CIGR-AgEng conference
Jun. 26–29, 2016, Aarhus, Denmark
Effect of the orange vegetation resistance on the airflow of an airblast sprayer in 3D CFD
simulations
Ramón Salcedo a, Ariane Vallet b, Rafael Granell a, José Blasco a, Guillermo Palau c, Cruz Garcerá a, Enrique
Moltó a, Patricia Chueca a,*
a
Centro de Agroingeniería, Instituto Valenciano de Investigaciones Agrarias. Ctra. Moncada-Náquera km. 4,5 46113 Moncada (Spain)
b
Institut de Recherche en Sciences et Technologies pour l'Environnement et l'Agriculture, 361 rue JF Breton - 34 196 Montpellier
(France)
c
Rural and Agrifood Engineering Department. Polytechnic University of Valencia. Camino de Vera 46022 Valencia (Spain)
* Corresponding author. Email: chueca_pat@gva.es
Abstract
During pesticide treatments with airblast sprayers a fraction of the applied volume is lost to the ground or atmosphere.
Computational Fluid Dynamics (CFD) models can help to understand this phenomenon. In previous works, turbulence
structures of air produced by an airblast sprayer in front of orange canopy have been observed. A 2D CFD model of the
airflow from the fan was designed and validated. The next step was to design a 3D model. The objective of this work was
to study the effect of the canopy resistance on the air currents behaviour around the tree to define the canopy
characteristics. A 3D model was designed based on extrusion of the 2D model. Boundary conditions were defined with
experimental velocities from the fan, with the sprayer in a static position. The canopy facing the sprayer was simulated as
a solid central body with a porous subdomain around. Different resistance values of the porous subdomain were
simulated to obtain different tree porosity values. Results were compared with experimental velocities around the tree.
The tree porosity did not affect the airflow behaviour. All the values reproduced the same experimental vortexes, but with
lower order of magnitude of the velocities than in the field test and the vortex above the tree was displaced in the
simulation in a further position to the sprayer. Furthermore, the air current dissipated faster than in the real behaviour.
These results show that it is necessary to improve the adjustment of the vegetation and other parameters in the 3D CFD
model.
Keywords: Pesticide treatments, canopy characteristics, vortexes, turbulence modelling
1. Introduction
Pesticide treatments on citrus in Spain are frequently realized by means of airblast sprayers. These equipments have
axial fans which produce strong air currents that push droplets to get them into the canopy. But during the application,
not all the applied volume reaches the vegetation (Chen et al., 2013). A part is lost to the ground and another is carried
out of the treated area by the wind. There are also additional losses, such as runoff or evaporation. These losses can cause
negative effects on biodiversity and human populations (Beketov et al., 2013; Damalas, 2015).
To optimize treatments as much as possible is necessary to study the factors affecting efficiency, as weather
conditions (Catania et al., 2011), machinery setup (García Ramos et al., 2012) or vegetation characteristics (Duga et al.,
2015). Understanding how these variables influence in the efficiency of spray treatments enables to propose solutions to
improve it and minimize losses. With this purpose, tests based on collectors to measure off-target losses or to quantify the
mass balance have been carried out (Jensen and Olesen, 2014). However, all these field assays are limited by the number,
location and height of the collectors. They are laborious, time-consuming and expensive. In addition, they are always
depending on external parameters such as wind. The numerical simulation of the turbulent behaviour of airflow and
sprayed droplets using Computational Fluid Dynamics (CFD) models gets rid of these limitations so they can be a
complement for these experiments (Lee et al., 2013).
Plant tree structure has a great influence on the movement of air currents (Finnigan, 2000). These variations affect to
the sprayed droplets cloud. Therefore, there is a great interest in knowing how to simulate the behaviour of airflow
around the vegetation. To reproduce the pesticide treatments in vineyards (Da Silva et al., 2006) and pear trees (Endalew
et al., 2010) in CFD models the vegetation was considered as a homogeneous and/or porous medium, with additional
terms for the momentum, turbulence kinetic energy and dissipation in the standard k-ε model (Sanz, 2003) and the
models were able to reproduce the sceneries successfully However, these characteristics of vegetacion for CFD model of
citrus trees were not able to simulate the reality, as it was observed in Salcedo et al. (2012). Vineyards and pears crops
have lower leaf density and diameter canopy than citrus in Mediterranean conditions, so that the turbulent airflow around
the canopy may be different, and hence also the particles trajectories.
In previous works, a 2D CFD model of airblast sprayer airflow when passing through orange trees was designed
(Salcedo et al., 2015a). This model conceived the tree facing the fan as a solid body and the turbulence model SST k-ω
(Menter, 1993) was selected. This model was able to reproduce the two vortexes around the canopy occurring in field
·1·
CIGR-AgEng conference
Jun. 26–29, 2016, Aarhus, Denmark
conditions: one behind the canopy and another above (Salcedo et al., 2015b). The next step was to define the preliminary
considerations to develop a 3D CFD model based on the 2D model. The objective of this work was to study the effect of
the vegetation resistance on the air currents behaviour around the tree in front of the the fan to define the canopy
characteristics, as a first step to design a 3D model of the plant products treatments with airblast sprayer on orange trees.
2. Materials and Methods
2.1. Model design
The 3D model was based on an extrusion of the 2D model of Salcedo et al. (2015a). The domain was 10 meters wide,
13 high and 20 m long (Figure 1i). The surfaces A and B representing the air inlet from the fan faced an orange tree. The
surface C represented the suction area of the fan. The surface D represented the sprayer (the tank and the solid area
between the air outlet and the inlet of the fan). All other surfaces were air outlets, except the bottom horizontal area
equivalent to the ground. A uniform tree row was represented in front of the air inlet. That row had a solid interior body
and a porous subdomain around. This porous subdomain was divided in four parts (Figure 1ii): three parts composing the
trunk and another for the rest of the canopy. In addition, there were also two other rows completely porous in the model.
The maximum width of the porous bodies was 3.5 m and the maximum height was 2.6 m, coinciding with the dimensions
of the trees of the field test (Salcedo et al., 2015a). The distance of the porous domain of the first tree to the air inlet was
0.55 m, corresponding to the distance between the post A and the canopy in the experiment.
·2·
CIGR-AgEng conference
Jun. 26–29, 2016, Aarhus, Denmark
Figure 1. Computational domain (i) and detailed porous subdomain (ii).
2.2. Simulation characteristics
These simulations consisted in varying the resistance of the porous subdomain of the first tree facing the fan and
comparing the resulting simulated air current around the canopy with the experimental data in the three planes (Figure 1).
The CFD code uses Darcy’s equation to estimate the pressure drop caused by the resistance to the flow of a porous
body. This equation adds the estimation of losses due to inertia and to viscosity. Due to the high turbulence behaviour of
air currents, the viscosity losses were considered negligible in this work, as other authors considered for the vegetation
(Da Silva et al., 2006). Only the inertial losses were considered. . Eleven different simulations with different resistance
values were carried out (Table 1). The resistance values were changed depending on the results obtained in the previous
simulation. On the first tree, several resistance value in each section were tested. On the second and third tree the
resistance value was constant (1 m-1) for all the simulations for this work by hypothesis. This work was focused in the
tree facing the fan.
Table 1. Resistance values during simulations
·3·
CIGR-AgEng conference
Jun. 26–29, 2016, Aarhus, Denmark
Resistance (m-1)
Simulation
Tree 1
Tree 2 Tree 3
Part 1 Part 2 Part 3 Part 4
1
0
0
0
0
1
1
2
0
7
7
7
1
1
3
0
14
14
14
1
1
4
7
7
7
0
1
1
5
7
7
7
7
1
1
6
1
1
1
1
1
1
7
1
1
1
1
7
1
8
1
1
1
1
14
1
9
0
0
0
14
7
1
10
0
0
0
28
7
1
11
0
0
0
42
7
1
The program used for all simulations was ANSYS Fluent® (ANSYS, Inc. Canonsburg, PA, USA). RANS (Reynolds
Average Navier Stokes) approach was used and the air was modelled as an incompressible, isothermal and Newtonian
flow. As initial air velocities and turbulence intensity from experimental data of section 2.3 were introduced in the model
through surfaces A and B. In addition the characteristic length of 5% was introduced, corresponding to the value
estimated by Delele et al. (2005) for airblast sprayers.
Wind action was not considered in this work, because it was considered negligible its effect for the vegetation study
due the higher values from the fan than the wind (Salcedo et al., 2015a). It was chosen a simple design for the sprayer
suction. The surface C consumed an airflow corresponding to the addition of the airflow in the A and B surfaces in the
model.
STT k-ω air turbulence model with a discretization scheme of the second order was used. The SIMPLE algorithm was
used to solve the linear pressure–velocity coupling (Ferziger and Peric, 2001). The convergence criterion for simulations
was to obtain a minimum normalised residual value of 10-4. The duration of each simulation ranged from 8 to 16 h.
2.3. Comparison between simulated and experimental data
Results of simulations were compared with experimental data obtained in Salcedo et al (2015a), it was taken into
account that the velocity component going through the tree was Uy, the component directed to the supposed driving
tractor was Vx and the component directed to the atmosphere was Wz. For all simulations, the three components of the
velocity vector for simulated were compared with the experimental velocities in three parallel measurement planes
considered (x = -30, x = 0 and x = +30 cm) for posts C and D, and in the plane x = 0 cm for posts E, F and G. (Figure 2i).
The vertical posts E, F and G and the horizontal post B were only contained in the plane x = 0 cm (Figure 2ii).
·4·
CIGR-AgEng conference
Jun. 26–29, 2016, Aarhus, Denmark
Figure 2. Field test scheme: total plan view (ii) and elevation for the plane x = 0 cm. Points in figure represents the air measurement
points.
3. Results and Discussion
Figure 3 compare the results of simulations 1 (sim1), simulation 11 (sim 11) and experimental data for each plane of
post C as an example. The three velocity components for the post C0, aligned with the fan outlet, had the same structure
and order of magnitude between the experimental and the simulated values. The greatest differences were detected for the
vertical velocity Wz from 1.0 m in height. In the post C+30, Uy and Wz simulated components showed to have the same
pattern and order of magnitude as experimental velocities. However, the sense of the horizontal component Vx in the post
C+30 was opposite to the experimental sense. This also happened with the same component, Vx, in the post C-30, located
between the air outlet area and the suction area. It always had different sense compared to the experimental data.
Therefore, the model only reproduced simultaneously the three components roughly in the plane of fan inlet (x = 0
cm), while in the simulations with greater resistance values it was detected that differences in Vx increased (Figure 3). In
addition, two vortexes were originated, on both sides of the air inlet and parallel to the airblast sprayer, which became
more intense with greater porous resistance values (Figure 6ii, iv). These vortexes were observed in the field, as
suggested the negative character of Uy in C+30 and C-30. But the sense of the horizontal component Vx indicated that the
position of the vortexes in the simulation was different to the experimental data. The appropriate approach only for the
plane x = 0 and the displacement of these vortexes was probably due to the faster dissipation of the simulated air current
than in the real behaviour.
·5·
CIGR-AgEng conference
Jun. 26–29, 2016, Aarhus, Denmark
Figure 3. Experimental velocities (exp; red points) and results from simulations 1 (sim 1; black square) and 11 (sim 11:
green triangle) in posts C0, C+30 and C-30 for component U (left), V (center) and W (right).
Regarding velocities behind and above the tree (Figure 4), it was possible to reproduce the same experimental
vortexes in the ZY plane with the simulations with bigger vegetation resistance, as simulation 11 (Figure 6iii): a
counterclockwise vortex behind the tree and other clockwise vortex located above it (Salcedo et al., 2015ab). However,
the order of magnitude of the simulations on posts D+30, D0 and D-30 for horizontal velocities was lower than in the
experiment. In the case of Vx, in the field test it had a variable nature, it was positive up to 1.5 m high and then it was
negative. However, in the simulations Vx showed a constant behavior, always presenting the same sign with around the
same value. Moreover, experimental vertical velocities were always negative on post D, but in the model, Wz tended to
become positive from 2.5 m, except in simulations 9, 10 and 11 (Table 1), which are those that offer greater porous
resistance. In these same simulations, minor differences with the experimental data were shown over the canopy. In fact,
only simulations 10 and 11 reflected the presence of the experimental vortex on the corresponding posts F0 and G0
(Figure 5). Hence it was found that increasing the porous resistance to the passage of air, the phenomenon of airflow
separation with the obstacle was increasing, and the vortex above the canopy had a greater presence in E0, F0 and G0.
However, in all the simulations, the order of magnitude of the velocities was lower than the experimental data, as Figure
4 showed. This vortex has special interest because it can determine the paths of the droplets circulating above the tree and
ending as atmospheric drift.
·6·
CIGR-AgEng conference
Jun. 26–29, 2016, Aarhus, Denmark
Figure 4. Experimental velocities (exp; red points) and results from simulations 1 (sim 1; black square) and 11 (sim 11:
green triangle) in posts D0, D+30 and D-30 for component U (left), V (center) and W (right).
Figure 5. Experimental velocities (exp; red points) and results from simulations 1 (sim 1; black square) and 11 (sim 11:
·7·
CIGR-AgEng conference
Jun. 26–29, 2016, Aarhus, Denmark
green triangle) in posts E0, F0 and G0 for component U (left), V (center) and W (right).
Figure 6. Streamlines for the vertical plane x = 0 cm (i, iii) and the horizontal plane y = 150 cm (ii, iv) for simulation 1 (i,
ii) and simulation 11 (iii, iv).
4. Conclusions
This paper presents a preliminary simulations to define the canopy characteristic to simulate phytosanitary treatments
with a 3D CFD model in orange trees. This work was based on a previous 2D model, where the tree in front of the fan
was considered as a solid body.
Simulations showed that if a porous subdomain was included around the solid body, it was possible to reproduce the
same turbulence structures measured in the experiments for elevated resistance values. But, simulated velocities had a
lower order of magnitude than in the field assays. In addition, in the case of the experimental vortex above the tree, it was
displaced in the simulation in a further position to the sprayer. It is necessary to follow studying the vegetation resistance,
varying more values for the inertial losses or checking new solid geometries for the first tree.
Other problem was the faster dispersion of the current for the fan inlet. Probably, a new design of the fan should be
reconsidered. One option is to use a tighter geometry that places the inlet air directly on its source. Air generation will
also have impact on the vortices around the tree.
More aspects need to be studied, as a sensitivity analysis of the mesh, a study of an appropriate turbulence model, the
addition of extra terms in the porous subdomain for the turbulent energy and dissipation or the adjustment of the wall
roughness.
Acknowledgements
This work was partially financed by the Spanish Ministry of Economy (projects AGL2007– 66093–C04–01 and
AGL2010–22304–C04–01) and the European Regional Development Fund (ERDF). Ramón Salcedo was recipient of a
postgraduate FPI-INIA scholarship.
References
·8·
CIGR-AgEng conference
Jun. 26–29, 2016, Aarhus, Denmark
Beketov, M.A., Kefford, B.J., Schäfer, R.B., Liess, M., 2013. Pesticides reduce regional biodiversity of stream
invertebrates. Proceedings of the Natural Academy of Science of the USA. 110, 11039-11043.
Catania, P., Inglese, P., Pipitone, F., Vallone, M. 2011. Assessment of the wind influence on spray application using an
artificial vineyard. European Journal of Horticultural Science. 76, 102-108.
Chen, Y., Ozkan, E., Zhu, H., Derksen, R., Krause, C.R., 2013. Spray deposition inside tree canopies from a newly
developed variable-rate air-assisted sprayer. Transactions of ASABE. 56, 1263-1272.
Da Silva, A., Sinfort, C., Tinet, C., Pierrat, D., Huberson, S., 2006. A Lagrangian model for spray behaviour within
vine canopies. Journal of Aerosol Science. 37, 658-674.
Damalas, C.A., 2015. Pesticide drift: Seeking reliable environmental indicators of exposure assessment, in: Armon,
R.H., Hänninen, O. (Eds.), Environmental Indicators. Dordrecht: Springer Netherlands, pp. 251-261.
Delele, M.A., Jaeken, P.,Debaer, C., Baetens, K., A. Melese Endalew, Ramon, H., Nicolaï, B.M., Verboven P., 2007.
CFD prototyping of fan air-assisted orchard sprayer aimed at drift reduction. Computers and Electronics in Agriculture.
55, 16-27.
Duga, A.T., Ruysen, K., Dekeyser, D., Nuyttens, D., Bylemans, D., Nicolai, B.M., Verboven, P., 2015. Spray
deposition profiles in pome fruit trees: Effects of sprayer design, training system and tree canopy characteristics. Crop
Protection. 67, 200-213.
Endalew, A.M., Debaer, C., Rutten, N., Vercammen, J., Delele, M.A., Ramon, H., Nicolaï, B.M., Verboven, P., 2010.
A new integrated CFD modelling approach towards air-assisted orchard spraying. Part II. Validation for different sprayer
types. Computers and Electronics in Agriculture. 71, 137-147.
Ferziger, J.H., Peric M., 2001. Computational methods for fluid dynamics, Berlin: Springer-Verlag.
Finnigan, J., 2000. Turbulence in plant canopies. Annu. Rev. Fluid. Mech. 32, 519-571.
Jensen, P.K., Olesen, M.H., 2014. Spray mass balance in pesticide applications: a review. Crop Protection. 61, 23-31.
García Ramos, F.J., Vidal, M., Bone, A., Malon, H., Aguirre, J., 2012. Analysis of the air flow generated by an airassisted sprayer equipped with two axial fans using a 3D sonic anemometer. Sensors. 12, 7598-7613.
Lee, I.B., Bitog, J.P.P., Hong, S.W., Seo, I.H., Kwon, K.S., Bartzanas, T., Kacira, M., 2013. The past, present and
future of CFD for agro-environmental applications. Computers and Electronics in Agriculture. 93, 168-183.
Menter, F.R. 1993. Zonal two equation k-turbulence models for aerodynamic flows. AIAA paper, 2906.
Salcedo, R., Granell, R., Garcerá, C., Palau, G., Moltó, E., Chueca P., 2012. CFD model of the effect of canopy on air
velocity in air-assisted treatments in mandarin orchards. In International Conference of Agricultural Engineering. IVIA,
Valencia, Spain, July 8-12. CIGR-EurAgEng. Eds., Juste, F. et al.
Salcedo, R., Granell, R., Palau, G., Vallet, A., Garcerá, C., Chueca, P., Moltó, E., 2015a. Design and validation of a
2D CFD model of the airflow produced by an airblast sprayer during pesticide treatments of citrus. Comput. Electron.
Agric. 116, 150-161.
Salcedo, R., Garcerá, C., Granell, R., Molto, E., Chueca, P. 2015b. Description of the airflow produced by an airassisted sprayer during pesticide applications to citrus. Spanish Journal of Agricultural Research.13, 02-08.
Sanz, C., 2003. A note on k-ε modelling of vegetation canopy air-flows. Boundary Layer-Meteorology. 108, 191-197.
·9·