CFD simulation and experimental verification of the spatial and temporal distributions of the downwash airflow of a quad-rotor agricultural UAV in hover

Guo, Qiwei; Zhu, Yifan; Tang, Yu; Hou, Chao; He, Yong; Zhuang, Jiajun; Zheng, Youliang; Luo, Shaoming

doi:10.1016/j.compag.2020.105343

Cited by 73 publications

(43 citation statements)

References 17 publications

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“…These conclusions are consistent with Yang et al.’s experiment [ 31 , 32 ], showing that the downwash wind field error generated by CFD is acceptable under the case of farmland plant protection.…”

Section: Application and Verification Of Cfd Simulationsupporting

confidence: 92%

“…Relevant studies show that the shear-stress transport (SST) k –

turbulence model can accurately capture the flow separation characteristics, simulate the turbulence phenomenon realistically, and calculate the aerodynamic parameters accurately. This model is widely used in the numerical simulation of propeller wind field [ 32 , 37 , 58 ]. The CFD simulation in this paper is based on Fluent (2019 R2, ANSYS Inc., Canonsburg, USA) simulation platform.…”

Section: Application and Verification Of Cfd Simulationmentioning

confidence: 99%

“…However, LBM often leads to a large amount of calculation, high equipment requirement, and low resolution [ 29 ]. Thus, mature FVM is often used to simulate the wind field of plant protection UAV for analyzing the effect of different flight and environment states [ 31 , 32 , 33 , 34 ]. The result shows that this method is reliable by using an anemometer or particle image velocimetry [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

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Design of Variable Spray System for Plant Protection UAV Based on CFD Simulation and Regression Analysis

Wang

Liu

et al. 2021

Sensors

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Multi-rotor unmanned aerial vehicles (UAVs) for plant protection are widely used in China’s agricultural production. However, spray droplets often drift and distribute nonuniformly, thereby harming its utilization and the environment. A variable spray system is designed, discussed, and verified to solve this problem. The distribution characteristics of droplet deposition under different spray states (flight state, environment state, nozzle state) are obtained through computational fluid dynamics simulation. In the verification experiment, the wind velocity error of most sample points is less than 1 m/s, and the deposition ratio error is less than 10%, indicating that the simulation is reliable. A simulation data set is used to train support vector regression and back propagation neural network with multiple parameters. An optimal regression model with the root mean square error of 6.5% is selected. The UAV offset and nozzle flow of the variable spray system can be obtained in accordance with the current spray state by multi-sensor fusion and the predicted deposition distribution characteristics. The farmland experiment shows that the deposition volume error between the prediction and experiment is within 30%, thereby proving the effectiveness of the system. This article provides a reference for the improvement of UAV intelligent spray system.

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Section: Application and Verification Of Cfd Simulationsupporting

confidence: 92%

“…Relevant studies show that the shear-stress transport (SST) k –

Section: Application and Verification Of Cfd Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design of Variable Spray System for Plant Protection UAV Based on CFD Simulation and Regression Analysis

Wang

Liu

et al. 2021

Sensors

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“…The UAV platform consisted of the flight controller, propulsion system (four propellers), GPS, and remote control. The effect of the downwash of the propellers was considered negligible for the thermal infrared data collection, due to the placement of the TeAx sensor underneath the central part of the UAV platform and the distance to the ground (13 m), especially considering the results by Guo et al [48]. The Universal Ground Control software (UgCS; SPH Engineering, Riga, Latvia) was used to control the UAV and undertake the flight planning with 60% sidelap, thereby providing 18 flight lines of the field trial.…”

Section: Thermal Data Acquisitionmentioning

confidence: 99%

Overcoming the Challenges of Thermal Infrared Orthomosaics Using a Swath-Based Approach to Correct for Dynamic Temperature and Wind Effects

et al. 2021

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The miniaturization of thermal infrared sensors suitable for integration with unmanned aerial vehicles (UAVs) has provided new opportunities to observe surface temperature at ultra-high spatial and temporal resolutions. In parallel, there has been a rapid development of software capable of streamlining the generation of orthomosaics. However, these approaches were developed to process optical and multi-spectral image data and were not designed to account for the often rapidly changing surface characteristics inherent in the collection and processing of thermal data. Although radiometric calibration and shutter correction of uncooled sensors have improved, the processing of thermal image data remains difficult due to (1) vignetting effects on the uncooled microbolometer focal plane array; (2) inconsistencies between images relative to in-flight effects (wind-speed and direction); (3) unsuitable methods for thermal infrared orthomosaic generation. Here, we use thermal infrared UAV data collected with a FLIR-based TeAx camera over an agricultural field at different times of the day to assess inconsistencies in orthophotos and their impact on UAV-based thermal infrared orthomosaics. Depending on the wind direction and speed, we found a significant difference in UAV-based surface temperature (up to 2 °C) within overlapping areas of neighboring flight lines, with orthophotos collected with tail wind being systematically cooler than those with head wind. To address these issues, we introduce a new swath-based mosaicking approach, which was compared to three standard blending modes for orthomosaic generation. The swath-based mosaicking approach improves the ability to identify rapid changes of surface temperature during data acquisition, corrects for the influence of flight direction relative to the wind orientation, and provides uncertainty (pixel-based standard deviation) maps to accompany the orthomosaic of surface temperature. It also produced more accurate temperature retrievals than the other three standard orthomosaicking methods, with a root mean square error of 1.2 °C when assessed against in situ measurements. As importantly, our findings demonstrate that thermal infrared data require appropriate processing to reduce inconsistencies between observations, and thus, improve the accuracy and utility of orthomosaics.

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“…In order to ensure the accuracy of simulation and test as much as possible, the single-rotor UAV was selected as the research platform in this study. Compared with the interaction among the airflow fields of the multi rotor UAV, the downwash airflow field of the single rotor UAV had no influence by other airflow, with smaller error and higher accuracy in terms of simulation accuracy and actual test data acquisition [18][19][20][21][22][23] . Meanwhile, accurate simulation of rotor flow airfield of single-rotor UAV could provide method reference and basic data support for further study of rotor airflow field of multi-rotor UAV.…”

Section: Introduction mentioning

confidence: 99%

Distribution regularity of downwash airflow under rotors of agricultural UAV for plant protection

Liu

Zhang

et al. 2021

International Journal of Agricultural and Biological Engineering

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In the plant protection spray operation of UAVs, the process of droplet from formation to sedimentation target is affected by airflow, easy to form uneven deposition. Accurately description of rotor downwash flow field, clarification of velocity vector distribution at different heights of the UAV rotor flow field, simulation of the flow field with high precision, which are the prerequisites for accurately analyzing the droplet deposition distribution in rotor downwash flow field. Based on CFD method, the detail of rotor flow field was numerically calculated. Taking LTH-100 single-rotor agricultural UAV as the research object, the three-dimensional solid model of UAV was established, the Reynolds average N-S equation was used as the control equation and the RNG κ-ε as the turbulence model to simulate the flow field of UAV in hover and lateral wind conditions, the wind velocity distribution at different altitudes of rotor downwash flow field was studied. The simulation results of the hover state showed that: In the flow field, the peak velocity appears in a circular distribution below the distal axis of the rotor. With the decrease of height, the peak velocity distribution area showed a tendency to expand gradually after small shrinkage; When the distance from the rotor was not more than 1.5 m, the downwash flow field presented an axisymmetric distribution based on the rotor axis, and the variation rate of velocity in the peak velocity was basically the same, turbulence in downwash flow field made the flow field more complex when the distance from rotor was larger than 2.0 m. On this basis, the optimal flight altitude of UAV is 1.5 m. Wind velocity test of the flow field was carried out on a rotor test bench, wind velocities at four altitudes of 0.5 m, 1.0 m, 1.5 m and 2.0 m were measured to verify the coincidence between the simulated and measured values. The test results showed that: the relative error between the measured and simulated values at four measurement heights were between 0.382-0.524, and the overall average relative errors was 0.430, which verified the confidence level of simulated values for measured values. When the lateral wind velocity was 3 m/s, 4 m/s and 5 m/s, the simulation results showed that: The distribution trend of airflow velocity at the same altitude in lateral-wind flow field with different wind speeds was similar; When the lateral wind speed was 5 m/s, the coupling field formed by the lateral wind and rotor airflow cannot reach the height of 2 m below the rotor. The results of this study can provide more accurate environmental conditions for theoretical analysis of droplet deposition regularity in the flow field, and also provide methodological guidance for the related research on rotor flow field of multi-rotor UAV.

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CFD simulation and experimental verification of the spatial and temporal distributions of the downwash airflow of a quad-rotor agricultural UAV in hover

Cited by 73 publications

References 17 publications

Design of Variable Spray System for Plant Protection UAV Based on CFD Simulation and Regression Analysis

Design of Variable Spray System for Plant Protection UAV Based on CFD Simulation and Regression Analysis

Overcoming the Challenges of Thermal Infrared Orthomosaics Using a Swath-Based Approach to Correct for Dynamic Temperature and Wind Effects

Distribution regularity of downwash airflow under rotors of agricultural UAV for plant protection

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