Koji Fukagata

A simple expression is derived of the componential contributions that different dynamical effects make to the frictional drag in turbulent channel, pipe and plane boundary layer flows. The local skin friction can be decomposed into four parts, i.e., laminar, turbulent, inhomogeneous and transient components, the second of which is a weighted integral of the Reynolds stress distribution. It is reconfirmed that the near-wall Reynolds stress is primarily important for the prediction and control of

We present a new nonlinear mode decomposition method to visualize decomposed flow fields, named the mode decomposing convolutional neural network autoencoder (MD-CNN-AE). The proposed method is applied to a flow around a circular cylinder at the Reynolds number as a test case. The flow attributes are mapped into two modes in the latent space and then these two modes are visualized in the physical space. Because the MD-CNN-AEs with nonlinear activation functions show lower reconstruction errors t

We present a theoretical prediction for the drag reduction rate achieved by superhydrophobic surfaces in a turbulent channel flow. The predicted drag reduction rate is in good agreement with results obtained from direct numerical simulations at Reτ≃180 and 400. The present theory suggests that large drag reduction is possible also at Reynolds numbers of practical interest (Reτ∼105–106) by employing a hydrophobic surface, which induces a slip length on the order of ten wall units or more.

We propose a method using supervised machine learning to estimate velocity fields from particle images having missing regions due to experimental limitations. As a first example, a velocity field around a square cylinder at the Reynolds number of ReD=300 is considered. To train machine learning models, we utilize artificial particle images (APIs) as the input data, which mimic the images of the particle image velocimetry (PIV). The output data are the velocity fields, and the correct answers for

Direct numerical simulation (DNS) of the channel flow with an anisotropic compliant surface is performed in order to investigate its drag reduction effect in a fully developed turbulent flow. The computational domain is set to be 3δ×2δ×3δ, where δ is the channel half-width. The surface is passively driven by the pressure and wall-shear stress fluctuations, and the surface velocity provides a boundary condition for the fluid velocity field. An evolutionary optimization method (CMA-ES) is used to

We review some fundamentals of turbulent drag reduction and the turbulent drag reduction techniques using streamwise traveling waves of blowing/suction from the wall and wall deformation. For both types of streamwise traveling wave controls, their significant drag reduction capabilities have been well confirmed by direct numerical simulation at relatively low Reynolds numbers. The drag reduction mechanisms by these streamwise traveling waves are considered to be the combination of direct effects

This paper extends the resolvent analysis of McKeon & Sharma ( J. Fluid Mech. , vol. 658, 2010, pp. 336–382) to elucidate the drag reduction mechanisms for the suboptimal control laws proposed by Lee, Kim & Choi ( J. Fluid Mech. , vol. 358, 1998, pp. 245–258). Under the resolvent formulation, the turbulent velocity field is expressed as a linear superposition of propagating modes identified via a gain-based decomposition of the Navier–Stokes equations. This decomposition enables targeted

An attempt is made for simultaneous, but independent control of skin friction reduction and heat transfer enhancement in a turbulent channel flow. First, a mathematical relation is derived of the contribution of turbulent heat flux to the Nusselt number. Two different thermal boundary conditions are considered: (1) isothermal condition, where two walls are kept at constant, but different temperatures; (2) isoflux condition, where both walls are heated at a given wall heat flux. By utilizing the

Friction drag reduction effect by superhydrophobic surfaces in a turbulent channel flow is investigated by means of direct numerical simulation. The simulations are performed under a constant pressure gradient at the friction Reynolds number of 180. A special focus is laid upon the influence of the angle of microridge structure to flow direction, while the gas area fraction on the surface is kept at 50% and the groove width is kept constant at 33.75 wall units. Larger drag reduction effect is ob