• Wind and Structures
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• 제24권5호
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• pp.465-480
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• 2017

Modelling an equilibrium atmospheric boundary layer (ABL) in computational wind engineering (CWE) and relevant areas requires the boundary conditions, the turbulence model and associated constants to be consistent with each other. Among them, the inflow boundary conditions play an important role and determine whether the equations of the turbulence model are satisfied in the whole domain. In this paper, the idea of modeling an equilibrium ABL through specifying proper inflow boundary conditions is extended to the SST $k-{\omega}$ model, which is regarded as a better RANS model for simulating the blunt body flow than the standard $k-{\varepsilon}$ model. Two new sets of inflow boundary conditions corresponding to different descriptions of the inflow velocity profiles, the logarithmic law and the power law respectively, are then theoretically proposed and numerically verified. A method of determining the undetermined constants and a set of parameter system are then given, which are suitable for the standard wind terrains defined in the wind load code. Finally, the full inflow boundary condition equations considering the scale effect are presented for the purpose of general use.

• 대기
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• 제25권4호
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• pp.659-667
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• 2015

In this study, sensitivity of inflow wind speed and turbulent Schmidt number to pollutant dispersion in an urban street canyon is investigated, by comparing CFD-simulated results to wind-tunnel results. For this, we changed systematically inflow wind speed at the street-canyon height ($1.5{\sim}10.0m\;s^{-1}$ with the increment of $0.5m\;s^{-1}$) and turbulent Schmidt number (0.2~1.3 with interval of 0.1). Also, we performed numerical experiments under the conditions that turbulent Schmidt numbers selected with the magnitude of mean kinetic energy at each grid point were assigned in the street canyon. With the increase of the inflow wind speed, the model underestimated (overestimated) pollutant concentration in the upwind (downwind) side of the street canyon because of the increase of pollutant advection. This implies that, for more realistic reproduction of pollutant dispersion in urban street canyons, large (small) turbulent Schmidt number should be assigned for week (strong) inflow condition. In the cases of selectively assigned turbulent Schmidt number, mean bias remarkably decreased (maximum 60%) compared to the cases of constant turbulent Schmidt number assigned. At week (strong) inflow wind speed, root mean square error decreases as the area where turbulent Schmidt number is selectively assigned becomes large (small).

• 한국물환경학회지
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• 제35권4호
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• pp.352-361
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• 2019

The purpose of this study is to analyze the characteristics and correlations of the Yeongcheon Lake in order to reduce the occurrence of harmful cyanobacteria. In this study, we investigated the water quality and phytoplankton of the lake from May to November in 2017. Correlation and data mining analyses were performed to analyze the relationship between the two factors. The water temperature was lowest at the point where the Yeongcheon Lake inflow occurs at Imha Lake. It was highest at the point where the outflow occurs to Angye Lake. The pH was also highest at the outflow point, but in the case of DO, it was highest at the midpoint between the inflow and outflow. The main cyanobacteria that emerged during the study period were Oscillatorialimosa, Microcysti saeruginosa and Aphanizomenon flos-aquae. As a result of correlation analysis, the water temperature, inflow, COD loading, TOC loading at the inflow point of the Yeongcheon Lake were the items that were related to the harmful cyanobacteria. The data mining analysis indicated that the TP loading and harmful cyanobacteria in the inflow point of the Yeongcheon Lake were influential on the detrimental cyanobacteria in the Yeongcheon Lake outflow point. When the TP loading was less than 39.0 kg/day at the inflow site, it was expected that the amount of harmful cyanobacteria could be maintained below 10,000 cells/mL.

• 한국물환경학회지
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• 제27권1호
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• pp.88-97
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• 2011

To identify the inflow and outflow characteristics of allchthonous organic matters and examine the change of allochthonous organic matter load pattern due to the climate change, we investigated the temporal variations of DOC and POC concentrations within inflow water and dam discharge water and spatio-temporal distribution of POM within the lake water in Lake Soyang which is the largest dam reservoir in Korea in 2006. Most of allochthonous DOC flowed into the lake water during initial rain and was not affected by the amount of precipitation, whereas most of allochthonous POC flowed into during concentrated heavy rain and the concentration of POC was significantly associated with the amount of inflow water and precipitation. Calculated annual allochthonous organic matter loads in Lake Soyang from 2003 to 2006 using the regression equation between the amount of inflow water and the concentration of POC indicate allochthonous organic matter loads are mainly affected by total influx and extreme influx of inflow water. The spatio-temporal distribution of POM indicated allochthonous organic matter of inflow river during flood period in July transported from upper part to middle and lower part of the lake a month later respectively along the middle layer of water column in Lake Soyang.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.437-437
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• 2022

The prediction of dam inflow rate is crucial for the management of the largest multi-purpose dam in South Korea, the Soyang Dam. The main issue associated with the management of water resources is the stochastic nature of the reservoir inflow leading to an increase in uncertainty associated with the inflow prediction. The Autoregressive (AR) model is commonly used to provide the simulation and forecast of hydrometeorological data. However, because its estimation is based solely on the time-series data, it has the disadvantage of being unable to account for external variables such as climate information. This study proposes the use of the Autoregressive Exogenous Stochastic Volatility (ARXSV) model within a Bayesian modeling framework for increased predictability of the monthly dam inflow by addressing the exogenous and stochastic factors. This study analyzes 45 years of hydrological input data of the Soyang Dam from the year 1974 to 2019. The result of this study will be beneficial to strengthen the potential use of data-driven models for accurate inflow predictions and better reservoir management.

• 한국수자원학회논문집
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• 제51권7호
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• pp.607-616
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• 2018

본 연구에서는 ANFIS 기반의 유황별 댐 예측유입량 산정 기법(Flow regime-based ANFIS Dam Inflow Prediction, FADIP)을 개발하고, 이를 단순 ANFIS 기반 댐 예측유입량 산정 기법(ANFIS Dam Inflow Prediction, ADIP)과 비교 평가하였다. 대상유역은 국내 주요 다목적댐인 충주댐 유역과 소양강댐 유역을 선정하였으며, 입력자료로 댐 유입량, 강수량, 장기기상예보 자료를 사용하였다. 모델의 훈련 및 보정기간으로 충주댐 유역은 1987~2010년, 소양강댐 유역은 1984~2010년을 선정하였다. 검정기간은 두 유역 모두 2011~2016년을 활용하였다. 훈련 및 보정결과 FADIP는 ADIP에 비해 평수기, 저수기에 훈련이 개선되는 것으로 나타났다. 검정결과 ADIP는 통계모델의 학습방법 특성상 일반적인 사상에 학습이 이루어져, 저수기에 예측성이 떨어지는 것으로 나타났다. 반면 FADIP는 ADIP에 비해 전기간의 정확도가 향상되었으며, 특히 평수기와 저수기에 예측성이 우수하였다. 따라서 FADIP는 다목적댐 이수관리에 활용성이 높을 것으로 판단된다.

• 한국물환경학회지
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• 제25권5호
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• pp.778-784
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• 2009

The purpose of this study is to figure out the differences of the inflow rates of Sewage Treatment Plants (STP), invested by public fund or public-private partnership (PPP). This paper finds that the average ratio of sewage inflow according to facility capacities (medium and small scale STP) was either nearly below 30% or above 100% in the first year. As the size of STP increased, there was decrease in the accuracy of demand assumption. This was because the operation time when the ratio of sewage inflow was uniform was different according to the size of STP, whereby the time was short when the STP were small. The design average ratio of sewage inflow was 10% larger than the real average ratio; this was considered overdesigned. In the case of a plant built by the PPP scheme, the average ratio of inflow of the STP before an abolition of MRG was larger than after the abolition of MRG. This may be explained by moral hazard from too much reliance on MRG. After the abolition of MRG, the demand risk of PPP was shifted from a PPP project to a conventional project.

• Wind and Structures
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• 제13권6호
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• pp.557-580
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• 2010

A new inflow turbulence generation method and a combined dynamic SGS model recently developed by the authors were applied to evaluate the wind effects on 508 m high Taipei 101 Tower. Unlike the majority of the past studies on large eddy simulation (LES) of wind effects on tall buildings, the present numerical simulations were conducted for the full-scale tall building with Reynolds number greater than $10^8$. The inflow turbulent flow field was generated based on the new method called discretizing and synthesizing of random flow generation technique (DSRFG) with a prominent feature that the generated wind velocity fluctuations satisfy any target spectrum and target profiles of turbulence intensity and turbulence integral length scale. The new dynamic SGS model takes both advantages of one-equation SGS model and a dynamic production term without test-filtering operation, which is particular suitable to relative coarse grid situations and high Reynolds number flows. The results of comparative investigations with and without generation of inflow turbulence show that: (1) proper simulation of an inflow turbulent field is essential in accurate evaluation of dynamic wind loads on a tall building and the prescribed inflow turbulence characteristics can be adequately imposed on the inflow boundary by the DSRFG method; (2) the DSRFG can generate a large number of random vortex-like patterns in oncoming flow, leading to good agreements of both mean and dynamic forces with wind tunnel test results; (3) The dynamic mechanism of the adopted SGS model behaves adequately in the present LES and its integration with the DSRFG technique can provide satisfactory predictions of the wind effects on the super-tall building.

• 한국방재학회 논문집
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• 제2권4호
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• pp.123-129
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• 2002

본 연구에서는 3층 신경망 모형에 의해 충주호의 유입량을 예측한 결과들을 이용하여 신경망 모형의 저수지 유입량 예측 특성을 분석하였다. 신경망 모형의 적절한 입력층 및 은닉층 뉴런 개수, 학습회수를 제시하였으며, 학습 첨두유량 크기가 예측된 첨두유량보다 작을 경우 예측 값이 과소평가되는 특징을 확인하였다. 또한 뉴런 개수, 학습회수가 과다할 경우 발생 가능한 과적합 현상을 확인하였으며, 정확한 예측을 위해 필요한 최소 학습자료 기간도 제시하였다. 결과적으로 충주호의 경우 $8{\sim}10$개의 뉴런 개수 및 $1500{\sim}3000$회의 학습회수를 이용한 신경망 모형이 적합한 것으로, 학습자료 기간 수는 최소한 600개 이상의 자료를 적용하여야 정확한 예측이 가능한 것으로 결과되었다.

• 한국수자원학회논문집
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• 제55권7호
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• pp.495-504
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• 2022

최근 인공지능의 발전으로 시계열 자료 분석에 효과적인 Long Short-Term Memory (LSTM) 모델이 댐유입량 예측의 정확도를 높이는 데 활용되고 있다. 본 연구에서는 그 중 LSTM의 성능을 더욱 향상할 수 있는 Sequence-to-Sequence (s2s) 구조에 Attention 기법을 LSTM 모델에 첨가하여 소양강댐 유역의 유입량을 예측하였다. 분석 데이터는 2013년부터 2020년까지의 유입량 시자료와 종관기상관측기온 및 강수량 자료를 학습, 검증, 평가로 나누어 훈련한 후, 모델의 성능 평가를 진행하였다. 분석 결과, LSTM-s2s 모델보다 attention까지 첨가한 모델이 일반적으로 더 좋은 성능을 보였으며, attention 첨가 모델이 첨두값도 더 잘 예측하는 모습을 보였다. 그리고 두 모델 모두 첨두값 발생 동안 유량 패턴을 잘 반영하였지만 세밀한 시간 단위 변화량에는 어려움이 있었다. 이를 통해 시간 단위 예측의 어려움에도 불구하고, LSTM-s2s에 attention까지 첨가한 모델이 기존 LSTM-s2s의 예측 성능을 향상할 수 있음을 알 수 있었다.