This work demonstrated that the cooling strip upstream of the synchronization point stabilized the boundary layer by damping the disturbances. The longer cooling strip further stabilized the boundary layer.

Realistic wind data are essential in developing, testing, and ensuring the safety of small unmanned aerial systems in operation. We present a non-intrusive reduced order modeling (NIROM) approach to replicate realistic wind data and predict wind fields.

When pressurized with a fluid, the sweeping jet actuator (SWJA) emits a self-induced and self-sustained temporally continuous, but spatially oscillating bi-stable jet at the outlet. The results showed that external geometric variations at the nozzle exit had a negligible impact on the oscillation frequency. However, there were notable effects on the pressure and velocity distribution in the flow field, indicating that the actuator had sensitivity towards the geometric variation of the exit nozzle—the wider the exit nozzle, the lower the downstream velocity. Notably, we observed that the mean velocity at the exit nozzle downstream for the curvature case was 40.3% higher than the reference SWJA.

We use Large-Eddy Simulation to understand the unsteady and highly coherent turbulent flow structures produced by buildings in neutral atmospheric boundary layer flow. Furthermore, we demonstrate a non-intrusive machine learning methodology to predict flow fields to augment safe wind-aware navigation systems for Unmanned Aerial Vehicles as a first step toward safely integrating UAS into existing aerial infrastructure.

This paper adopts a recently introduced quantum algorithm for partial differential equations to solve Burgers’ equation and develops a quantum CFD solver. We used our quantum CFD solver to verify the quantum Burgers’ equation algorithm to find the flow solution when a shockwave is and is not present. We found excellent agreements for both cases, and the error of the quantum CFD solver was comparable to that of the classical CFD solver.