35 citations · 37 across the 7 of their papers we have counts for
8 papers
Relative velocity between a particle and turbulence
Yutaro Motoori, Daiki Watanabe, Susumu Goto
The relative velocity between a particle and the surrounding fluid is a key quantity that determines the various phenomena of particle-laden turbulence, such as particle clustering…
Optimization of fluid mixing by reinforcement learning using limit cycles of a dynamical system
Masaki Shimizu, Daiki Watanabe, Takumi Aoyagi +1
We propose a method to overcome the difficulties encountered when applying reinforcement learning to fluid mixing processes. The proposed method has two main features: (i) it does…
Physical mechanism of turbulence attenuation by polymers from a timescale perspective
Hayato Masuda, Yusuke Koide, Yutaro Motoori +1
To elucidate the physical mechanism of turbulence attenuation by polymers at each scale, we conduct direct numerical simulations of homogeneous isotropic turbulence in dilute polym…
Lagrangian velocity statistics of homogeneous isotropic turbulence in dilute polymer solutions
Yusuke Koide, Susumu Goto
We conduct direct numerical simulations of homogeneous isotropic turbulence in dilute polymer solutions to investigate the Lagrangian velocity statistics. We show how polymers modu…
Multifractal sets of coherent and incoherent vortices in turbulence
Susumu Goto, Daiki Watanabe, Tsuyoshi Yoneda
We numerically verify multifractal theory (Frisch and Parisi 1985) for turbulence using simulation data at a high Reynolds number. First, we propose a simple method to directly est…
Relationship between the power spectral density of the Lagrangian velocity and the hierarchy of coherent vortices in turbulence
Yusuke Koide, Susumu Goto
We conduct direct numerical simulations of developed turbulence in a periodic cube to investigate the formation mechanism of the power spectral density of the Lagrangian velocity.…
Attenuation mechanism of wall-bounded turbulence by heavy finite-size particles
Yutaro Motoori, Susumu Goto
To elucidate the attenuation mechanism of wall-bounded turbulence due to heavy small particles, we conduct direct numerical simulations (DNS) of turbulent channel flow laden with f…
Data-driven nonlinear turbulent flow scaling with Buckingham Pi variables
Kai Fukami, Susumu Goto, Kunihiko Taira
Nonlinear machine learning for turbulent flows can exhibit robust performance even outside the range of training data. This is achieved when machine-learning models can accommodate…