycles, there are some effects of our model on changing the middle school students' conceptions of electric current.

• 대기
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• 제33권4호
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• pp.355-365
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• 2023

To proactively manage climate risk, near-term climate predictions on annual to decadal time scales are of great interest to various communities. This study evaluates the near-term climate prediction skills in East Asia with DePreSys4 retrospective decadal predictions. The model is initialized every November from 1960 to 2020, consisting of 61 initializations with ten ensemble members. The prediction skill is quantitatively evaluated using the deterministic and probabilistic metrics, particularly for annual mean near-surface temperature, land precipitation, and sea level pressure. The near-term climate predictions for May~September and November~March averages over the five years are also assessed. DePreSys4 successfully predicts the annual mean and the five-year mean near-surface temperatures in East Asia, as the long-term trend sourced from external radiative forcing is well reproduced. However, land precipitation predictions are statistically significant only in very limited sporadic regions. The sea level pressure predictions also show statistically significant skills only over the ocean due to the failure of predicting a long-term trend over the land.

• 천문학회지
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• 제45권5호
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• pp.111-116
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• 2012

We present a mathematical model that predicts the variation of illuminance during a solar eclipse, considering continuous effects of limb darkening. We assume that (1) the Sun and the Moon constitute perfect spheres, (2) the Moon crosses the Sun with a constant apparent velocity, and (3) sunspots, prominences, and coronae can be neglected. We compare predictions of this model with actual measurements made by M$\ddot{o}$llmann & Vollmer (2006) during a total solar eclipse in Turkey, and with predictions of existing models. The new model is shown to describe the actual phenomenon more accurately than existing models.

• Asian-Australasian Journal of Animal Sciences
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• 제12권2호
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• pp.282-286
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• 1999

Growth of pigs is influenced by many factors. To assist pig producers in the evaluation of alternative feeding and management strategies growth models have been developed. In the Netherlands the Technical Model Pigfeeding (TMV) is developed. This model predicts the influence of feed intake, feed composition, genotype, sex and climate on growth, body composition, gross margin and mineral excretion of healthy growing/finishing pigs. The purpose of TMV is to support information services, feed companies, researchers and students. In addition to providing accurate predictions, a model should also be user-friendly and wishes of the user should be taken into account to stimulate application of the model in practice. In this paper, the theoretical background of TMV and a methodology to stimulate application of models in practice will be described.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.253-256
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• 1996

Rainfall is one of the major and complicated elements of hydrologic system. Accurate prediction of rainfall is very important to mitigate storm damage. The neural network is a good model to be applied for the classification problem, large combinatorial optimization and nonlinear mapping. In this dissertation, rainfall predictions by the neural network theory were presented. A multi-layer neural network was constructed. The network learned continuous-valued input and output data. The network was used to predict rainfall. The online, multivariate, short term rainfall prediction is possible by means of the developed model. A multidimensional rainfall generation model is applied to Seoul metropolitan area in order to generate the 10-minute rainfall. Application of neural network to the generated rainfall shows good prediction. Also application of neural network to 1-hour real data in Seoul metropolitan area shows slightly good predictions.