Anti-PIV type 1 Monoclonal antibody, Clone C293M

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.

The tip flows in modern gas turbines are primarily transonic under realistic conditions and significantly impact the overall thrust performance and safety of the turbines. This study is aimed at providing a deeper understanding of the mechanisms underlying and controlling the tip flow characteristics. Particle image velocimetry (PIV) and Schlieren and oil flow visualizations were performed to reveal the basic structure of the tip flow fields. A computational fluid dynamics model was developed, and the experimental results validated its accuracy. FLUENT 18.0 was employed to apply the Spalart-Allmaras turbulence model and perform two-dimensional calculations that furthered the investigation. The PIV and Schlieren visualization results indicated that the tip flow accelerated rapidly to the transonic level at the gap inlet separation when the gap pressure ratio exceeded 2.0. Furthermore, an oblique shock wave was generated when the transonic tip flow reattached and then reflected within the gap. The oil flow visualization provided the corresponding boundary layer behavior on the bottom wall. Additionally, the computation of the transonic tip flow with respect to various sizes and pressure values demonstrated that the Reynolds number is the key parameter that controls the gap flow field. The flow similarity existed as long as the Reynolds number remained constant. An in-depth analysis of the simulation improved the model performance at predicting the inlet separation size, discharge coefficient, and friction coefficient based on the Reynolds number. The study results provide a reference for the design and testing of engine blade gaps in real-world conditions.