The downwash flow field of the multi-rotor unmanned aerial vehicle (UAV), formed by propellers during operation, has a significant influence on the deposition, drift and distribution of droplets as well as the spray width of the UAV for plant protection. To study the general characteristics of the distribution of the downwash airflow and simulate the static wind field of multi-rotor UAVs in hovering state, a 3D full-size physical model of JF01-10 six-rotor plant protection UAV was constructed using SolidWorks. The entire flow field surrounding the UAV and the rotation flow fields around the six rotors were established in UG software. The physical model and flow fields were meshed using unstructured tetrahedral elements in ANSYS software. Finally, the downwash flow field of UAV was simulated. With an increased hovering height, the ground effect was reduced and the minimum current velocity increased initially and then decreased. In addition, the spatial proportion of the turbulence occupied decreased. Furthermore, the appropriate operational hovering height for the JF01-10 is considered to be 3 m. These results can be applied to six-rotor plant protection UAVs employed in pesticide spraying and spray width detection.

Currently, Computational Fluid Dynamics (CFD) has been used to investigate agricultural UAV downwash. However, the validations of CFD models are difficult to deal with. Current verification methods are to use either water-sensitive papers or wind-speed arrays, which could get wind distribution or speed only. In this study, model migration was used to develop and verify downwash CFD models. The basic idea is to try to use the results of a scaled-down drone to represent that of a real-used UAV. The CFD models of both a real-used six-rotor UAV, JF01-10, and a 1:10 scaled-down small drone were developed by ANSYS. Then, the scaled-down drone was utilized to conduct trials by particle image velocimetry (PIV), so that not only distribution and speed but also flowing direction of downwash could be obtained. Results indicated the relative error between the PIV tests and the CFD models of the small UAV was less than 12%, while that between the tests and the CFD models of JF01-10 was less than 34%. It could be indicated that model migration could reflect multiple downwash characteristics but should be optimized in some complex details. This study was a preliminary but fundamental attempt to investigate CFD modelling and validation of agricultural UAVs and provided a novel thinking of downwash verification.

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Abstract.Moisture is one of the most important factors affecting grain quality in storage. The grain must be dried as soon as possible after harvesting to lower moisture to a standard level. It is difficult to obtain satisfactory measurement effect on precision in capacitive grain's moisture measurement due to many influencing factors, such as temperature, species and weight. The data confusion method of Radial Basis Function nerve network is adopted with improved hardware of the measurement system. Tests show that the precision in moisture measurement of wheat has been improved.

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