The wakes behind a square cylinder were simulated using two-equation turbulence models, k-ε and RNG k-ε models. For comparisons between the model predictions and analytical solutions, we employed three skill assessments:, the correlation coefficient for the similarity of the wake shape, the error of maximum velocity difference (EMVD) of the accuracy of wake velocity, and the ratio of drag coefficient (RDC) for the flow patterns as in the authors’ previous study. On the basis of the calculated results, we discussed the feasibility of each model for wake simulation and suggested a suitable value for an eddy viscosity related constant in each turbulence model. The k-ε model underestimated the drag coefficient by over 40 %, and its performance was worse than that in the previous study with one-equation and mixing length models, resulting from the empirical constants in the ε-equation. In the RNG k-ε model experiments, when an eddy viscosity related constant was six times higher than the suggested value, the model results were yielded good predictions compared with the analytical solutions. Then, the values of EMVD and RDC were 3.8 % and 3.2 %, respectively. The results of the turbulence model simulations indicated that the RNG k-ε model results successfully represented wakes behind the square cylinder, and the mean error for all skill assessments was less than 4 %.

Air pollution dispersion from rooftop emissions around hexahedron buildings was investigated using computational fluid dynamics (referred to hereafter as CFD). The Shear Stress Transport (referred to hereafter as SST) k-ω model in FLUENT CFD code was used to simulate the flow and pollution dispersion around the hexahedron buildings. The two buildings used in the study had the dimensions of H: L: W (where H = height, L = length, and W = width) with the ratios of 1:1:1 and 1:1:2. Experimental data from the wind tunnel obtained by a previous study was used to validate the numerical result of the hexahedron building. Five validation metrics are used to obtain an overall and quantitative evaluation of the performance of SST k-ω models: the fractional bias (FB), the geometric mean bias (MG), the normalized mean square errors (NMSE), the geometric variance (VG), and the factor of 2 of the observations (FAC2). The results of vertical concentration profile and longitudinal surface concentration of the 1:1:2 building illustrate the reasonable performance for all five metrics. However, the lateral concentration profile at X = 3H (where X is the distance from the source) shows poor performance for all of the metrics with the exception of NMSE, and the lateral concentration profile at X = 10H shows poor performance for FB and MG.

This paper aims to reveal the effects of the K- turbulence model on the performance analysis of battery cooling system for electric vehicle. The maximum temperature, the difference of temperature, and temperature distributions on the battery module were compared with and without K- turbulence model under the different flow rate. It can be expected that the maximum temperature of K- turbulence model is corrected by using the average error rate without the result of K- turbulence model.

This paper aims to reveal the effects of the K-ε turbulence model on the performance analysis of battery cooling system for electric vehicle. The maximum temperature, the difference of temperature, and temperature distributions on the battery module were compared with and without K- ε turbulence model under the different flow rate. It was found that there was no need to apply K-ε turbulence model when the flow rate is over 500m3/h because the difference of maximum temperature is under the 6℃.

This paper presents the performance of a CFD model for the near field dispersion of odor from rooftop emissions. The FLUENT Shear-Stress Transport (SST hereinafter) k-ω turbulence model was used to simulate odor dispersion from a rooftop odor vent. The results were compared with a wind tunnel experiment and the calculated results of ASHRAE 2003 and 2007. The FLUENT SST k-ω turbulence model provided good results for making reasonable predictions about the building rooftop surface normalized dilution. It was found that increasing the vent height (from 1 m to 7 m) reduces rooftop surface normalized dilution. ASHRAE 2003 and ASHRAE 2007 performance measures are generally not as good as FLUENT SST k-ω turbulence model performance measures, with larger MG (the geometric mean bias, VG (the geometric variance), NMSE (the normalized mean square error), FB (Fractional bias), and smaller FAC2 (the fraction of predictions within a factor of two of observations).

In this study we investigated odor (hydrogen sulfide) dispersion around a cubic building by using commercial FLUENT CFD code. The FLUENT Shear-Stress Transport (hereafter SST) k-ω turbulence model was used to simulate odor dispersion from an odor source. The results were compared with a wind tunnel experiment and other simulation results. SST k-ω turbulence model provided good grounds for making reasonable predictions about the building surface concentrations and concentration profiles of selected leeward positions of the cubic building. It was found that a vent, which was positioned 7 m above the top of the square building center, decreased the plume length lower by 0.73 and increased the plume height by 1.43 compared to roof top vents. It was also found that by increasing the vent height there a corresponding decrease in the maximum dimensionless concentration around the roof surface.

This study describes the amendment of Durbin's k-∈-v2-f model and its application to turbulent channel flow to test the model’s performance. Modeling redistribution and dissipation rate terms for the scalar v2 transport equation is considered by the elliptic blending equation which is used in the second moment closure generally. The prediction results are directly compared to the DNS and Durbin's original k-∈-v2-f model to assess the performance of the new model predictions and to show their reasonable agreement with the DNS and Durbin's model for all the flow characteristics that are analyzed for the present study.

본 연구는 전산유체역학을 이용하여 균질한 중립 상태에 있는 대기층에서 발생되는 바람의 특성을 재현하는 것이다. 이를 판단하기 위하여 표준 k-ε난류모델을 이용하여 해석 영역을 통과하는 기류가 입력한 특성으로부터 어떻게 변화되는지 살펴본다. 네 가지 지표조도에서 정의된 KBC-2009 기준의 멱지수 형식인 평균 풍속과 회귀 분석으로 결정한 자연로그 형식의 평균 풍속을 적용하였다. 기준을 이용한 난류 운동에너지 k 및 소산률 ε을 표준 모델로부터 유도한 근사해를 이용하여 풍속과 상응하게 입력하였다. 표준 k-ε 난류모델에서 3개의 상수와 지표 경계조건 등을 지표조도에 따라 변화시켰다. 제안된 두 형식으로부터 큰 차이 없이 기준의 기류 특성들은 CFD에서 적절히 재현되었다. 로그 형식의 입력이 멱지수 형식에 비교하여 입력 성질이 약간 더 효과적으로 유지되었다. 부드러운 지표조도일수록 기류의 특성이 효과적으로 재현되었다. 지표 경계에 접한 첫 번째 유체요소 안에 적절한 지표조도를 반영한 경계조건이 필수적이었다.

A study of fracture to material is getting interest in nuclear and aerospace industry as a viewpoint of safety. Acoustic emission (AE) is a non-destructive testing and new technology to evaluate safety on structures. In previous research continuously, all tensile tests on the pre-defected coupons were performed using the universal testing machine, which machine crosshead was move at a constant speed of 5mm/min. This study is to evaluate an AE source characterization of SM45C steel by using k-nearest neighbor classifier, k-NNC. For this, we used K-means clustering as an unsupervised learning method for obtained multi -variate AE main data sets, and we applied k-NNC as a supervised learning pattern recognition algorithm for obtained multi-variate AE working data sets. As a result, the criteria of Wilk's λ, D&B(Rij) & Tou are discussed.

본 연구에서는 중층 밀도류를 모의할 수 있는 k-ε 난류모형의 지배방정식을 제시하고 수치모의를 수행하였다. 깊은 수체에 모형을 적용하여 중층 밀도류를 모의하고 게산된 유속과 초과밀도 분포를 분석하였다. 밀도류의 주 흐름방향을 따라 물 연행으로 인해 유속이 감소되는 것과 Richardson 수의 증가로 인해 유속 변화율이 감소되는 것을 관찰하였다. 유속과 초과밀도의 유사성을 확인하였으나, 난류운동에너지와 소산율의 유사성에서는 보이지 않았다. k-ε 모형의 모의 결과를 이용하여 중층 밀도류의 층적분 모형에서 사용될 수 있는 형상계수를 계산하였다. 또한, 층적분 모형을 이용하여 k-ε 모형에서 사용되는 부력관련 모형상수 (c₃ɛ)와 부피팽창계수 (β0)를 계산하였다.

본 연구는 난류현상의 모형화를 위해 널리 이용되는 k-과 k- 난류모형을 비교하는 것이 목적으로, 횡방향 흐름이 무시될 수 있는 U-튜브 모양의 터널형 수로 내 높은 레이놀즈수를 가진 진동 경계층 흐름에 두 난류해석방법을 적용하였다. 난류모형의 적용은 1차원 연직 모형을 통해 이루어지며, 수치 모의 결과, 유속의 분포와 난류운동에너지 (turbulent kinetic energy) 모두에서 두 모형이 매우 유사한 결과를 나타낸다. 이를 통하여, 횡방향