Method for simulating air flow and fog droplet deposition in canopy and system thereof

Abstract

A method for simulating air flow and fog droplet deposition in a canopy and a system thereof is provided, and relates to the technical field of simulation in a canopy. The method comprises: determinin

Description

L IL VUlM 1CAL Jr IJtLJL / JLI

[0001] The present disclosure relates to the technical field of simulation in a canopy, in particular to a method for simulating air flow and fog droplet deposition in a canopy and a system thereof. BACKGROUND

[0002] In the process of agricultural sustainable development, the problem of plant diseases and insect pests has always been the focus of attention. As an important tool for insect disease prevention, a plant protection unmanned aerial vehicle has been widely concerned with its flexibility and convenience, and has made new progress in insect disease prevention. However, in the process of pesticide application by an unmanned aerial vehicle, the influence of crop canopy on the downwash airflow of the rotor cannot be ignored. The downwash airflow entrains the fog droplet for movement, which affects the spatial movement of fog droplets and their attachment and penetration in a canopy. Therefore, it is an important basis for evaluating the deposition effect of fog droplets in the canopy to clarify the air flow and its law when the plant protection unmanned aerial vehicle applies pesticide to fruit trees, and then the efficiency of pesticide application of the unmanned aerial vehicle can be improved in a targeted manner.

[0003] At present, Computational Fluid Dynamics (CFD) is used to simulate the airflow law in the canopy, comprising the following two methods. (1) Porous media-constructing virtual crops, in which because the resistance of the crop canopy will cause the momentum loss of downwash airflow, the method is to construct a virtual crop model through geometric modeling, construct mesh of a structure in the whole calculation domain, establish a virtual crop canopy in the structure mesh according to the actual measured position of the unmanned aerial vehicle with respect to the crop, and then set the resistance of a input flow field to the canopy in a porous medium area through Fluent emulation, and the main parameters are an inertial resistance coefficient and an viscous resistance coefficient. (2) Porous media-static porosity similarity method, in which a crop population is abstracted into layers of porous media planes with different porosities according to the inherent hierarchy and the orderliness formed by the fractal characteristics of the crop morphological structure, and sufficient surfaces with different optical porosities are combined according to their heights, so that the crop population with this porosity can be approximately described. Different static porosities are set to replace different crops or different growth morphology of crops. An emulation calculation model of unmanned aerial vehicle spray with different static porosities is established in CFD, and emulation calculation is carried out, so that the spatial distribution of fog droplets with different porosities can be obtained.

[0004] Because porous media only adds additional momentum loss to the momentum equation, the influence of the porous media on turbulence is only approximate, which will result in flow field distortion to some extent. It is impossible to accurately measure the airflow speed and fog droplet deposition, etc. inside the constructed virtual crop, that is, the turbulent distribution in the actual operation after the airflow passes through the crop cannot be reflected. However, the simulation effect of the static porosity similarity method is far from the actual operation, and it is impossible to simulate the real situation. Therefore, the accuracy of the conventional technology for simulating the air flow and fog droplet deposition inside the canopy is low, which leads to a poor effect in improving the efficiency of pesticide application by the unmanned aerial vehicle in a targeted manner. SUMMARY

[0005] The purpose of the present disclosure is to provide a method for simulating air flow and fog droplet deposition in a canopy and a system thereof, which realizes the simulation of air flow and fog droplet deposition in the canopy.

[0006] In order to achieve the above purpose, the present disclosure provides the following solution:

[0007] A method for simulating air flow and fog droplet deposition in a canopy is provided, wherein the method comprises:

[0008] establishing an unmanned aerial vehicle rotor source model and a fruit tree canopy model;

[0009] determining a rotation domain of an unmanned aerial vehicle rotor according to the unmanned aerial vehicle rotor source model;

[0010] determining a canopy domain of a fruit tree according to the fruit tree canopy model;

[0011] determining a calculation domain according to the rotation domain and the canopy domain;

[0012] establishing a fluid geometric model according to the rotation domain, the canopy domain and the calculation domain;

[0013] meshing the fluid geometric model to obtain a fluid mesh model;

[0014] setting, by using a Fluent software, surfaces in the calculation domain except a bottom surface as a pressure outlet, setting the bottom surface as a wall, setting a rotational speed of the unmanned aerial vehicle rotor according to a payload of an unmanned aerial vehicle where the unmanned aerial vehicle rotor source model is located, setting a center and a direction of the rotation domain, establishing a discrete phase model regarding movement of the fog droplet, determining that a nozzle is located directly below the unmanned aerial vehicle rotor with a distance of 0.1m to 0.5m from the unmanned aerial vehicle rotor, an injection half-angle is 10 degrees to 90 degrees, the injection flow rate is 0.005kg / s to 0.02kg / s, an release time is 0s to 10s, and a hole width is 0.0005m to 0.001m, determining that a turbulence model is SST k-ro, setting a solution method as double precision, and determining that an iteration method is coupling iteration;

[0015] meshing the canopy domain to obtain a solid mesh model;

[0016] transforming, by using a LS-DYNA software, an area where the solid mesh model is located into a particle model, setting particle parameters including density, type and size, setting a contact surface between particles of the solid domain and grids of the fluid domain, setting a total number and a generating rate of particles, setting a generating mode of particles as a particle dynamic generating mode, specifying a number of particles generated and a number of grids of the canopy domain to be same during emulation, and setting a coupling interface between a peripheral wall and the fluid mesh model to be complete analytic;

[0017] setting a number of emulation steps, a time represented by each step and the emulation end condition;

[0018] repeatedly executing an emulation process until the emulation end condition is met, and outputting a simulation result of air flow and fog droplet deposition in the canopy; in which the emulation process comprises: solving a result of the fluid mesh model within one emulation step in Fluent environment, and solving the result of the particle model within one emulation step in LS-DYNA environment; and carrying out data exchange between the result of the fluid mesh model and the result of the solid mesh model through the coupling interface.

[0019] Preferably, the establishing an unmanned aerial vehicle rotor source model specifically comprises:

[0020] acquiring structural parameters of the unmanned aerial vehicle rotor, wherein the structural parameters comprise an outer diameter, a pitch, a thickness and an inclination angle;

[0021] establishing the unmanned aerial vehicle rotor source model according to the structural parameters.

[0022] Preferably, the establishing a fruit tree canopy model specifically comprises:

[0023] acquiring fruit tree parameters; wherein the fruit tree parameters comprise a lowest point of the canopy, the a highest point of the canopy, an outer diameter of the canopy and a density of the canopy;

[0024] establishing the fruit tree canopy model according to the fruit tree parameters.

[0025] A system for simulating air flow and fog droplet deposition in a canopy is provided, wherein the system comprises:

[0026] a model establishing module, which is configured to establish an unmanned aerial vehicle rotor source model and a fruit tree canopy model;

[0027] a rotation domain determining module, which is configured to determine a rotation domain of an unmanned aerial vehicle rotor according to the unmanned aerial vehicle rotor source model;

[0028] a canopy domain determining module, which is configured to determine a canopy domain of a fruit tree according to the fruit tree canopy model;

[0029] a calculation domain determining module, which is configured to determine a calculation domain according to the rotation domain and the canopy domain;

[0030] a fluid geometric model determining module, which is configured to establish a fluid geometric model according to the rotation domain, the canopy domain and the calculation domain;

[0031] a fluid mesh model determining module, which is configured to carry out mesh division on the fluid geometric model to obtain a fluid mesh model;

[0032] a first setting module, which is configured to set, by using a Fluent software, surfaces in the calculation domain except a bottom surface as a pressure outlet, set the bottom surface as a wall, set a rotational speed of the unmanned aerial vehicle rotor according to a payload of an unmanned aerial vehicle where the unmanned aerial vehicle rotor source model is located, set a center and a direction of the rotation domain, establish a discrete phase model regarding movement of the fog droplet, determine that a nozzle is located directly below the unmanned aerial vehicle rotor with a distance of 0.1m to 0.5m from the unmanned aerial vehicle rotor, an injection half-angle is 10 degrees to 90 degrees, an injection flow rate is 0.005kg / s to 0.02kg / s, a release time is 0s to 10s, and the hole width is 0.0005m to 0.001m, determine that a turbulence model is SST k-ro, set a solution method as double precision, and determine that an iteration method is coupling iteration;

[0033] a solid mesh model determining module, which is configured to mesh the canopy domain to obtain a solid mesh model;

[0034] a second setting module, which is configured to transform, by using a LS-DYNA software, an area where the solid mesh model is located into a particle model, set particle parameters including density, type and size, set a contact surface between particles of the solid domain and grids of the fluid domain, set a total number and a generating rate of particles, set the generating mode of particles as a particle dynamic generating mode, specify a number of particles generated and the number of grids of the canopy domain to be same during emulation, and set a coupling interface between a peripheral wall and the fluid mesh model to be complete analytic;

[0035] a third setting module, which is configured to set a number of emulation steps, a time represented by each step and the emulation end condition;

[0036] an emulating module, which is configured to repeatedly execute an emulation process until the emulation end condition is met, and output a simulation result of air flow and fog droplet deposition in the canopy; in which the emulation process comprises: solving a result of the fluid mesh model within one emulation step in Fluent environment, and solving a result of the particle model within one emulation step in LS-DYNA environment; and carrying out data exchange between the result of the fluid mesh model and the result of the solid mesh model through the coupling interface.

[0037] Preferably, the model establishing module comprises an unmanned aerial vehicle rotor source model establishing submodule, and the unmanned aerial vehicle rotor source model establishing submodule specifically comprises:

[0038] a structural parameter acquisition unit, which is configured to acquire structural parameters of the unmanned aerial vehicle rotor, wherein the structural parameters comprise an outer diameter, a pitch, a thickness and an inclination angle;

[0039] an unmanned aerial vehicle rotor source model establishing unit, which is configured to establish the unmanned aerial vehicle rotor source model according to the structural parameters.

[0040] Preferably, the model establishing module comprises a fruit tree canopy model establishing submodule, and the fruit tree canopy model establishing submodule specifically comprises:

[0041] a fruit tree parameter acquisition unit, which is configured to acquire fruit tree parameters; wherein the fruit tree parameters comprise the lowest point of the canopy, the highest point of the canopy, an outer diameter of the canopy and a density of the canopy;

[0042] a fruit tree canopy model establishing unit, which is configured to establish the fruit tree canopy model according to the fruit tree parameters.

[0043] The present disclosure discloses a method for simulating air flow and fog droplet deposition in a canopy and a system thereof, wherein the method comprises: establishing an unmanned aerial vehicle rotor source model and a fruit tree canopy model; determining a rotation domain of an unmanned aerial vehicle rotor according to the unmanned aerial vehicle rotor source model; determining a canopy domain of a fruit tree according to the fruit tree canopy model; determining a calculation domain according to the rotation domain and the canopy domain; establishing a fluid geometric model according to the rotation domain, the canopy domain and the calculation domain; meshing the fluid geometric model to obtain a fluid mesh model; setting, by using a Fluent software, the surfaces in the calculation domain except the bottom surface as a pressure outlet, setting the bottom surface as a wall, setting the rotational speed of the unmanned aerial vehicle rotor according to the payload of the unmanned aerial vehicle where the unmanned aerial vehicle rotor source model is located, setting the center and the direction of the rotation domain, establishing a discrete phase model regarding movement of fog droplet, determining that a nozzle is located directly below the unmanned aerial vehicle rotor with a distance of 0.1m to 0.5m from the unmanned aerial vehicle rotor, the injection half-angle is 10 degrees to 90 degrees, the injection flow rate is 0.005kg / s to 0.02kg / s, the release time is Os to 10s, and the hole width is 0.0005m to 0.001m, determining that a turbulence model is SST k-co, setting a solution method as double precision, and determining that an iteration method is coupling iteration; meshing the canopy domain to obtain a solid mesh model; transforming, by using a LS-DYNA software, the area where the solid mesh model is located into a particle model, setting particle parameters including density, type and size, setting the contact surface between particles of the solid domain and grids of the fluid domain, setting the total number and the generating rate of particles, setting the generating mode of particles as a particle dynamic generating mode, specifying the number of particles generated and the number of grids of the canopy domain to be same during emulation, and setting a coupling interface between a peripheral wall and the fluid mesh model to be complete analytic; setting the number of emulation steps, the time represented by each step and the emulation end condition; repeatedly executing the emulation process until the emulation end condition is met, and outputting the simulation result of air flow and fog droplet deposition in the canopy; in which the emulation process comprises: solving the result of the fluid mesh model within one emulation step in Fluent environment, and solving the result of the particle model within one emulation step in LS-DYNA environment; and carrying out data exchange between the result of the fluid mesh model and the result of the solid mesh model through the coupling interface. The present disclosure realizes the simulation of air flow and fog droplet deposition in the canopy. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to explain the embodiments of the present disclosure or the technical solutions in the prior art more clearly, the drawings that need to be used in the embodiments will be briefly introduced. Apparently, the drawings in the following description are only some embodiments of the present disclosure. For those skilled in the art, other drawings can be obtained according to these drawings without creative labor.

[0045] FIG. 1 is a schematic flow diagram of a method for simulating air flow and fog droplet deposition in a canopy according to an embodiment of the present disclosure;

[0046] FIG. 2 is a schematic diagram of a calculation domain, a canopy domain and a rotation domain;

[0047] FIG. 3 is a schematic diagram of a particle model; and

[0048] FIG. 4 is a schematic structural diagram of a system for simulating air flow and fog droplet deposition in a canopy according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] The technical solutions in the embodiments of the present disclosure will be clearly and completely described with reference to the drawings in the embodiments of the present disclosure hereinafter. Apparently, the described embodiments are only some embodiments of the present disclosure, rather than all of the embodiments. Based on the embodiment of the present disclosure, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present disclosure.

[0050] The purpose of the present disclosure is to provide a method for simulating air flow and fog droplet deposition in a canopy and a system thereof, aiming at realizing the simulation of air flow and fog droplet deposition in the canopy.

[0051] In order to make the above objects, features and advantages of the present disclosure more readily understandable, the present disclosure will be explained in further detail with reference to the drawings and specific implementation hereinafter.

[0052] FIG. 1 is a schematic flow chart of a method for simulating air flow and fog droplet deposition in a canopy according to an embodiment of the present disclosure. As shown in FIG. 1, in this embodiment, a method for simulating air flow and fog droplet deposition in a canopy is provided. The method comprises the following steps.

[0053] In Step 101, an unmanned aerial vehicle rotor source model and a fruit tree canopy model are established.

[0054] In Step 102, a rotation domain of an unmanned aerial vehicle rotor is determined according to the unmanned aerial vehicle rotor source model.

[0055] In Step 103, a canopy domain of a fruit tree is determined according to the fruit tree canopy model.

[0056] In Step 104, a calculation domain is determined according to the rotation domain and the canopy domain.

[0057] Specifically, as shown in FIG. 2, the calculation domain wraps the rotation domain and the canopy domain. The length, width and height of the projection area of the calculation domain are 3 to 5 times the length, width and height of the projection area of the canopy domain, so that the rotation domain can be completely wrapped, and the canopy domain is located in the center of the calculation domain. The calculation domain is usually set as a region such as a cuboid or a cylinder, and the size of the calculation domain needs to leave enough space for the development of air flow. The airflow flows in the set calculation domain, and theoretically the larger the better. However, as the calculation domain becomes larger, the more calculation resources are required, so that the waste of calculation resources should also be considered.

[0058] In Step 105, a fluid geometric model is established according to the rotation domain, the canopy domain and the calculation domain.

[0059] In Step 106, the fluid geometric model is meshed to obtain a fluid mesh model.

[0060] In Step 107, Fluent software is adopted to set requirements for the fluid mesh model.

[0061] The Step 107 specifically comprises the following steps. By using Fluent software, the surfaces in the calculation domain except the bottom surface are set as pressure outlets, the bottom surface is set as a wall, a rotational speed (1500 rpm to 3000 rpm) of the unmanned aerial vehicle rotor is set according to the payload (5 kg to 15 kg) of the unmanned aerial vehicle to which the unmanned aerial vehicle rotor source model is directed, a center and a direction of the rotation domain are set, and a discrete phase model of fog droplet movement is established; it is determined that a nozzle is located directly below the unmanned aerial vehicle rotor with a distance of 0.1m to 0.5m from the unmanned aerial vehicle rotor, an injection half-angle is 10 degrees to 90 degrees, an injection flow rate is 0.005kg / s to 0.02kg / s, a release time is 0s to 10s, and a hole width is 0.0005m to 0.001m; a turbulence model is determined as SST k-©, a solution method is set as double precision, and an iteration method is determined as coupling iteration.

[0062] For example, by using Fluent software, the surfaces in the calculation domain except the bottom surface are set as pressure outlets, the bottom surface is set as a wall, a rotational speed of the unmanned aerial vehicle rotor is set as 1500 rpm to 2500 rpm. A center and a direction of the rotation domain are set, and a discrete phase model of fog droplet movement is established. It is determined that a nozzle is located directly below the unmanned aerial vehicle rotor with a distance of 0.25m from the unmanned aerial vehicle rotor, the injection half-angle is 55 degrees, the injection flow rate is O.OO833kg / s, the release time is Os to 10s, and the hole width is 0.0007. a turbulence model is determined as SST k-©, a solution method is set as double precision, and an iteration method is determined as coupling iteration. The nozzle position can be adjusted according to the actual situation, ranging from 0.1m to 0.5m. The injection half-angle can be adjusted according to the actual situation, ranging from 10 degrees to 90 degrees. The injection flow rate can be adjusted according to the actual situation of the water pump of the nozzle. The injection angle can be in the range of 0.005kg / s to 0.02kg / s. The hole width can be adjusted according to the actual situation of the nozzle.

[0063] Specifically, the other surfaces in the calculation domain except the bottom surface are set as pressure outlets (in Fluent software: pressure outlet) with an initial value of 0. The bottom surface is set as a wall (in Fluent software: wall). The direction of the rotation domain is clockwise or counterclockwise, which can be set according to the actual situation of rotor distribution.

[0064] In Step 108, the canopy domain is meshed to obtain a solid mesh model.

[0065] In Step 109, LS-DYNA software is adopted to transform a solid mesh model into a particle model and the requirements are set.

[0066] The Step 109 specifically comprises the following steps. By using LS-DYNA software, the area where the solid mesh model is located is transformed into a particle model, particle parameters including density, type and size are set, the contact surfaces between particles of the solid domain and grids of the fluid domain are set, the total number and the generating rate of particles are set, the generating mode of particles is set as a particle dynamic generating mode. The amount of particles generated and the number of grids of the canopy domain are specified to be same during emulation, and a coupling interface between a peripheral wall and the fluid mesh model is set to be complete analytic. As shown in FIG. 3, the particle model is a Smoothed Particle Hydrodynamics (that is, SPH) model.

[0067] Specifically, according to the comparison table of SPH particle parameters, the density can be approximately obtained from the actual canopy density, and the type and the size can be divided into the upper, middle and lower layers according to the actual situation, and can be approximately obtained from the maturity and hardness of canopy leaves.

[0068] In Step 110, the number of emulation steps, the time represented by each step and the emulation end condition are set.

[0069] Specifically, the number of emulation steps, the time represented by each step and the emulation end condition can be set according to the actual situation. For example, the number of emulation steps is set to be long enough to make the emulation results converge, for example, 4000 to 10000 steps. The time represented by each emulation step is set, for example, 0.001 second. The emulation end condition (that is, the emulation residual convergence condition) is set, so that the emulation comes to an end after the emulation residual is less than 0.00001.

[0070] In Step 111, the emulation process is repeatedly executed until the emulation end condition is met, and the simulation results of air flow and fog droplet deposition in the canopy is output. The emulation process comprises the following steps. The result of the fluid mesh model within one emulation step is solved in Fluent environment, and the result of the particle model within one emulation step is solved in LS-DYNA environment; and the data exchange is carried out between the result of the fluid mesh model and the result of the solid mesh model through the coupling interface.

[0071] As an optional embodiment, the process of establishing the unmanned aerial vehicle rotor source model specifically comprises the following steps.

[0072] First, structural parameters of the unmanned aerial vehicle rotor are acquired, and the structural parameters comprise an outer diameter, a pitch, a thickness and an inclination angle.

[0073] Then, the unmanned aerial vehicle rotor source model is established according to the structural parameters.

[0074] As an optional embodiment, the process of establishing the fruit tree canopy model specifically comprises the following steps.

[0075] First, fruit tree parameters are acquired, and the fruit tree parameters comprise the lowest point of the canopy, the highest point of the canopy, the outer diameter of the canopy and the density of the canopy.

[0076] Then, the fruit tree canopy model is established according to the fruit tree parameters.

[0077] FIG. 4 is a schematic structural diagram of a system for simulating air flow and fog droplet deposition in a canopy according to an embodiment of the present disclosure. As shown in FIG. 4, in this embodiment, a system for simulating air flow and fog droplet deposition in a canopy is provided, comprising:

[0078] a model establishing module 201, which is configured to establish an unmanned aerial vehicle rotor source model and a fruit tree canopy model;

[0079] a rotation domain determining module 202, which is configured to determine a rotation domain of an unmanned aerial vehicle rotor according to the unmanned aerial vehicle rotor source model;

[0080] a canopy domain determining module 203, which is configured to determine a canopy domain of a fruit tree according to the fruit tree canopy model;

[0081] a calculation domain determining module 204, which is configured to determine a calculation domain according to the rotation domain and the canopy domain;

[0082] a fluid geometric model determining module 205, which is configured to establish a fluid geometric model according to the rotation domain, the canopy domain and the calculation domain;

[0083] a fluid mesh model determining module 206, which is configured to mesh the fluid geometric model to obtain a fluid mesh model;

[0084] a first setting module 207, which is configured to use Fluent software, set the surfaces in the calculation domain except the bottom surface as pressure outlets, set the bottom surface as a wall, set the rotational speed of the unmanned aerial vehicle rotor according to the payload of the unmanned aerial vehicle where the unmanned aerial vehicle rotor source model is located, set the center and the direction of the rotation domain, establish a discrete phase model of fog droplet movement, determine that a nozzle is located 0.1m to 0.5m directly below the unmanned aerial vehicle rotor, the injection half-angle is 10 degrees to 90 degrees, the injection flow rate is 0.005kg / s to 0.02kg / s, the release time is 0s to 10s, and the hole width is 0.0005m to 0.001m, determine that a turbulence model is SST k-w, set a solution method as double precision, and determine that an iteration method is coupling iteration;

[0085] a solid mesh model determining module 208, which is configured to mesh the canopy domain to obtain a solid mesh model;

[0086] a second setting module 209, which is configured to use LS-DYNA software, transform the area where the solid mesh model is located into a particle model, set particle parameters including density, type and size, set the contact surface between particles of the solid domain and grids of the fluid domain, set the total number and the generating rate of particles, set the generating mode of particles as a particle dynamic generating mode, specify the amount of particles generated and the number of grids of the canopy domain to be same during emulation, and set a coupling interface between a peripheral wall and the fluid mesh model as complete analytical;

[0087] a third setting module 210, which is configured to set the number of emulation steps, the time represented by each step and the emulation end condition;

[0088] an emulating module 211, which is configured to repeatedly execute the emulation process until the emulation end condition is met, and output the simulation results of air flow and fog droplet deposition in the canopy; in which the emulation process comprises: solving the result of the fluid mesh model with an emulation step in Fluent environment, and solving the result of the particle model with an emulation step in LS-DYNA environment; and carrying out data exchange between the result of the fluid mesh model and the result of the solid mesh model through the coupling interface.

[0089] As an optional embodiment, the model establishing module 201 comprises an source model establishing submodule of the unmanned aerial vehicle rotor, and the source model establishing submodule specifically comprises:

[0090] a structural parameter acquisition unit, which is configured to acquire structural parameters of the unmanned aerial vehicle rotor, wherein the structural parameters comprise an outer diameter, a pitch, a thickness and an inclination angle;

[0091] an source model establishing unit of the unmanned aerial vehicle rotor, which is configured to establish the unmanned aerial vehicle rotor source model according to the structural parameters.

[0092] As an optional embodiment, the model establishing module 201 comprises a fruit tree canopy model establishing submodule, and the fruit tree canopy model establishing submodule specifically comprises:

[0093] a fruit tree parameter acquisition unit, which is configured to acquire fruit tree parameters; wherein the fruit tree parameters comprise the lowest point of the canopy, the highest point of the canopy, the outer diameter of the canopy and the density of the canopy;

[0094] a fruit tree canopy model establishing unit, which is configured to establish the fruit tree canopy model according to the fruit tree parameters.

[0095] In this specification, various embodiments are described in a progressive way. The differences between each embodiment and other embodiments are highlighted, and the same and similar parts of various embodiments can be referred to each other. Since the system disclosed in the embodiment corresponds to the method disclosed in the embodiment, the device is described simply. Refer to the description of the method for the relevant points.

[0096] In the present disclosure, specific examples are applied to illustrate the principle and implementation of the present disclosure, and the explanations of the above embodiments are only used to help understand the method and core ideas of the present disclosure. At the same time, according to the idea of the present disclosure, there will be some changes in the specific implementation and application scope for those skilled in the art. To sum up, the contents of the specification should not be construed as limiting the present disclosure.

Claims

1. A method for simulating air flow and fog droplet deposition in a canopy, wherein the method comprises:establishing an unmanned aerial vehicle rotor source model and a fruit tree canopy model;determining a rotation domain of an unmanned aerial vehicle rotor according to the unmanned aerial vehicle rotor source model;determining a canopy domain of a fruit tree according to the fruit tree canopy model;determining a calculation domain according to the rotation domain and the canopy domain;establishing a fluid geometric model according to the rotation domain, the canopy domain and the calculation domain;meshing the fluid geometric model to obtain a fluid mesh model;setting, by using a Fluent software, surfaces in the calculation domain except a bottom surface as a pressure outlet, setting the bottom surface as a wall, setting a rotational speed of the unmanned aerial vehicle rotor according to a payload of an unmanned aerial vehicle where the unmanned aerial vehicle rotor source model is located, setting a center and a direction of the rotation domain, establishing a discrete phase model regarding movement of the fog droplet, determining that a nozzle is located directly below the unmanned aerial vehicle rotor with a distance of 0.1m to 0.5m from the unmanned aerial vehicle rotor, an injection half-angle is 10 degrees to 90 degrees, an injection flow rate is 0.005kg / s to 0.02kg / s, a release time is Os to 10s, and the hole width is 0.0005m to 0.001m, determining that a turbulence model is SST k-co, setting a solution method as double precision, and determining that an iteration method is coupling iteration;meshing the canopy domain to obtain a solid mesh model;transforming, by using a LS-DYNA software, an area where the solid mesh model is located into a particle model, setting particle parameters including density, type and size, setting a contact surface between particles of the solid domain and grids of the fluid domain, setting a total number and a generating rate of particles, setting a generating mode of particles as a particle dynamic generating mode, specifying a number of particles generated and a number of grids of the canopy domain to be same during emulation, and setting a coupling interface between a peripheral wall and the fluid mesh model to be complete analytic;setting a number of emulation steps, a time represented by each step and the emulation end condition;repeatedly executing an emulation process until the emulation end condition is met, andoutputting a simulation result of air flow and fog droplet deposition in the canopy; wherein the emulation process comprises: solving a result of the fluid mesh model within one emulation step in a Fluent environment, and solving a result of the particle model within one emulation step in a LS-DYNA environment; and carrying out data exchange between the result of the fluid mesh model and the result of the solid mesh model through the coupling interface.

2. The method according to claim I, wherein the establishing an unmanned aerial vehicle rotor source model comprises:acquiring structural parameters of the unmanned aerial vehicle rotor, wherein the structural parameters comprise an outer diameter, a pitch, a thickness and an inclination angle;establishing the unmanned aerial vehicle rotor source model according to the structural parameters.

3. The method according to claim 1, wherein the establishing a fruit tree canopy model comprises:acquiring fruit tree parameters; wherein the fruit tree parameters comprise a lowest point of the canopy, a highest point of the canopy, an outer diameter of the canopy and a density of the canopy;establishing the fruit tree canopy model according to the fruit tree parameters.

4. A system for simulating air flow and fog droplet deposition in a canopy, wherein the system comprises:a model establishing module, configured to establish an unmanned aerial vehicle rotor source model and a fruit tree canopy model;a rotation domain determining module, configured to determine a rotation domain of an unmanned aerial vehicle rotor according to the unmanned aerial vehicle rotor source model;a canopy domain determining module, configured to determine a canopy domain of a fruit tree according to the fruit tree canopy model;a calculation domain determining module, configured to determine a calculation domain according to the rotation domain and the canopy domain;a fluid geometric model determining module, configured to establish a fluid geometric model according to the rotation domain, the canopy domain and the calculation domain;a fluid mesh model determining module, configured to mesh the fluid geometric model to obtain a fluid mesh model;a first setting module, configured to set, by using a Fluent software, surfaces in the calculation domain except a bottom surface as a pressure outlet, set the bottom surface as a wall, set a rotational speed of the unmanned aerial vehicle rotor according to a payload of anunmanned aerial vehicle where the unmanned aerial vehicle rotor source model is located, set a center and a direction of the rotation domain, establish a discrete phase model regarding movement of the fog droplet, determine that a nozzle is located directly below the unmanned aerial vehicle rotor with a distance of 0.1m to 0.5m from the unmanned aerial vehicle rotor, an injection half-angle is 10 degrees to 90 degrees, an injection flow rate is 0.005kg / s to 0.02kg / s, a release time is Os to 10s, and the hole width is 0.0005m to 0.001m, determine that a turbulence model is SST k-m, set a solution method as double precision, and determine that an iteration method is coupling iteration;a solid mesh model determining module, configured to mesh the canopy domain to obtain a solid mesh model;a second setting module, configured to transform, by using a LS-DYNA software, an area where the solid mesh model is located into a particle model, set particle parameters including density, type and size, set a contact surface between particles of the solid domain and grids of the fluid domain, set a total number and a generating rate of particles, set the generating mode of particles as a particle dynamic generating mode, specify a number of particles generated and the number of grids of the canopy domain to be same during emulation, and set a coupling interface between a peripheral wall and the fluid mesh model to be complete analytic;a third setting module, configured to set a number of emulation steps, a time represented by each step and the emulation end condition;an emulating module, configured to repeatedly execute an emulation process until the emulation end condition is met, and output a simulation result of air flow and fog droplet deposition in the canopy; wherein the emulation process comprises: solving a result of the fluid mesh model within one emulation step in a Fluent environment, and solving a result of the particle model within one emulation step in a LS-DYNA environment; and carrying out data exchange between the result of the fluid mesh model and the result of the solid mesh model through the coupling interface.

5. The system according to claim 4, wherein the model establishing module comprises an unmanned aerial vehicle rotor source model establishing submodule, and the unmanned aerial vehicle rotor source model establishing submodule comprises:a structural parameter acquisition unit, configured to acquire structural parameters of the unmanned aerial vehicle rotor, wherein the structural parameters comprise an outer diameter, a pitch, a thickness and an inclination angle;an unmanned aerial vehicle rotor source model establishing unit, configured to establish the unmanned aerial vehicle rotor source model according to the structural parameters.

6. The system according to claim 4, wherein the model establishing module comprises a fruit tree canopy model establishing submodule, and the fruit tree canopy model establishing submodule comprises:a fruit tree parameter acquisition unit, configured to acquire fruit tree parameters; wherein the fruit tree parameters comprise a lowest point of the canopy, a highest point of the canopy, an outer diameter of the canopy and a density of the canopy;a fruit tree canopy model establishing unit, configured to establish the fruit tree canopy model according to the fruit tree parameters.

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