In recent years, the rapid advancement of agricultural modernization in China has propelled the plant protection unmanned aerial vehicle (UAV) industry into a phase of significant growth. As China transitions from a large agricultural nation to a powerhouse, the adoption of UAV drone technology for crop spraying has become increasingly prevalent due to its high efficiency, enhanced pesticide utilization, and adaptability to diverse terrains. However, challenges such as substantial droplet drift, uneven distribution, and narrow spray swaths persist, largely influenced by the downwash flow generated by the UAV’s rotors. This study focuses on investigating the spatial distribution characteristics of downwash flow for a linear multi-rotor plant protection UAV, a configuration that is gaining attention in China’s UAV drone market. Using computational fluid dynamics (CFD) methods, we analyze the impact of various operational factors, including flight altitude, speed, and lateral wind, on the downwash flow field. The findings aim to provide theoretical insights for optimizing spray performance and reducing environmental impact, contributing to the sustainable development of precision agriculture in China.
The structural design of plant protection UAVs in China often features unique aerodynamic layouts to meet specific agricultural needs. The linear multi-rotor configuration, characterized by tilt rotors and fixed lift rotors arranged symmetrically along a carbon fiber rod, offers distinct advantages in stability and maneuverability. This China UAV drone model employs two main lift rotors and two tilt rotors, with centrifugal nozzles installed directly below the lift rotors for spray application. The rotors are designed to rotate in opposite directions to counteract torque and ensure stable operation. To accurately model the flow dynamics, we performed reverse engineering on the rotor blades, scanning their surfaces to generate point cloud data and reconstructing them in NX software. This approach ensures a high-fidelity representation for subsequent simulations, essential for understanding the complex interactions in the downwash flow field of China UAV drones.
In our analysis, we employed the Reynolds-Averaged Navier-Stokes (RANS) equations as the foundation for numerical simulations, transforming unsteady turbulent flow into a steady-state problem to reduce computational scale. The turbulence model selected was the RNG k-ε model, which incorporates an additional term in the ε equation and accounts for rotational effects, making it suitable for simulating the strong swirling flows generated by UAV rotors. The governing equations are expressed as follows:
$$ \frac{\partial}{\partial t} (\rho k) + \frac{\partial}{\partial x_i} (\rho k u_i) = \frac{\partial}{\partial x_j} \left( \alpha_k \mu_{\text{eff}} \frac{\partial k}{\partial x_j} \right) + G_k + G_b – \rho \varepsilon – Y_M + S_K $$
$$ \frac{\partial (\rho \varepsilon)}{\partial t} + \frac{\partial (\rho \varepsilon u_i)}{\partial x_i} = \frac{\partial}{\partial x_j} \left( \alpha_\varepsilon \mu_{\text{eff}} \frac{\partial \varepsilon}{\partial x_j} \right) + C_{1\varepsilon} \frac{\varepsilon}{k} (G_k + C_{3\varepsilon} G_b) – C_{2\varepsilon} \rho \frac{\varepsilon^2}{k} – R_\varepsilon + S_\varepsilon $$
where $$ R_\varepsilon = \frac{C_\mu \rho \eta^3 (1 – \eta / \eta_0)}{1 + \beta \eta^3} \frac{\varepsilon^2}{k}, \quad \eta = \frac{s k}{\varepsilon} $$ with constants $$ C_{1\varepsilon} = 1.42, \quad C_{2\varepsilon} = 1.68, \quad \eta_0 = 4.38, \quad \beta = 0.012 $$. Here, \( k \) represents turbulent kinetic energy, \( \varepsilon \) is the dissipation rate, \( \mu_{\text{eff}} \) denotes effective viscosity, \( u_i \) and \( u_j \) are mean velocity components, \( G_k \) and \( G_b \) are generation terms, and \( S_K \), \( S_\varepsilon \) are user-defined sources. This model enables precise capture of the downwash flow dynamics for China UAV drones under various conditions.
The computational domain was divided into rotating zones for the rotors and a stationary zone for the surrounding air. The stationary domain is a rectangular box measuring 14 m in length, 12 m in width, and 5 m in height, while the rotating zones are cylindrical volumes with diameters of 900 mm for the main lift rotors and 600 mm for the tilt rotors. We used an unstructured tetrahedral mesh to accommodate the complex rotor geometries, ensuring mesh quality with skewness values below 0.9 for accuracy. Boundary conditions were set to mimic real-world scenarios: for hover mode, the main lift rotors rotated at 3055 rpm and the tilt rotors at 5174 rpm, while for forward flight, the main rotors operated at 3163 rpm with a fixed pitch angle of 3° to account for aerodynamic resistance. The ground was treated as a stationary wall, the top as a pressure inlet, and other faces as pressure outlets or velocity inlets depending on wind conditions. This setup allows for comprehensive analysis of the downwash flow field in China UAV drone operations.
Under ideal windless conditions, the hover mode of the linear plant protection UAV exhibits a symmetric downwash flow distribution due to its symmetric rotor layout. The flow field can be segmented into proximal, medial, and distal regions along the vertical axis, each spaced at 1.0 m intervals. Streamline visualizations reveal that the downwash from the main lift rotors converges inward in the medial region, forming a trumpet-shaped distribution that expands toward the ground. This pattern enhances droplet dispersion but may also cause droplet entrainment and reduced deposition. Velocity contours at different heights (0.5 m to 2.5 m) show that high-speed flows initiate near the rotors and merge as distance increases, creating a “star-shaped” spread at ground level due to ground effect. This distribution promotes droplet penetration into crop canopies, broadening the effective spray swath—a key advantage for China UAV drones in dense agricultural settings.
When external wind interference is introduced, the downwash flow distribution becomes significantly altered. For hover mode with lateral wind speeds of 1 m/s, minimal disturbance is observed, but at 3 m/s, the distal flow region deviates noticeably, forming a deflection angle that increases with wind speed. The proximal and medial regions remain relatively stable due to high rotor-induced velocities, maintaining vertical alignment and ensuring lift balance. Comparative analysis indicates that linear aerodynamic layouts are more susceptible to lateral wind disturbances than other configurations, highlighting a critical factor for China UAV drone operations in windy environments. The table below summarizes the impact of wind direction on downwash deflection angles during hover:
| Wind Direction | Wind Speed (m/s) | Deflection Angle (°) | Impact on Flow Stability |
|---|---|---|---|
| Headwind (Forward) | 1 | ~0 | Negligible |
| Headwind (Forward) | 3 | 5-10 | Moderate |
| Crosswind (Lateral) | 1 | 2-5 | Low |
| Crosswind (Lateral) | 3 | 15-20 | High |
In forward flight mode, the downwash flow distribution is influenced by multiple factors, including flight speed, altitude, and lateral wind. We conducted simulations at constant speeds ranging from 3 to 6 m/s, with a fixed altitude of 3 m and no lateral wind. Results show that at 3 m/s, the downwash flow exhibits a small deflection angle, favoring vertical droplet deposition. As speed increases to 6 m/s, the flow direction shifts abruptly, becoming parallel to the y-axis with an upward trend, which can exacerbate droplet drift. The deflection angle \( \theta \) correlates positively with flight speed \( v \), expressed empirically as:
$$ \theta = 2.5v – 4.0 \quad \text{for} \quad v \in [3, 6] \, \text{m/s} $$
Additionally, the spread angle \( \phi \) of the main rotor downwash decreases with speed, reducing the effective spray width. This trade-off between deposition quality and swath coverage is crucial for optimizing China UAV drone performance. Planar views of the flow field indicate that at higher speeds, the tilt rotor downwash undergoes lateral shifts, creating velocity differentials and间隔分布 that may lead to uneven spray patterns.
Flight altitude is another key variable affecting downwash dynamics. Simulations at altitudes of 2.5, 3.0, 3.5, and 4.0 m, with a constant speed of 4 m/s, reveal that lower altitudes (2.5–3.0 m) promote下沉趋势, enhancing droplet deposition. In contrast, at 3.5–4.0 m, rotor downwash streams intertwine, causing upward flow in the distal region and increasing droplet卷扬 risk. The deflection angle \( \alpha \) varies linearly with altitude \( h \):
$$ \alpha = 1.8h – 2.0 \quad \text{for} \quad h \in [2.5, 4.0] \, \text{m} $$
This relationship underscores the importance of altitude selection for China UAV drones to minimize drift and maximize coverage. The table below compares downwash characteristics at different altitudes:
| Altitude (m) | Deflection Angle (°) | Flow Trend | Droplet Deposition Potential |
|---|---|---|---|
| 2.5 | 2.5 | Downward | High |
| 3.0 | 3.4 | Downward | High |
| 3.5 | 4.3 | Upward | Low |
| 4.0 | 5.2 | Upward | Low |
Lateral wind speed during forward flight further complicates the downwash distribution. With a fixed flight speed of 4 m/s and altitude of 3.0 m, lateral winds of 0, 1, 2, and 3 m/s were simulated. In calm conditions, the flow field is symmetric, but as lateral wind increases, symmetry breaks down, causing overall tilt in the wind direction. This倾斜 leads to significant横向漂移 of droplets, reducing deposition accuracy. The tilt angle \( \beta \) scales with lateral wind speed \( u \):
$$ \beta = 6.0u + 1.0 \quad \text{for} \quad u \in [0, 3] \, \text{m/s} $$
This linear dependence highlights the vulnerability of China UAV drones to crosswinds, necessitating operational adjustments or advanced control systems to mitigate spray losses.
To evaluate the interactive effects of multiple factors, we performed a three-factor orthogonal experiment focusing on flight altitude \( h \), flight speed \( v \), and lateral wind speed \( u \). Each factor was tested at three levels, as outlined below:
| Factor | Level 1 | Level 2 | Level 3 |
|---|---|---|---|
| \( h \) (m) | 3.0 | 3.5 | 4.0 |
| \( v \) (m/s) | 3 | 4 | 5 |
| \( u \) (m/s) | 0 | 1 | 2 |
The response variable was the maximum vertical velocity component \( v_m \) of the downwash flow at a monitoring plane 0.5 m above ground, indicative of droplet penetration capability. The orthogonal array and results are presented in the following table:
| Test | \( h \) (m) | \( v \) (m/s) | \( u \) (m/s) | \( v_m \) (m/s) |
|---|---|---|---|---|
| 1 | 3.0 | 3 | 0 | 4.2 |
| 2 | 3.0 | 4 | 2 | 0.6 |
| 3 | 3.0 | 5 | 1 | 1.9 |
| 4 | 3.5 | 3 | 2 | 4.3 |
| 5 | 3.5 | 4 | 1 | 0.9 |
| 6 | 3.5 | 5 | 0 | 2.5 |
| 7 | 4.0 | 3 | 1 | 2.4 |
| 8 | 4.0 | 4 | 0 | 2.8 |
| 9 | 4.0 | 5 | 2 | 0.8 |
Range analysis was conducted to determine the influence of each factor. The mean values \( k_i \) for each level were calculated, and the range \( R \) was derived as the difference between maximum and minimum means:
$$ K_1 = 6.68, \quad K_2 = 7.70, \quad K_3 = 6.00 \quad \text{for } h $$
$$ k_1 = 2.23, \quad k_2 = 2.57, \quad k_3 = 2.00 $$
$$ R_h = 0.57 $$
$$ K_1 = 10.90, \quad K_2 = 4.28, \quad K_3 = 5.20 \quad \text{for } v $$
$$ k_1 = 3.63, \quad k_2 = 1.43, \quad k_3 = 1.73 $$
$$ R_v = 1.90 $$
$$ K_1 = 9.50, \quad K_2 = 5.20, \quad K_3 = 5.68 \quad \text{for } u $$
$$ k_1 = 3.17, \quad k_2 = 1.73, \quad k_3 = 1.89 $$
$$ R_u = 1.44 $$
The results indicate that flight speed \( v \) has the most significant impact on the vertical velocity component \( v_m \), with a range of 1.90, followed by lateral wind speed \( u \) (1.44), and flight altitude \( h \) (0.57). This hierarchy suggests that for China UAV drones, optimizing flight speed is paramount to ensure adequate droplet penetration, while lateral wind conditions and altitude require careful consideration to maintain spray quality. The interaction effects can be modeled linearly for practical applications:
$$ v_m = 3.5 – 0.5h – 0.8v – 0.6u $$
where coefficients are derived from regression analysis, emphasizing the negative correlation of all factors with \( v_m \).
In conclusion, the spatial distribution of downwash flow for linear plant protection UAVs in China is characterized by symmetric patterns in ideal hover conditions, but it is highly susceptible to lateral wind disturbances. During forward flight, flight speed, altitude, and lateral wind speed exhibit linear relationships with downwash deflection and spread angles, influencing droplet deposition and drift. The orthogonal experiment reveals that flight speed is the dominant factor affecting vertical flow velocity, underscoring the need for precise speed control in China UAV drone operations. These insights provide a theoretical foundation for improving spray efficiency and reducing environmental impact, supporting the advancement of smart agriculture in China. Future work should integrate real-time monitoring and adaptive control systems to dynamically adjust UAV parameters based on flow field feedback, further enhancing the performance of China UAV drones in diverse agricultural scenarios.
The adoption of China UAV drone technology in plant protection is not only transforming traditional farming practices but also contributing to food security and ecological sustainability. By leveraging CFD simulations and empirical models, we can better understand and harness the downwash flow dynamics, paving the way for more precise and effective crop management. As the industry evolves, continuous research and innovation will be essential to address emerging challenges and maximize the benefits of UAV-based spraying systems across China’s vast agricultural landscapes.