This paper introduces a multi-fidelity computational framework for the analysis of aerodynamic performance of flight formation. The Vortex Lattice and Reynolds Averaged Navier–Stokes methods form the basis of the framework, as low- and high-fidelity, respectively. Initially, the computational framework is validated for an isolated wing, and then two rectangular NACA23012 wings are considered for assessing the aerodynamic performance of this formation; the optimal relative position is through the multi-fidelity framework based on the total drag reduction. The performance estimates are in good agreement with experimental measurements of the same configuration. Total aerodynamic performance of formation flight is also assessed with respect to attitude variations of the lifting bodies involved. The framework is also employed to determine the optimal position of blended-wing-body unmanned aerial vehicles in tandem formation flight.

This paper investigates aerodynamic performance improvements of formation flight at transonic speeds for a medium size Unmanned Aerial Vehicle (UAV). The metric for assessing the aerodynamic improvement of formation flight is the computed drag. The total drag for each formation configuration is compared with a single UAV, where a final drag reduction percentage is estimated. The evaluation of the aerodynamic performance is conducted by employing an in-house Computational Fluid Dynamics (CFD) solver, grid generation and post processing tools. For critical understanding of the tendency of the formation efficiency depending on main parameters, broad formation configurations are analysed. The parameterisation includes number of aircraft, proximity and formation shape. Full realisation of the benefit predicted would need to be proven in the real world, but there is sufficient confidence to suggest that it exist: the empirical parametric analysis suggests that formation flight can improves aerodynamic performance and formation configuration greatly influence the degree of improvement.

The flow field development through a simplified shock absorber orifice geometry is investigated using a single phase Large Eddy Simulation. Hydraulic oil is used as the working fluid with a constant inlet velocity and an open top boundary to allow the study to focus on the free shear layer and the flow development in the vicinity of the main orifice. The flow field is validated using standard mixing layer dynamics. The impact of the orifice shape is discussed with regards to the initial free shear layer growth, boundary layer development and the potential appearance of cavitation bubbles. Observations are made regarding the presence of flow field disturbances upstream of and through the orifice, thereby, leading to a notable turbulence intensity level in those regions.

A fully resolved (FR) NREL 5 MW turbine model is employed in two unsteady Reynolds-averaged Navier–Stokes (URANS) simulations (one with and one without the turbine tower) of a periodic atmospheric boundary layer (ABL) to study the performance of an infinitely large wind farm. The results show that the power reduction due to the tower drag is about 5% under the assumption that the driving force of the ABL is unchanged. Two additional simulations using an actuator disc (AD) model are also conducted. The AD and FR results show nearly identical tower-induced reductions of the wind speed above the wind farm, supporting the argument that the AD model is sufficient to predict the wind farm blockage effect. We also investigate the feasibility of performing delayed-detached-eddy simulations (DDES) using the same FR turbine model and periodic domain setup. The results show complex turbulent flow characteristics within the farm, such as the interaction of large-scale hairpin-like vortices with smaller-scale blade-tip vortices. The computational cost of the DDES required for a given number of rotor revolutions is found to be similar to the corresponding URANS simulation, but the sampling period required to obtain meaningful time-averaged results seems much longer due to the existence of long-timescale fluctuations.