As skin-friction drag in wall turbulence has a considerable environmental impact, a feedback control technique is applied to provide the drag reduction effect. Specifically, opposition control, a feedback control technique, is used, which involves applying blowing and suction from the wall to cancel out turbulent vortical structures in the region near the wall. Opposition control typically involves the use of detected velocity immediately as the control input. However, in the present study, the near-wall velocity is predicted by an orbit-instability-based forecasting method based on deterministic chaos in low-Reynolds-number flow. We performed direct numerical simulations of turbulent channel flows to investigate the drag reduction performance. The maximum drag reduction rate was found to be comparable to that of the original opposition control. Although the drag reduction rate decreased slightly with increasing prediction time in the orbit-instability-based forecasting method, a positive drag reduction rate was achieved even when the prediction time was more than 100 times the time resolution of direct numerical simulation.

The forces acting on a delta wing are predominantly influenced by local flow structures, specifically leading-edge vortices (LEVs). This study applies colour-coded particle image velocimetry (PIV) to quantify wake dynamics and lift generation for a delta wing at varying angles of attack in wind tunnel experiments. Key contributions include an application of the novel colour-PIV technique through comparison with theoretical models and previous experimental data, and an in-depth analysis of vortex dynamics. Results reveal that LEVs exhibit maximum stability and strength at an angle of attack of 30°, corresponding to peak lift generation. These findings provide insights into the aerodynamic characteristics of delta wings and demonstrate the advantages of colour-PIV in experimental fluid mechanics.