WeChat Mini Program
Old Version Features

Numerical Simulation of the Effects of Downwash Airflow and Crosswinds on the Spray Performance of Quad-Rotor Agricultural UAVs

Qiwei Guo, Yaozong Zhu,Yu Tang,Chaojun Hou, Mingwei Fang, Xiaobing Chen

Smart Agricultural Technology(2025)

Cited 0|Views7
Abstract
To elucidate the influence of rotor downwash airflow on droplet dynamics during agricultural UAV spraying, this study established a three-dimensional gas-liquid coupling numerical model. The synergistic effects of flight speed (1–5 m/s), operation altitude (2–4 m), and crosswinds (0–2 m/s) on droplet deposition and drift were systematically analyzed. Results demonstrated that increased UAV flight speed significantly tilted the downwash airflow backward, exacerbating drift losses for smaller droplets. Higher operation altitudes prolonged droplet residence times within airflow, further elevating drift risk. Crosswind velocity positively correlated with downwash airflow deflection angles, expanding airflow coverage under crosswinds; however, increasing crosswind velocities unexpectedly reduced droplet deflection angles. Experimental validation revealed a relative error between simulated and measured deposition of 27.2 % to 30 %, confirming the model's reliability. This study uniquely uncovers droplet drift patterns under crosswind conditions, offering new theoretical insights for optimizing UAV spray operations.
More
Translated text
Key words
Downwash airflow,Quad-rotor agricultural UAV,Ambient crosswinds,Droplet drift
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined