Deep reinforcement learning (DRL) has been applied to a variety of problems during the
past decade and has provided effective control strategies in high-dimensional and non …

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 …

The increase in emissions associated with aviation requires deeper research into novel
sensing and flow-control strategies to obtain improved aerodynamic performances. In this …

Physics-informed neural networks (PINN) are machine-learning methods that have been
proved to be very successful and effective for solving governing equations of fluid flow. In …

Stereo particle imaging velocimetry measurements and reduced-order modeling are
combined to provide a full picture of the interaction of plasma jets with a turbulent boundary …

We present our approach to making direct numerical simulations of turbulence with
applications in sustainable shipping. We use modern Fortran and the spectral element …

For adverse-pressure-gradient turbulent boundary layers, the study of integral skin-friction
contributions still poses significant challenges. Beyond questions related to the integration …

The control efficacy of classical periodic forcing and deep reinforcement learning (DRL) is
assessed for a turbulent separation bubble (TSB) at Re τ= 180 on the upstream region …

We conducted high-resolution large-eddy simulations (LESs) to explore the effects of
opposition control (OC) on turbulent boundary layers (TBLs) over a wing at a chord-based …

Direct numerical simulations are conducted to gain insight into the blowing-based active
control in a spatially developing turbulent boundary layer at a low Reynolds number. The …