Dynamic Estimation of Formation Wake Flow Fields Based on On-Board Sensing

Highlights: What are the main findings? The PI-Model significantly improves the accuracy of wake velocity distribution estimation and formation aerodynamic effect prediction compared to theoretical models. In two-aircraft formations, the estimation error of the optimal position by the PI-Model remains within 1% of the wingspan, and the converged position of the algorithm enables a 15–25% drag reduction for the follower aircraft. What is the implication of the main finding? This work provides a practical method for wake dynamics estimation in complex multi-aircraft formations, offering a feasible and low-cost approach for achieving fast and adaptive energy-efficient formation flight. The proposed PI-Model demonstrates feasibility for formation flight applications and can be extended to larger heterogeneous mixed fleets. It holds significant theoretical and engineering value for improving the energy efficiency and mission performance of future UAV swarms. Close formation flight is a practical strategy for fixed-wing unmanned aerial vehicle (UAV) swarms. Maintaining UAVs at aerodynamically optimal positions is essential for efficient formation flight. However, aerodynamic optimization methods based on computational fluid dynamics (CFD) are computationally intensive and difficult to apply in real time for large-scale formations. Inspired by bio-formation flight, this study proposes an on-board sensing-based method for wake flow field estimation, with potential for extension to complex formations. The method is based on a parameter identification-induced velocity model (PI-Model), which uses only onboard sensors, including two lateral air data systems (ADS), to sample the wake field. By minimizing the residual of the induced velocity, the model identifies key parameters of the wake and provides a dynamic estimation of the wake velocity field. Comparisons between the PI-Model and CFD simulations show that it achieves higher accuracy than the widely used single horseshoe vortex model in both wake velocity and aerodynamic effects. Applied to a two-UAV formation scenario, CFD validation confirms that the trailing UAV achieves a 15–25% drag reduction. These results verify the effectiveness of the proposed method for formation flight and demonstrate its potential for application in complex, dynamic multi-UAV formations.