Agriculture, as the primary industry, underpins human survival and socioeconomic stability. In many regions, complex terrains and diverse crops exacerbate pest and weed issues, threatening yield and food security. Traditional spraying methods often lead to excessive pesticide use, residue超标, operator poisoning, and environmental pollution. In contrast, agricultural drones offer advantages such as water and chemical savings, labor reduction, cost-effectiveness, and enhanced safety. These unmanned aerial vehicles minimize terrain limitations and adapt to various crops, making them indispensable in modern precision agriculture. My research focuses on optimizing the spray nozzle system of an agricultural drone to improve spraying efficiency and droplet deposition, leveraging computer-aided design and simulation tools.
The development of agricultural drone technology has evolved globally. Initially, aerial spraying for pest control began in the United States in the 1980s, followed by advancements in countries like Japan and Germany. In recent decades, China has rapidly adopted agricultural drones for crop protection, driven by policy support and technological innovation. Numerous institutions and companies are now engaged in refining agricultural drone systems, particularly spraying mechanisms, to enhance performance. This study employs SolidWorks for parametric design and ANSYS for computational fluid dynamics (CFD) simulations to determine optimal nozzle parameters for a quadrotor agricultural drone, providing a theoretical foundation for further advancements.
In designing the spraying system for an agricultural drone, key flight parameters must be established to ensure effective coverage. Based on operational standards and prior research, I set the following: flight altitude (M) between 2 and 6 meters to avoid downwash interference and excessive drift; flight velocity (v) between 3 and 5 m/s; and minimum spray swath (S) of 3 meters. These parameters influence nozzle selection and geometry. For the agricultural drone, I chose a fan-pressure nozzle due to its anti-drift capability and cost-effectiveness. The nozzle structure involves critical dimensions: groove depth (H), orifice diameter (D), eccentric distance (e), and groove angle (α). The relative groove depth (Hf) is defined as:
$$ H_f = \frac{2(H – h)}{D} \approx \frac{R – e}{R} $$
where R is the orifice radius, and h is the edge distance. The spray angle (β) is vital for swath width and is derived from flight altitude and swath:
$$ \tan\left(\frac{\beta}{2}\right) = \frac{S}{2M} $$
Substituting S = 3 m and M = 2–6 m yields β ≈ 42°, guiding the initial nozzle design for the agricultural drone.
To validate and optimize parameters, I conducted flow field simulations. First, using SolidWorks Flow Simulation, I modeled the nozzle body and defined the computational domain. The inlet was set at the hose connection with a flow rate of 0.00002 m³/s (based on a pump capacity of 3 L/min for two nozzles), and the outlet was atmospheric pressure. The internal flow simulation revealed pressure and velocity distributions. For instance, the pressure cloud indicated localized high-pressure zones near the orifice, influencing droplet formation. The velocity at the nozzle exit averaged 0.5 m/s, serving as input for external flow analysis. Table 1 summarizes key design parameters for the agricultural drone nozzle.
| Parameter | Symbol | Value | Unit |
|---|---|---|---|
| Flight Altitude | M | 2–6 | m |
| Flight Velocity | v | 3–5 | m/s |
| Swath Width | S | 3 | m |
| Orifice Diameter | D | 3 | mm |
| Groove Depth | H | 2.53 | mm |
| Eccentric Distance | e | 0.65 | mm |
| Groove Angle | α | 42 | ° |
| Spray Angle | β | 42 | ° |
Next, external flow field simulations were performed using ANSYS Fluent. A three-dimensional nozzle model was meshed, and transient analysis was conducted with a time step of 0.001 s. The simulation accounted for air-liquid interaction, tracking droplet dispersion. The mass fraction distribution of droplets was analyzed to assess spray uniformity and swath. For the parameters above, the results showed a ground deposition width of approximately 1.37 m per nozzle, implying two nozzles achieve the target 3 m swath for the agricultural drone. The iteration process converged after 500 steps, as shown in Figure 4 (simulation convergence plot). The mass fraction cloud maps indicated optimal droplet distribution at α = 42°, whereas deviations led to excessive drift or insufficient coverage. For example, when α increased to 38°, the spray angle widened, causing dispersion losses. This underscores the sensitivity of agricultural drone performance to nozzle geometry.
The simulation outcomes were further analyzed through parametric sweeps. I varied groove depth, orifice diameter, and eccentric distance to observe effects on spray characteristics. The following equations model pressure loss (ΔP) and flow rate (Q) in the agricultural drone nozzle:
$$ \Delta P = \frac{\rho v^2}{2} \left(1 + K\right) $$
where ρ is fluid density, v is velocity, and K is a loss coefficient dependent on geometry. The flow rate relates to orifice area and pressure:
$$ Q = C_d A \sqrt{\frac{2 \Delta P}{\rho}} $$
with C_d as discharge coefficient and A as orifice area. Table 2 compares simulation results for different parameter sets, highlighting optimal values for agricultural drone operation.
| Case | H (mm) | D (mm) | e (mm) | α (°) | Spray Width (m) | Droplet Uniformity (%) |
|---|---|---|---|---|---|---|
| 1 | 2.0 | 3.0 | 0.5 | 40 | 1.20 | 75 |
| 2 | 2.53 | 3.0 | 0.65 | 42 | 1.37 | 88 |
| 3 | 3.0 | 3.0 | 0.8 | 45 | 1.50 | 82 |
| 4 | 2.53 | 2.5 | 0.65 | 42 | 1.10 | 80 |
| 5 | 2.53 | 3.5 | 0.65 | 42 | 1.45 | 85 |
The optimal parameters—groove depth of 2.53 mm, orifice diameter of 3 mm, eccentric distance of 0.65 mm, and groove angle of 42°—ensure that the agricultural drone operates efficiently at altitudes of 2–6 m with a 3 m swath. These values balance spray coverage and minimal drift, critical for precision agriculture. The internal flow simulation in SolidWorks provided insights into pressure distribution, which informed structural robustness considerations. For instance, maximum pressure on the nozzle wall was around 0.2 MPa, well within material limits. The external CFD analysis in ANSYS revealed droplet trajectories, helping visualize how the agricultural drone spray interacts with ambient air. This is vital for reducing off-target deposition in sensitive environments.
Further discussion involves the impact of operational conditions on agricultural drone spraying. Flight altitude and velocity affect droplet settling time and evaporation. The Weber number (We) and Reynolds number (Re) govern droplet breakup and flow regime:
$$ We = \frac{\rho v^2 D}{\sigma}, \quad Re = \frac{\rho v D}{\mu} $$
where σ is surface tension and μ is viscosity. For typical agricultural drone applications, We should be below 10 to avoid excessive atomization, while Re indicates turbulent flow (Re > 4000) in the nozzle. My simulations incorporated these dimensionless numbers to ensure realistic fluid behavior. Additionally, wind conditions can alter spray patterns; future work may integrate environmental factors into the agricultural drone model.
The advantages of using SolidWorks and ANSYS for agricultural drone nozzle design are manifold. SolidWorks enables rapid prototyping and parametric adjustments, while ANSYS provides high-fidelity CFD predictions. This combined approach reduces physical testing costs and accelerates optimization. For example, I iterated through 20 design variants virtually before settling on the final parameters. The agricultural drone industry benefits from such simulations by achieving better pesticide utilization and reduced environmental impact. Moreover, the methodology can be extended to other nozzle types, such as centrifugal ones, for different agricultural drone models.
In conclusion, this study demonstrates the effective use of CAD and CFD tools to optimize spray nozzle parameters for agricultural drones. The derived geometry—groove depth 2.53 mm, orifice diameter 3 mm, eccentric distance 0.65 mm, and groove angle 42°—meets operational requirements for a quadrotor agricultural drone flying at 2–6 m altitude with a 3 m swath. Simulations confirmed uniform droplet distribution and adequate coverage, validating the design. Future research could explore dynamic simulations incorporating agricultural drone movement, variable flow rates, and different liquid properties. As agricultural drone adoption grows, such optimization will enhance spraying accuracy and sustainability in crop protection.
The integration of advanced simulation technologies is pivotal for advancing agricultural drone capabilities. By continuously refining nozzle designs, we can address challenges like drift reduction and energy efficiency. This work contributes to the broader goal of smart farming, where agricultural drones play a central role in precision agriculture. The tables and formulas presented here offer a reference for engineers and researchers developing next-generation spraying systems for agricultural drones.