Changhoon Lee 교수

A new adaptive controller based on a neural network was constructed and applied to turbulent channel flow for drag reduction. A simple control network, which employs blowing and suction at the wall based only on the wall-shear stresses in the spanwise direction, was shown to reduce the skin friction by as much as 20% in direct numerical simulations of a low-Reynolds number turbulent channel flow. Also, a stable pattern was observed in the distribution of weights associated with the neural networ

Two simple feedback control laws for drag reduction are derived by applying a suboptimal control theory to a turbulent channel flow. These new feedback control laws require pressure or shear-stress information only at the wall, and when applied to a turbulent channel flow at Re τ =110, they result in 16–22% reduction in the skin-friction drag. More practical control laws requiring only the local distribution of the wall pressure or one component of the wall shear stress are also derived and are

Inflammation is a normal physiological response to an infection or injury, such as aggression by microbes, trauma, or heat and radiation. Inflammation works to maintain homeostasis and is a highly regulated process with both pro- and anti-inflammatory components to ensure the prompt resolution of noxious conditions. In the initial stages of inflammation, macrophages destroy the abnormal stimuli, and remove the apoptotic bodies of the dead neutrophils as well as any remaining hazard factor. The m

We investigate the possibility of manipulating turbulence structures in the viscous sublayer for the purpose of drag reduction using a direct numerical simulation of a turbulent channel flow. Recognizing that a great portion of production of vorticity occurs in the viscous sublayer, a body force is used to suppress spanwise velocity in the sublayer, and a significant amount of drag reduction is obtained. A more realistic body force or wall movement in the spanwise direction using instantaneous w

Using direct numerical simulation of a fully developed turbulent channel flow, we investigate the behavior of acceleration near a solid wall. We find that acceleration near the wall is highly intermittent and the intermittency is in large part associated with the near wall organized coherent turbulence structures. We also find that acceleration of large magnitude is mostly directed towards the rotation axis of the coherent vortical structures, indicating that the source of the intermittent accel

The development of a reliable subgrid-scale (SGS) model for large-eddy simulation (LES) is of great importance for many scientific and engineering applications. Recently, deep learning approaches have been tested for this purpose using high-fidelity data such as direct numerical simulation (DNS) in a supervised learning process. However, such data are generally not available in practice. Deep reinforcement learning (DRL) using only limited target statistics can be an alternative algorithm in whi

Inspired by the recent finding by Min et al. [J. Fluid Mech. 558, 309 (2006)], the stability of a channel flow subject to wall blowing and suction in the form of a traveling wave is investigated by combined use of the Floquet analysis, direct numerical simulation, and singular value decomposition analysis. Results show that stability highly depends on the phase speed of the traveling wave; most disturbances become highly unstable when the phase speed is around 40% of the centerline velocity, whi

We performed two-way coupled direct numerical simulations of turbulent channel flow with Lagrangian tracking of small, heavy spheres at a dimensionless gravitational acceleration of 0.077 in wall units, which is based on the flow condition in the experiment by Gerashchenko et al. ( J. Fluid Mech. , vol. 617, 2008, pp. 255–281). We removed deposited particles after several collisions with the lower wall and then released new particles near the upper wall to observe direct interactions between par