随机侧风影响下植保无人机下洗流场分布规律与预测模型研究

Distribution Patterns and Prediction Models of Downwash Airflow of Plant Protection Unmanned Aerial Vehicles under Random Crosswinds

摘要:

植保无人机田间作业过程中，随机变化的环境侧风与旋翼下洗流场的交互作用是影响喷洒雾滴沉积与飘移的重要因素。针对田间试验难以获得规律性研究成果，基于数值建模方法的模拟仿真无法准确描述田间速度场与空间分布且耗时耗力等问题，本文设计滤波高斯白噪声信号模拟田间随机变化的侧风信号，基于计算流体力学(Computational fluid dynamic，CFD)方法对随机侧风影响下植保无人机旋翼下洗流场进行数值模拟。引入一种余弦退火学习率的物理神经网络(Physics-informed neural networks，PINNs)下洗流场预测模型，该模型嵌入不可压缩方程N-S作为物理学损失项来参与训练，以低分辨率流场数据输入作为约束条件，实现高分辨率流场数据的复现，进而实现旋翼下洗流场任意时空位置速度信息的快速准确预测。结果表明：以气流覆盖面积为评价指标，旋翼下洗气流近似呈“圆柱形”向下发展1s内，气流覆盖面积达到峰值且具有较强抵抗随机侧风的能力；随着侧风风速增加，下洗气流结构和强度发生明显变化，当随机侧风大于3m/s时，气流覆盖面积和竖直方向最大速度大幅减小，不利于喷施作业。PINNs预测模型在水平方向和竖直方向速度预测值与试验值的总体拟合优度R2分别为0.971和0.919，均方根误差RMSE分别为0.364、0.253m/s，模型在预测速度信息方面表现出较高的准确性。基于该模型所获得的高精度流场信息，可进一步进行雾滴漂移趋势的评估，从而为喷雾作业效果分析提供物理依据。

Abstract:

In plant protection unmanned aerial vehicle (UAV) operations, the interaction between a random environmental crosswind and a rotor downwash flow notably influences spray droplet deposition and drift. Obtaining consistent research results in field experiments presents challenges, and simulations based on numerical modeling methods cannot accurately describe the field velocity and spatial distribution and are time-consuming and labor-intensive. A filtered Gaussian white noise signal was designed to simulate the random environmental crosswind signal in the field, and a numerical simulation of the downwash flow field of a plant protection UAV rotor under random crosswinds was conducted based on the computational fluid dynamics method. A prediction model for the downwash flow field that utilized a cosine annealing learning rate within physics-informed neural networks (PINNs) was introduced. The downwash airflow approximated a “cylindrical” downward development within 1s, reaching a peak coverage area and exhibiting strong resistance to random crosswinds. As the crosswind speed was increased, the structure and intensity of the downwash airflow changed significantly. When the random crosswind exceeded 3m/s, the airflow coverage area and peak vertical velocity were decreased substantially, which was unfavorable for spraying operations. The PINNs prediction model demonstrated high accuracy in predicting velocity information, with overall goodness of fit (R2) values of 0.971 and 0.919 and root mean square errors of 0.364m/s and 0.253m/s for the horizontal and vertical velocity predictions, respectively. Based on the high-accuracy flow field information obtained from the model, the droplet drift trend can be further evaluated, thereby providing a physical basis for analyzing the effectiveness of spraying operations. These findings can provide valuable references for studying the influence of rotor airflow field on droplet deposition characteristics during field operations.