• Wind and Structures
• /
• 제37권4호
• /
• pp.303-314
• /
• 2023

In this study, two steady RANS turbulence models (SST k-ω and Realizable k-ε) and four unsteady turbulence models (URANS SST k-ω and Realizable k-ε, SST-SAS, and SST-IDDES) are evaluated with respect to their capacity to predict crosswind characteristics on high-speed trains (HSTs). All of the numerical simulations are compared with the wind tunnel values and LES results to ensure the accuracy of each turbulence model. Specifically, the surface pressure distributions, time-averaged aerodynamic coefficients, flow fields, and computational cost are studied to determine the suitability of different models. Results suggest that the predictions of the pressure distributions and aerodynamic forces obtained from the steady and transient RANS models are almost the same. In particular, both SAS and IDDES exhibits similar predictions with wind tunnel test and LES, therefore, the SAS model is considered an attractive alternative for IDDES or LES in the crosswind study of trains. In addition, if the computational cost needs to be significantly reduced, the RANS SST k-ω model is shown to provide relatively reasonable results for the surface pressures and aerodynamic forces. As a result, the RANS SST k-ω model might be the most appropriate option for the expensive aerodynamic optimizations of trains using machine learning (ML) techniques because it balances solution accuracy and resource consumption.

• Smart Structures and Systems
• /
• 제25권1호
• /
• pp.81-96
• /
• 2020

Wind measurements were made on the Canton Tower at a height of 461 m above ground during the Typhoon Vincente, the wind-induced accelerations and displacements of the tower were recorded as well. Comparisons of measured wind parameters at upper level of atmospheric boundary layer with those adopted in wind tunnel testing were presented. The measured turbulence intensity can be smaller than the design value, indicating that the wind tunnel testing may underestimate the crosswind structural responses for certain lock-in velocity range of vortex shedding. Analyses of peak factors and power spectral density for acceleration response shows that the crosswind responses are a combination of gust-induced buffeting and vortex-induced vibrations in the certain range of wind directions. The identified modal frequencies and mode shapes from acceleration data are found to be in good agreement with existing experimental results and the prediction from the finite element model. The damping ratios increase with amplitude of vibration or equivalently wind velocity which may be attributed to aerodynamic damping. In addition, the natural frequencies determined from the measured displacement are very close to those determined from the acceleration data for the first two modes. Finally, the relation between displacement responses and wind speed/direction was investigated.

• 천문학회보
• /
• 제38권2호
• /
• pp.94.1-94.1
• /
• 2013

Whistler mode chorus waves, which are observed outside the plasmasphere of the Earth's magnetosphere, play a major role in accelerating and scattering energetic electrons in the radiation belts. In this study we developed a predicting scheme of the global distribution of chorus by using the Time History of Events and Macroscale Interactions during Substorms (THEMIS) satellite data. First, we determined global spatial distributions of chorus activity, and identified fit functions that best represent chorus intensities in specific L-MLT zones. Second, we determined the specific dependence of average chorus intensity on preceding solar wind conditions (e.g., solar wind speed, IMF Bz, energy coupling degree) as well as preceding geomagnetic states (as represented by AE, for example). Finally, we combined these two results to develop the predicting functions for the global distribution and intensity of chorus. Implementing these results in the radiation belt models should improve the local acceleration effect by chorus waves.

• 대기
• /
• 제24권4호
• /
• pp.471-479
• /
• 2014

A road surface temperature prediction model (UM-Road) using input data of the Unified Model (UM) output and road physical properties is developed and verified with the use of the observed data at road weather information system. The UM outputs of air temperature, relative humidity, wind speed, downward shortwave radiation, net longwave radiation, precipitation and the road properties such as slope angles, albedo, thermal conductivity, heat capacity at maximum 7 depth are used. The net radiation is computed by a surface radiation energy balance, the ground heat flux at surface is estimated by a surface energy balance based on the Monin-Obukhov similarity, the ground heat transfer process is applied to predict the road surface temperature. If the observed road surface temperature exists, the simulated road surface temperature is corrected by mean bias during the last 24 hours. The developed UM-Road is verified using the observed data at road side for the period from 21 to 31 March 2013. It is found that the UM-Road simulates the diurnal trend and peak values of road surface temperature very well and the 50% (90%) of temperature difference lies within ${\pm}1.5^{\circ}C$ (${\pm}2.5^{\circ}C$) except for precipitation case.

• Communications for Statistical Applications and Methods
• /
• 제30권2호
• /
• pp.119-133
• /
• 2023

With the rapid growth of the economy and fossil fuel consumption, the concentration of air pollutants has increased significantly and the air pollution problem is no longer limited to small areas. We conduct statistical analysis with the actual data related to air quality that covers the entire of South Korea using R and Python. Some factors such as SO₂, CO, O₃, NO₂, PM₁₀, precipitation, wind speed, wind direction, vapor pressure, local pressure, sea level pressure, temperature, humidity, and others are used as covariates. The main goal of this paper is to predict air quality index (AQI) spatio-temporal data. The observations of spatio-temporal big datasets like AQI data are correlated both spatially and temporally, and computation of the prediction or forecasting with dependence structure is often infeasible. As such, the likelihood function based on the spatio-temporal model may be complicated and some special modelings are useful for statistically reliable predictions. In this paper, we propose several methods for this big spatio-temporal AQI data. First, random effects with spatio-temporal basis functions model, a classical statistical analysis, is proposed. Next, neural networks model, a deep learning method based on artificial neural networks, is applied. Finally, random forest model, a machine learning method that is closer to computational science, will be introduced. Then we compare the forecasting performance of each other in terms of predictive diagnostics. As a result of the analysis, all three methods predicted the normal level of PM_2.5 well, but the performance seems to be poor at the extreme value.

• 대한원격탐사학회지
• /
• 제37권3호
• /
• pp.395-408
• /
• 2021

본 연구에서는 GIS와 CFD 모델을 이용하여 풍속과 풍향이 건물 화재에 미치는 영향을 조사하였다. 이를 위해, 2020년 10월 8일 울산의 한 아파트에서 발생한 화재 사고에 대한 수치 실험을 수행하였고, 현실적인 기상 조건을 반영하기 위하여 국지기상예보시스템(LDAPS)의 바람과 온위 자료를 초기·경계 자료로 사용하였다. 먼저, 현실적인 경계 조건을 사용하여 두 가지 수치 실험을 수행하였다(규준 실험에서는 건물 화재를 고려하고, 다른 실험에서는 건물 화재를 제외하고는 규준 실험과 동일한 기상 조건 이용). 그런 다음, 규준 실험과 유입 풍속과 방향이 다른 4개의 수치 실험을 추가로 수행하였다. 수치 실험 결과, 발화 지점이 건물 풍상측에 위치할 때에는 화재로 인한 강한 상승 기류가 건물 지붕과 풍하측 지역에 영향을 미쳤다. 또한, 대피층(15층)은 건물 풍상 측 벽면의 화재를 풍하측으로 확산시키는 역할을 했다. 유입 풍속이 약할수록 발화점 주변으로의 화재가 좁게 확산되었지만 건물 위로 화염이 도달하는 고도는 상승했다. 유입 풍향이 반대인 경우, 발화 지점이 풍하측에 위치할 때에는 화염이 건물 풍상 측으로 확산되지 않았다. 본 연구 결과는 풍속과 풍향이 화재가 발생한 건물 주변의 흐름과 온도(화염) 분포에 중요하다는 것을 보여준다.

• 스마트미디어저널
• /
• 제8권1호
• /
• pp.82-91
• /
• 2019

This study provides the analysis and prediction of fruits diseases related to weather conditions (temperature, wind speed, solar power, rainfall and humidity) using Linear Model and Poisson Regression. The main goal of the research is to control the method of fruits diseases and also to prevent diseases using less agricultural pesticides. So, it is needed to predict the fruits diseases with weather data. Initially, fruit data is used to detect the fruit diseases. If diseases are found, we move to the next process and verify the condition of the fruits including their size. We identify the growth of fruit and evidence of diseases with Linear Model. Then, Poisson Regression used in this study to fit the model of fruits diseases with weather conditions as an input provides the predicted diseases as an output. Finally, the residuals plot, Q-Q plot and other plots help to validate the fitness of Linear Model and provide correlation between the actual and the predicted diseases as a result of the conducted experiment in this study.

• 대기
• /
• 제26권1호
• /
• pp.1-18
• /
• 2016

Eleven Tropical Cyclone (TC) intensity guidance models in the western North Pacific have been validated over 2008~2014 based on various analysis methods according to the lead time of forecast, year, month, intensity, rapid intensity change, track, and geographical area with an additional focus on TCs that influenced the Korean peninsula. From the evaluation using mean absolute error and correlation coefficients for maximum wind speed forecasts up to 72 h, we found that the Hurricane Weather Research and Forecasting model (HWRF) outperforms all others overall although the Global Forecast System (GFS), the Typhoon Ensemble Prediction System of Japan Meteorological Agency (TEPS), and the Korean version of Weather and Weather Research and Forecasting model (KWRF) also shows a good performance in some lead times of forecast. In particular, HWRF shows the highest performance in predicting the intensity of strong TCs above Category 3, which may be attributed to its highest spatial resolution (~3 km). The Navy Operational Global Prediction Model (NOGAPS) and GFS were the most improved model during 2008~2014. For initial intensity error, two Japanese models, Japan Meteorological Agency Global Spectral Model (JGSM) and TEPS, had the smallest error. In track forecast, the European Centre for Medium-Range Weather Forecasts (ECMWF) and recent GFS model outperformed others. The present results has significant implications for providing basic information for operational forecasters as well as developing ensemble or consensus prediction systems.

• 수산해양기술연구
• /
• 제57권3호
• /
• pp.236-245
• /
• 2021

In this study, a drifting test using a experimental vessel (2,966 tons) in the northern waters of Jeju was carried out for the first time in order to obtain the fundamental data for drift. During the test, it was shown that the average leeway speed and direction by GPS position were 0.362 m/s and 155.54° respectively and the leeway rate for wind speed was 8.80%. The analysis of linear regression modes about leeway speed and direction of the experimental vessel indicated that wind or current (i.e. explanatory variable) had a greater influence upon response variable (e.g. leeway speed or direction) with the speed of the wind and current rather than their directions. On the other hand, the result of multiple regression model analysis was able to predict that the direction was negative, and it was demonstrated that predicted values of leeway speed and direction using an experimental vessel is to be more influential by current than wind while the leeway speed through variance and covariance was positive. In terms of the leeway direction of the experimental vessel, the same result of the leeway speed appeared except for a possibility of the existence of multi-collinearity. Then, it can be interpreted that the explanatory variables were less descriptive in the predicted values of the leeway direction. As a result, the prediction of leeway speed and direction can be demonstrated as following equations. Ŷ₁= 0.4031-0.0032X₁+0.0631X₂-0.0010X₃+0.4110X₄ Ŷ₂= 0.4031-0.6662X₁+27.1955X₂-0.6787X₃-420.4833X₄ However, many drift tests using actual vessels and various drifting objects will provide reasonable estimations, so that they can help search and rescue fishing gears as well.

• 한국재난정보학회:학술대회논문집
• /
• 한국재난정보학회 2016년 정기학술대회
• /
• pp.207-211
• /
• 2016

최근 세계적으로 기후변화에 따라 자연재해에 의한 피해가 대형화, 가속화 되면서 이를 예측하고 대응할 수 있는 체계적이며 국내 특성을 반영할 수 있는 피해예측 시스템의 필요성이 제기되고 있다. 국내에서는 경험적 통계기반의 강우예측에 대한 연구가 주로 진행되었으며, 강풍에 대한 연구는 부족한 상황이다. 본 연구는 기존의 연구와는 달리 모델링을 통한 예측이 아닌 실제 발생한 강풍 피해 자료를 기반으로 풍속에 따른 피해액을 예측할 수 있는 강풍 피해예측 단순회귀모형을 개발하는 것을 목적으로 한다.