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Author

Donghyun You

Bio: Donghyun You is an academic researcher from Pohang University of Science and Technology. The author has contributed to research in topics: Large eddy simulation & Turbulence. The author has an hindex of 25, co-authored 102 publications receiving 2235 citations. Previous affiliations of Donghyun You include Seoul National University & Yonsei University.


Papers
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Journal ArticleDOI
TL;DR: Park et al. as mentioned in this paper proposed an improvement of the dynamic procedure for closure of the subgrid-scale eddy-viscosity model developed by Vreman [Phys. Fluids 18, 125109 (2006), which is especially suitable for large-eddy simulation in complex geometries.
Abstract: An improvement of the dynamic procedure of Park et al. [Phys. Fluids 18, 125109 (2006)] for closure of the subgrid-scale eddy-viscosity model developed by Vreman [Phys. Fluids 16, 3670 (2004)] is proposed. The model coefficient which is globally constant in space but varies in time is dynamically determined assuming the “global equilibrium” between the subgrid-scale dissipation and the viscous dissipation of which utilization was proposed by Park et al. Like the Vreman model with a fixed coefficient and the dynamic-coefficient model of Park et al., the present model predicts zero eddy-viscosity in regions where the vanishing eddy viscosity is theoretically expected. The present dynamic model is especially suitable for large-eddy simulation in complex geometries since it does not require any ad hoc spatial and temporal averaging or clipping of the model coefficient for numerical stabilization and more importantly, requires only a single-level test filter in contrast to the dynamic model of Park et al., whi...

177 citations

Journal ArticleDOI
TL;DR: In this paper, the authors use adversarial training to extract features of flow dynamics in an unsupervised manner and predict future flow fields at future occasions based on information on flow fields from previous occasions.
Abstract: Unsteady flow fields over a circular cylinder are used for training and then prediction using four different deep learning networks: generative adversarial networks with and without consideration of conservation laws; and convolutional neural networks with and without consideration of conservation laws. Flow fields at future occasions are predicted based on information on flow fields at previous occasions. Predictions of deep learning networks are made for flow fields at Reynolds numbers that were not used during training. Physical loss functions are proposed to explicitly provide information on conservation of mass and momentum to deep learning networks. An adversarial training is applied to extract features of flow dynamics in an unsupervised manner. Effects of the proposed physical loss functions and adversarial training on predicted results are analysed. Captured and missed flow physics from predictions are also analysed. Predicted flow fields using deep learning networks are in good agreement with flow fields computed by numerical simulations.

172 citations

Journal ArticleDOI
TL;DR: In this paper, the tip-leakage flow in a turbomachinery cascade is studied using large-eddy simulation with particular emphasis on understanding the underlying mechanisms for viscous losses in the vicinity of the tip gap.
Abstract: The tip-leakage flow in a turbomachinery cascade is studied using large-eddy simulation with particular emphasis on understanding the underlying mechanisms for viscous losses in the vicinity of the tip gap. Systematic and detailed analysis of the mean flow field and turbulence statistics has been made in a linear cascade with a moving endwall. Gross features of the tip-leakage vortex, tip-separation vortices, and blade wake have been revealed by investigating their revolutionary trajectories and mean velocity fields. The tip-leakage vortex is identified by regions of significant streamwise velocity deficit and high streamwise and pitchwise vorticity magnitudes. The tip-leakage vortex and the tip-leakage jet which is generated by the pressure difference between the pressure and suction sides of the blade tip are found to produce significant mean velocity gradients along the spanwise direction, leading to the production of vorticity and turbulent kinetic energy. The velocity gradients are the major causes for viscous losses in the cascade endwall region. The present analysis suggests that the endwall viscous losses can be alleviated by changing the direction of the tip-leakage flow such that the associated spanwise derivatives of the mean streamwise and pitchwise velocity components are reduced.

156 citations

Book ChapterDOI
TL;DR: In this article, a large-eddy simulation of turbulent flow separation over an airfoil and evaluate the effectiveness of synthetic jets as a separation control technique is performed and the results show that synthetic-jet actuation effectively delays the onset of flow separation and causes a significant increase in the lift coefficient.

139 citations

Journal ArticleDOI
TL;DR: By using explicit filtering in large-eddy simulation, turbulent statistics and energy spectra are shown to be independent of the mesh resolution used.
Abstract: The governing equations for large-eddy simulation are derived from the application of a low-pass filter to the Navier–Stokes equations. It is often assumed that discrete operations performed on a particular grid act as an implicit filter, causing results to be sensitive to the mesh resolution. Alternatively, explicit filtering separates the filtering operation, and hence the resolved turbulence, from the underlying mesh distribution alleviating some of the grid sensitivities. We investigate the use of explicit filtering in large-eddy simulation in order to obtain numerical solutions that are grid independent. The convergence of simulations using a fixed filter width with varying mesh resolutions to a true large-eddy simulation solution is analyzed for a turbulent channel flow at Reτ=180, 395, and 640. By using explicit filtering, turbulent statistics and energy spectra are shown to be independent of the mesh resolution used.

120 citations


Cited by
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Journal Article

1,306 citations

Journal ArticleDOI
TL;DR: A theoretical framework in which dynamic mode decomposition is defined as the eigendecomposition of an approximating linear operator, which generalizes DMD to a larger class of datasets, including nonsequential time series, and shows that under certain conditions, DMD is equivalent to LIM.
Abstract: Originally introduced in the fluid mechanics community, dynamic mode decomposition (DMD) has emerged as a powerful tool for analyzing the dynamics of nonlinear systems. However, existing DMD theory deals primarily with sequential time series for which the measurement dimension is much larger than the number of measurements taken. We present a theoretical framework in which we define DMD as the eigendecomposition of an approximating linear operator. This generalizes DMD to a larger class of datasets, including nonsequential time series. We demonstrate the utility of this approach by presenting novel sampling strategies that increase computational efficiency and mitigate the effects of noise, respectively. We also introduce the concept of linear consistency, which helps explain the potential pitfalls of applying DMD to rank-deficient datasets, illustrating with examples. Such computations are not considered in the existing literature, but can be understood using our more general framework. In addition, we show that our theory strengthens the connections between DMD and Koopman operator theory. It also establishes connections between DMD and other techniques, including the eigensystem realization algorithm (ERA), a system identification method, and linear inverse modeling (LIM), a method from climate science. We show that under certain conditions, DMD is equivalent to LIM.

1,067 citations

Journal ArticleDOI
TL;DR: A sharp interface immersed boundary method for simulating incompressible viscous flow past three-dimensional immersed bodies is described, with special emphasis on the immersed boundary treatment for stationary and moving boundaries.

1,013 citations

01 Nov 2002
TL;DR: An efficient ghost-cell immersed boundary method (GCIBM) for simulating turbulent flows in complex geometries is presented in this paper, where a boundary condition is enforced through a ghost cell method.
Abstract: An efficient ghost-cell immersed boundary method (GCIBM) for simulating turbulent flows in complex geometries is presented. A boundary condition is enforced through a ghost cell method. The reconstruction procedure allows systematic development of numerical schemes for treating the immersed boundary while preserving the overall second-order accuracy of the base solver. Both Dirichlet and Neumann boundary conditions can be treated. The current ghost cell treatment is both suitable for staggered and non-staggered Cartesian grids. The accuracy of the current method is validated using flow past a circular cylinder and large eddy simulation of turbulent flow over a wavy surface. Numerical results are compared with experimental data and boundary-fitted grid results. The method is further extended to an existing ocean model (MITGCM) to simulate geophysical flow over a three-dimensional bump. The method is easily implemented as evidenced by our use of several existing codes.

740 citations