• Journal of the Korean Society of Propulsion Engineers
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• v.9 no.3
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• pp.49-59
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• 2005

Turbulent heat transfer over a fully-developed wavy channel is investigated by a turbulence model. The nonlinear k- f - f$_{ model of Park et at.[1] is slightly modified and their explicit algebraic heat flux model is employed. The Reynolds number is fixed at Re$_{b}$=6760 and the wave configuration is varied in the range of 0 $\leq$ $\alpha$/$\lambda$$\leq$0.15 and 0.25 $\leq$A/H$\leq$4.0. In order to verify model performances, a large eddy simulation is performed for the selected cases. The model performance is shown to be generally satisfactory. By using k- $\varepsilon$ - f$_{ model, the enhancement of heat transfer and the characteristics of turbulent flow in wavy wall are investigated. Finally, the influence of wavy configuration on heat transfer is scrutinized.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Journal of ILASS-Korea
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• v.11 no.1
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• pp.1-6
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• 2006

The present study is focused on the small scale turbulent mixing processes in the scalar Held. In order to deal with molecular mixing in turbulent flow, the linear eddy model is addressed. In each realization, the molecular mixing term is implemented deterministically, and turbulent stirring is represented by a sequence of instantaneous, statistically independent rearrangement event called by triplet map. The LEM approach is applied with relatively simple conditions. The characteristics of scalar mixing and PDF profiles are addressed in detail.

• Journal of Korean Society of Environmental Engineers
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• v.32 no.2
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• pp.165-174
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• 2010

The numerical modeling of a coal gasification reaction occurring in an entrained flow coal gasifier is presented in this study. The purposes of this study are to develop a reliable evaluation method of coal gasifier not only for the basic design but also further system operation optimization using a CFD(Computational Fluid Dynamics) method. The coal gasification reaction consists of a series of reaction processes such as water evaporation, coal devolatilization, heterogeneous char reactions, and coal-off gaseous reaction in two-phase, turbulent and radiation participating media. Both numerical and experimental studies are made for the 1.0 ton/day entrained flow coal gasifier installed in the Korea Institute of Energy Research (KIER). The comprehensive computer program in this study is made basically using commercial CFD program by implementing several subroutines necessary for gasification process, which include Eddy-Breakup model together with the harmonic mean approach for turbulent reaction. Further Lagrangian approach in particle trajectory is adopted with the consideration of turbulent effect caused by the non-linearity of drag force, etc. The program developed is successfully evaluated against experimental data such as profiles of temperature and gaseous species concentration together with the cold gas efficiency. Further intensive investigation has been made in terms of the size distribution of pulverized coal particle, the slurry concentration, and the design parameters of gasifier. These parameters considered in this study are compared and evaluated each other through the calculated syngas production rate and cold gas efficiency, appearing to directly affect gasification performance. Considering the complexity of entrained coal gasification, even if the results of this study looks physically reasonable and consistent in parametric study, more efforts of elaborating modeling together with the systematic evaluation against experimental data are necessary for the development of an reliable design tool using CFD method.