植保无人机施药数值建模关键技术与验证方法研究进展

doi:10.12133/j.smartag.2021.3.3.202107-SA004

Smart Agriculture ›› 2021, Vol. 3 ›› Issue (3): 1-21.doi: 10.12133/j.smartag.2021.3.3.202107-SA004

• 专题--智能植保机械与施药技术 • 下一篇

植保无人机施药数值建模关键技术与验证方法研究进展

唐青^1,^2,³(), 张瑞瑞^1,^2,³, 陈立平^1,^2,³(), 李龙龙^1,^2,³, 徐刚^1,^2,³

^1. 国家农业智能装备工程技术研究中心，北京 100097
^2. 北京市农林科学院智能装备技术研究中心，北京 100097
^3. 国家农业航空应用技术国际联合研究中心，北京 100097

收稿日期:2021-07-08 修回日期:2021-09-17 出版日期:2021-06-30 发布日期:2021-12-06
基金资助:
国家自然科学基金项目(31771674);北京市农林科学院青年科研基金(QNJJ202009)
作者简介:唐青（1985-），男，博士，副研究员，研究方向为精准农业航空应用技术。E-mail：tangq@nercita.org.cn。
通讯作者: 陈立平 E-mail:tangq@nercita.org.cn;chenlp@ nercita.org.cn

Research Progress of Key Technologies and Verification Methods of Numerical Modeling for Plant Protection Unmanned Aerial Vehicle Application

TANG Qing^1,^2,³(), ZHANG Ruirui^1,^2,³, CHEN Liping^1,^2,³(), LI Longlong^1,^2,³, XU Gang^1,^2,³

^1. National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
^2. Research Center for Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
^3. National Center for International Research on Agricultural Aerial Application Technology, Beijing 100097, China

Received:2021-07-08 Revised:2021-09-17 Online:2021-06-30 Published:2021-12-06
corresponding author: CHEN Liping E-mail:tangq@nercita.org.cn;chenlp@ nercita.org.cn

摘要/Abstract

摘要：

随着植保无人机在精细农业上的应用日益增长，目前在植保无人机下洗风场演化及其作用下的雾滴沉积飘移过程的数值模拟方法取得了快速多样化发展，但对各方法的优势、缺陷、适用范围及验证手段仍缺乏系统的梳理。本文针对无粘模型、计算流体力学模型及格子玻尔兹曼模型分别开展论述。基于涡元法的无粘尾涡模型优势在于计算过程简单，但由于缺乏粘性和湍流模型，其雾滴沉积飘移模拟精度较低。计算流体力学模型又分为有限体积法与有限差分法。其中，有限体积法鲁棒性高，可适用于各种复杂环境的模拟，但格式精度有限，其模拟的翼尖涡耗散速度远快于实际情况；有限差分法能够实现对翼尖涡演化的高时空精度模拟，但其存在网格结构化要求高，算力要求过大等问题。格子玻尔兹曼方法在计算具有复杂边界条件和非平稳运动物体的三维流场问题中具备优势，但其在功能多样性和完备性上还存在不足。上述数值模型精度还需综合运用田间实验及室内实验，如高速粒子图像测速（Particle Image Velocimetry，PIV）或相位多普勒测速（Phase Doppler Interferometry，PDI）方法进行验证和优化。最后，本文提出了未来植保无人机施药模拟及验证方法发展方向。

关键词: 植保无人机, 下洗风场, 数值模拟, 雾滴沉积飘移, 计算流体力学

Abstract:

With the increasing application of plant protection unmanned aerial vehicle (UAV) in precision agriculture, the numerical simulation methods for the development of the downwash flow field of the plant protection UAV and the deposition and drift process of droplets affected by the downwash flow field have achieved rapid and diversified development, but the advantages, disadvantages, scope of application, and verification of each method still lack a systematic review. This article discusses the inviscid model, computational fluid dynamics model and lattice Boltzmann model (LBM) respectively. The advantage of the inviscid wake vortex model based on the vortex element method is that the calculation process is simple. Moreover, integrated with the most widely used aerial spray drift prediction software AGricultural DISPersal (AGDISP), it can be a promising way to do real-time UAV spray drift prediction. But due to lack of viscosity and turbulence models, the droplet deposition and drift simulation accuracy of inviscid model is relatively lower than other models. The computational fluid dynamics (CFD) model includes the finite volume method (FVM) and the finite difference method (FDM). The FVM in the computational fluid dynamics model has high robustness and can be applied to the simulation of various complex environments. Many commercial CFD software are based on FVM and achieved a fast development in aerial spray modeling recently. However, the FVM is greatly affected by the quality of the mesh, and its commonly used upwind style has limited accuracy (second-order accuracy). Under the same mesh density, it is easier to generate artificial dissipation when simulating the rotor tip vortex than the finite difference method. As a result, the simulated rotor tip vortex dissipation speed is much faster than the actual situation. Compared with the FVM, the structured grid used in the FDM is easier to construct a high-order precision numerical format. Which can reach 4-5 orders of accuracy, and with adaptive grid technology, FDM can simulate the evolution of rotor tip vortex with high temporal and spatial accuracy, and can reproduce the typical flow structure development process of the real rotor downwash flow field. However, it also has problems such as high grid structure requirements and excessive computing power requirements. LBM has advantages in computing three-dimensional flow field problems with complex boundary conditions and non-stationary moving objects. However, there are still shortcomings in its functional diversity and completeness. The accuracy of the numerical models mentioned above still needs field test and indoor experiment such as high-speed Particle Image Velocimetry (PIV)/ Phase Doppler Interferometry (PDI) method to verify and optimize. The authors finally pointed out the future direction of plant protection UAV application simulation and verification.

Key words: plant protection UAV, downwash flow field, numerical simulation, droplet deposition and drift, computational fluid dynamics

中图分类号:

S252+.3

唐青, 张瑞瑞, 陈立平, 李龙龙, 徐刚. 植保无人机施药数值建模关键技术与验证方法研究进展[J]. 智慧农业(中英文), 2021, 3(3): 1-21.

TANG Qing, ZHANG Ruirui, CHEN Liping, LI Longlong, XU Gang. Research Progress of Key Technologies and Verification Methods of Numerical Modeling for Plant Protection Unmanned Aerial Vehicle Application[J]. Smart Agriculture, 2021, 3(3): 1-21.

图/表 15

图1

图2

图3

表 1

D3Q27 离散速度模型参数

模型	格子声速 $c S / c$	格子离散速度 $e α / c$	权重 $w α$
D3Q27	$1 / 3$	（0， 0， 0）	8/27 （ $α = 0$ ）
		（ $± 1$ ， 0， 0），（0， $± 1$ ， 0），（0， 0， $± 1$ ）	2/27 （ $α = 1, ?, 6$ ）
		（ $± 1$ ， $± 1$ ， 0），（ $± 1$ ， 0， $± 1$ ），（0， $± 1$ ， $± 1$ ）	1/54 （ $α = 7, ?, 18$ ）
		（ $± 1$ ， $± 1$ ， $± 1$ ）	1/216 （ $α = 19, ?, 26$ ）

表 1

图4

图5

图6

图7

图8

图9

图10

图11

图12

表2

表 3

参考文献 78

1	HEWITT A J , JOHNSON D R , FISH J D , et al . Development of the spray drift task force database for aerial applications[J]. Environmental Toxicology and Chemistry, 2002, 21(3): 648-658.
2	BIRD S L , PERRY S G , RAY S L, et al . Evaluation of the AgDISP aerial spray algorithms in the AgDRIFT model[J]. Environmental Toxicology and Chemistry, 2002, 21(3): 672-681.
3	TESKE M , THISTLE H , FRITZ B . Modeling aerially applied sprays: An update to AgDISP model development[J]. Transactions of the ASABE, 2019, 62(2): 343-354.
4	TESKE M , THISTLE H , SCHOU W , et al . A review of computer models for pesticide deposition prediction[J]. Transactions of the ASABE, 2011, 54: 1-14.
5	TSAY J , LIANG L , LU L . Evaluation of an air‐assisted boom spraying system under a no-canopy condition using CFD simulation[J]. Transactions of the ASABE, 2004, 47(6): 1887-1897.
6	DELELE M A , JAEKEN P , DEBAER C , et al . CFD prototyping of an air-assisted orchard sprayer aimed at drift reduction[J]. Computers and Electronics in Agriculture, 2007, 55(1): 16-27.
7	DEKEYSER D , DUGA A T , VERBOVEN P , et al . Assessment of orchard sprayers using laboratory experiments and computational fluid dynamics modelling[J]. Biosystems Engineering, 2013, 114(2): 157-169.
8	ENDALEW A M , DEBAER C , RUTTEN N , et al . A new integrated CFD modelling approach towards air-assisted orchard spraying: Part I. Model development and effect of wind speed and direction on sprayer airflow[J]. Computers and Electronics in Agriculture, 2010, 71(2): 128-136.
9	ENDALEW A M , DEBAER C , RUTTEN N , et al . A new integrated CFD modelling approach towards air-assisted orchard spraying: Part II. Validation with different machine types[J]. Computers and Electronics in Agriculture, 2010, 71(2): 137-147.
10	ENDALEW A M , HENDRICKX N , GOOSSENS T , et al . An integrated approach to investigate the orchard spraying process: Toward a CFD model incorporating tree architecture[C]// ASABE International Meeting, St. Joseph, Michigan: ASABE, 2010.
11	DUGA A T , DELELE M A , RUYSENK, et al . Development and validation of a 3D CFD model of drift and its application to air-assisted orchard sprayers[J]. Biosystems Engineering, 2017, 154: 62-75.
12	BAETENS K , NUYTTENS D , VERBOVEN P , et al . Predicting drift from field spraying by means of a 3D computational fluid dynamics model[J]. Computers and Electronics in Agriculture, 2007, 56(2): 161-173.
13	ZHANG B , TANG Q , CHEN L , et al . Numerical simulation of spray drift and deposition from a crop spraying aircraft using a CFD approach[J]. Biosystems Engineering, 2018, 166: 184-199.
14	ZHANG B , TANG Q , CHEN L , et al . Numerical simulation of wake vortices of crop spraying aircraft close to the ground[J]. Biosystems Engineering, 2016, 145: 52-64.
15	TESKE M E , WACHSPRESS D A , THISTLE H W . Prediction of aerial spray release from UAVs[J]. Transactions of the ASABE, 2018, 61(3): 909-918.
16	QUACKENBUSH T R , BLISS D B , WACHSPRESS D A , et al . Computation of rotor aerodynamic loads in forward flight using a full-span free wake analysis[R]. NASA Contractor Report 177611, 1993.
17	QUACKENBUSH T R , WACHSPRESS D A , BLISS D B , et al . Helicopter free wake analyses using influence coefficients[R]. NASA Contractor Report 177612,1993.
18	QUACKENBUSH T R , LAM C M G, WACHSPRESS D A , et al . Analysis of high resolution unsteady airloads for helicopter rotor blades[C]// American Helicopter Society 50th Annual Forum Proceedings. Washington, D.C., USA: American Helicopter Society, 1994.
19	QUACKENBUSH T , WACHSPRESS D , BLISS D . New free-wake analysis of rotorcraft hover performance using influence coefficients[J]. Journal of Aircraft, 1989, 26: 1090-1097.
20	WACHSPRESS D , QUACKENBUSH T , BOSCHITSCH A . Rotorcraft interactional aerodynamics with fast vortex/fast panel methods[J]. Journal of The American Helicopter Society, 2003, 48: 223-235.
21	RULE J A , BLISS D B . Prediction of turbulent trailing vortex structure for rotorcraft blade-vortex interaction[C]// American Helicopter Society 51st Annual Forum Proceedings. Fort Worth, TX: American Helicopter Society, 1995.
22	ASHBY D L , DUDLEY M R , IGUCHI S K . Development and validation of an advanced low-order panel method[R]. NASA Technical Memorandum 101024, 1988.
23	Maskew B . Program VSAERO theory document[R]. NASA Contractor Report 4023, 1987.
24	MORINO L , KUO C C . Subsonic potential aerodynamics for complex configurations: A general theory[J]. AIAA Journal, 1974, 12(2): 191-197.
25	APPEL A W . An efficient program for many-body simulation[J]. SIAM Journal on Scientific Statistical Computing, 1985, 6(1): 85-103.
26	TADROS T F . Interactions at interfaces and effects on transfer and performance[J]. Aspects of Applied Biology, 1987, 14: 1-22.
27	SCHOU W , FORSTER W A , MERCER G , et al . Building canopy retention into AGDISP: Preliminary models and results[J]. Transactions of the ASABE, 2012, 55: 2059-2066.
28	ATTANÉ P , GIRARD F , MORIN V . An energy balance approach of the dynamics of drop impact on a solid surface[J]. Physics of Fluids, 2007, 19(1): ID 012101.
29	FORSTER W A , MERCER G N , SCHOU W C . Process driven models for spray droplet shatter, adhesion or bounce[C]// 9th International Symposium on Adjuvants for Agrochemicals. Munich, Germany: Technical University of Munich, 2010.
30	YANG F , XUE X , ZHANG L , et al . Numerical simulation and experimental verification on downwash air flow of six-rotor agricultural unmanned aerial vehicle in hover[J]. International Journal of Agricultural and Biological Engineering, 2017, 10(4): 41-53.
31	杨风波, 薛新宇, 蔡晨, 等 . 多旋翼植保无人机悬停下洗气流对雾滴运动规律的影响[J]. 农业工程学报, 2018, 34(2): 64-73.
	YANG F , XUE X , CAI C , et al . Effect of down wash airflow in hover on droplet motion law for multi-rotor unmanned plant protection machine[J]. Transactions of the CSAE, 2018, 34(2): 64 -73.
32	SHI Q , MAO H , GUAN X . Numerical simulation and experimental verification of the deposition concentration of an unmanned aerial vehicle[J]. Applied Engineering in Agriculture, 2019, 35: 367-376.
33	ZHU H , LI H , ZHANG C , et al . Performance characterization of the UAV chemical application based on CFD simulation[J]. Agronomy, 2019, 9(6): ID 308.
34	张宋超, 薛新宇, 秦维彩, 等 . N-3型农用无人直升机航空施药飘移模拟与试验[J]. 农业工程学报, 2015, 31(3): 87-93.
	ZHANG S , XUE X , QIN W , et al . Simulation and experimental verification of aerial spraying drift on N-3 unmanned spraying helicopter[J]. Transactions of the CSAE, 2015, 31(3): 87-93.
35	张豪, 祁力钧, 吴亚垒, 等 . 基于Porous模型的多旋翼植保无人机下洗气流分布研究[J]. 农业机械学报, 2019, 50(2): 112-122.
	ZHANG H , QI L , WU Y , et al . Spatio-temporal distribution of downwash airflow for multirotor plant protection UAV based on porous model[J]. Transactions of the CSAM, 2019, 50(2): 112-122.
36	杨知伦, 葛鲁振, 祁力钧, 等 . 植保无人机旋翼下洗气流对喷幅的影响研究[J]. 农业机械学报, 2018, 49(1): 116-122.
	YANG Z , GE L , QI L , et al . Influence of UAV rotor down-wash airflow on spray width[J]. Transactions of the CSAM, 2018, 49(1): 116-122.
37	XU L , WENG P . High order accurate and low dissipation method for unsteady compressible viscous flow computation on helicopter rotor in forward flight[J]. Journal of Computational Physics, 2014, 258: 470-488.
38	LAKSHMINARAYAN V , KALRA T S , BAEDER J . Detailed computational investigation of a hovering microscale rotor in ground effect[J]. AIAA Journal, 2013, 51(4): 893-909.
39	KALRA T . CFD modeling and analysis of rotor wake in hover interacting with a ground plane[D]. Maryland: The University of Maryland, College Park, 2014.
40	CHEN S , DOOLEN G . Lattice Boltzmann method for fluid flows[J]. Annual Review of Fluid Mechanics, 1998, 30(1): 329-364.
41	SUGA K , KUWATA Y , TAKASHIMA K , et al . A D3Q27 multiple-relaxation-time lattice Boltzmann method for turbulent flows[J]. Computers and Mathematics with Applications, 2015, 69(6): 518-529.
42	Xflow 2017 theory guide[Z]. 2011-2017 Dassault Systèmes España, SLU.
43	ZHANG H , QI L , WU Y , et al . Numerical simulation of airflow field from a six-rotor plant protection drone using lattice Boltzmann method[J]. Biosystems Engineering, 2020, 197: 336-351.
44	文晟, 韩杰, 兰玉彬, 等 . 单旋翼植保无人机翼尖涡流对雾滴漂移的影响[J]. 农业机械学报, 2018, 49(8): 127-137, 160.
	WEN S , HAN J , LAN Y , et al . Influence of wing tip vortex on drift of single rotor plant protection unmanned aerial vehicle[J].Transactions of the CSAM, 2018, 49(8): 127-137, 160.
45	WEN S , HAN J , NING Z , et al . Numerical analysis and validation of spray distributions disturbed by quad-rotor drone wake at different flight speeds[J]. Computers and Electronics in Agriculture, 2019, 166: ID 105036.
46	TANG Q , ZHANG R , CHEN L , et al . Numerical simulation of the downwash flow field and droplet movement from an unmanned helicopter for crop spraying[J]. Computers and Electronics in Agriculture, 2020, 174: ID 105468.
47	TANG Q , CHEN L , ZHANG R , et al . Effects of application height and crosswind on the crop spraying performance of unmanned helicopters[J]. Computers and Electronics in Agriculture, 2021, 181: ID 105961.
48	唐青, 陈立平, 张瑞瑞, 等 . 高速气流条件下标准扇形喷头和空气诱导喷头雾化特性[J]. 农业工程学报, 2016, 32(22): 121-128.
	TANG Q , CHEN L , ZHANG R , et al . Atomization characteristics of normal flat fan nozzle and air induction nozzle under high speed airflow conditions[J]. Transactions of the CSAE, 2016, 32(22): 121-128.
49	TANG Q , CHEN L , ZHANG R , et al . Droplet spectra and high-speed wind tunnel evaluation of air induction nozzles[J]. Frontiers of Agricultural Science and Engineering, 2018, 5(4): 442-454.
50	KIRK I W . Measurement and prediction of atomization parameters from fixed-wing aircraft spray nozzles[J]. Transactions of the ASABE, 2007, 50, 693-703.
51	FRITZ B , HOFFMANN W C , BAGLEY W , et al . Measuring droplet size of agricultural spray nozzles-measurement distance and airspeed effects[J]. Atomization and Sprays, 2014, 24: 747-760.
52	李继宇, 周志艳, 兰玉彬, 等 . 旋翼式无人机授粉作业冠层风场分布规律[J]. 农业工程学报, 2015, 31(3): 77-86.
	LI J , ZHOU Z , LAN Y , et al . Distribution of canopy wind field produced by rotor unmanned aerial vehicle pollination operation[J]. Transactions of the CSAE, 2015, 31(3): 77-86.
53	王昌陵, 何雄奎, 王潇楠, 等 . 无人植保机施药雾滴空间质量平衡测试方法[J]. 农业工程学报, 2016, 32(11): 54-61.
	WANG C , HE X , WANG X , et al . Testing method of spatial pesticide spraying deposition quality balance for unmanned aerial vehicle[J]. Transactions of the CSAE, 2016, 32(11): 54-61.
54	陈盛德, 兰玉彬, 李继宇, 等 . 植保无人机航空喷施作业有效喷幅的评定与试验[J]. 农业工程学报, 2017, 33(7): 82-90.
	CHEN S , LAN Y , LI J , et al . Evaluation and test of effective spraying width of aerial spraying on plant protection UAV[J]. Transactions of the CSAE, 2017, 33(7): 82-90.
55	王昌陵, 宋坚利, 何雄奎, 等 . 植保无人机飞行参数对施药雾滴沉积分布特性的影响[J]. 农业工程学报, 2017, 33(23): 109-116.
	WANG C , SONG J , HE X , et al . Effect of flight parameters on distribution characteristics of pesticide spraying droplets deposition of plant-protection unmanned aerial vehicle[J]. Transactions of the CSAE, 2017, 33(23): 109-116.
56	WEN Y , ZHANG R , CHEN L , et al . A new spray deposition pattern measurement system based on spectral analysis of a fluorescent tracer[J]. Computers and Electronics in Agriculture, 2019, 160: 14-22.
57	ZHANG R , WEN Y , LI L , et al . Method for UAV spraying pattern measurement with PLS model based spectrum analysis[J]. International Journal of Agricultural and Biological Engineering, 2020, 13(3): 22-28.
58	张京, 何雄奎, 宋坚利, 等 . 无人驾驶直升机航空喷雾参数对雾滴沉积的影响[J]. 农业机械学报, 2012, 43(12): 94-96.
	ZHANG J , HE X , SONG J , et al . Influence of spraying parameters of unmanned aircraft on droplets deposition[J].Transactions of the CSAM, 2012, 43(12): 94-96.
59	JIAO L , DONG D , FENG H , et al . Monitoring spray drift in aerial spray application based on infrared thermal imaging technology[J]. Computers and Electronics in Agriculture, 2016, 121: 135-140.
60	GREGORIO E , TORRENT X , DE MARTÍ S P , et al . Measurement of spray drift with a specifically designed lidar system[J]. Sensors, 2016, 16: 1-15.
61	KIRA O , LINKER R , DUBOWSKI Y . Estimating drift of airborne pesticides during orchard spraying using active open path FTIR[J]. Atmospheric Environment, 2016, 142: 264-270.
62	FRITZ B K , HOFFMANN W C . Update to the USDA-ARS fixed-wing spray nozzle models[J]. Transactions of the ASABE, 2015: 281-295.
63	HEWITT A J . Droplet size spectra classification categories in aerial application scenarios[J]. Crop Protection, 2008, 27(9): 1284-1288.
64	FRITZ B K , HOFFMANN W C , KRUGER G R , et al . Comparison of drop size data from ground and aerial application nozzles at three testing laboratories[J]. Atomization & Sprays, 2014, 24(2): 181-192.
65	李龙龙, 何雄奎, 宋坚利, 等 . 基于高频电磁阀的脉宽调制变量喷头喷雾特性[J]. 农业工程学报, 2016, 32(1): 97-103.
	LI L , HE X , SONG J , et al . Spray characteristics on pulse-width modulation variable application based on high frequency electromagnetic valve[J]. Transactions of the CSAE, 2016, 32(1): 97-103.
66	TANG Q , CHEN L P , ZHANG R R , et al . Droplet spectra and high-speed wind tunnel evaluation of air induction nozzles[J]. Frontiers of Agricultural Science and Engineering, 2018, 5(4): 442-454.
67	RAFFEL M , GREGORIO F D , GROOT K D , et al . On the generation of a helicopter aerodynamic database[J]. Aeronautical Journal, 2011, 115(1164): 103-112.
68	RAFFEL M , RICHARD H , EHRENFRIED K , et al . Recording and evaluation methods of PIV investigations on a helicopter rotor model[J]. Experiments in Fluids, 2004, 36(1): 146-156.
69	WALL BVAN DER , RICHARD H . Analysis methodology for 3C-PIV data of rotary wing vortices[J]. Experiments in Fluids, 2006, 40(5): 798-812.
70	JOHNSON B , LEISHMAN J G , SYDNEY A . Investigation of sediment entrainment using dual-phase, high-speed particle image velocimetry[J]. Journal of the American Helicopter Society, 2010, 55(4): 42-53.
71	SYDNEY A , LEISHMAN J G . Time-resolved measurements of rotor-induced particle flows produced by a hovering rotor[J]. Journal of the American Helicopter Society, 2014, 59: ID 022004.
72	LEE T E , LEISHMAN J G , RAMASAMY M . Fluid dynamics of interacting blade tip vortices with a ground plane[J]. Journal of the American Helicopter Society, 2010, 55(2): ID 022005.
73	NATHAN N D , GREEN R B . The flow around a model helicopter main rotor in ground effect[J]. Experiments in Fluids, 2012, 52(1): 151-166.
74	TANG Q , ZHANG R , CHEN L , et al . High-accuracy, high-resolution downwash flow field measurements of an unmanned helicopter for precision agriculture[J]. Computers and Electronics in Agriculture, 2020, 173: ID 105390.
75	TANG Q , ZHANG R , CHEN L , et al . Droplets movement and deposition of an 8-rotor agricultural UAV in downwash flow field[J]. International Journal of Agricultural and Biological Engineering, 2017, 10(3): 47-56.
76	NUYTTENS D , BAETENS K , DE SCHAMPHELEIRE M , et al . Direct and indirect drift assessment means. Part 1: PDPA laser based droplet characterisation[J]. Communications in Agricultural and Applied Biological Sciences, 2008, 73(4): 749-756.
77	NUYTTENS D , SCHAMPHELEIRE M D , VERBOVEN P , et al . Droplet size and velocity characteristics of agricultural sprays[J]. Transactions of the ASABE, 2009, 52(5): 1471-1480.
78	LI S , CHEN C , WANG Y , et al . Study on the atomization characteristics of flat fn nozzles for pesticide application at low pressures[J]. Agriculture, 2021, 11(4): ID 309.

模型验证方法	风速	雾滴速度/位置	雾滴粒径	时间分辨率	空间分辨率	场地适应性	操作难度
超声风速仪	√	——	——	高	低	高	中
风杯式风速仪	√	——	——	低	低	高	低
水敏纸及卡片	——	——	√	——	中	高	低
荧光纸带	——	——	√	——	高	高	中
热线风速仪	√	——	——	高	低	中	高
红外热成像	——	√	——	中	中	高	中
激光雷达	——	√	——	高	中	高	中
近红外开路傅里叶	——	√	——	中	中	高	中
激光粒度仪	——	——	√	中	中	中	中
PIV	√	√	√	高	高	低	高
PDI	——	√	√	高	中	低	高

[1]	王国宾, 韩鑫, 宋灿灿, 伊丽丽, 鲁文霞, 兰玉彬. 冬小麦田植保无人飞机喷施除草剂雾滴粒径及沉积飘移分布特性评估[J]. 智慧农业(中英文), 2021, 3(3): 38-51.
[2]	资乐, 臧禹, 黄俊浩, 包瑞峰, 周志艳, 肖汉祥. 植保无人机飞防助剂与杀虫剂的混配方式对二化螟防治效果影响研究[J]. 智慧农业(中英文), 2021, 3(3): 52-59.
[3]	王昌陵, 何雄奎, BONDSJane, 齐鹏, 杨苡, 高万林. 不同飞行参数下八旋翼植保无人机下洗气流场对雾滴沉积分布特性的影响[J]. 智慧农业（中英文）, 2020, 2(4): 124-136.
[4]	朱航, 李宏泽, 黄钰, 于海涛, 董云哲, 李君兴. 施药技术参数对旋翼植保无人机喷雾特性的影响[J]. 智慧农业, 2019, 1(3): 113-122.

植保无人机施药数值建模关键技术与验证方法研究进展

Research Progress of Key Technologies and Verification Methods of Numerical Modeling for Plant Protection Unmanned Aerial Vehicle Application

在线阅读

知网下载
（推荐）

本地下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 15

参考文献 78

相关文章 4

编辑推荐

Metrics

本文评价

模型	冠层适用性	环境适用性	模型适用性	湍流适用性	计算量	操作难度	涡模拟精度
无粘模型	低	中	中	低	低	低	中
有限体积法	中	高	中	高	高	中	中
有限差分法	中	高	低	高	极高	高	高
格子玻尔兹曼法	高	低	高	中	高	低	较高

植保无人机施药数值建模关键技术与验证方法研究进展

Research Progress of Key Technologies and Verification Methods of Numerical Modeling for Plant Protection Unmanned Aerial Vehicle Application

在线阅读

知网下载（推荐）

本地下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 15

参考文献 78

相关文章 4

编辑推荐

Metrics

本文评价

知网下载
（推荐）