Droplet distribution information determination method and apparatus, electronic device, and storage medium

Abstract

A method and device for determining droplet distribution information, electronic equipment and storage medium, the method for determining droplet distribution information comprises: obtaining neighborhood information of a to-be-tested microcluster in a current cloud and fog area;Based on the neighborhood information, it is judged whether the to-be-tested microcluster will break into sub-droplets;If the to-be-tested microcluster will break into sub-droplets, the motion information of each sub-droplet formed by the breakage of the to-be-tested microcluster is calculated to obtain the current distribution information of each sub-droplet;If the to-be-tested microcluster will not break into sub-droplets, the motion information of the to-be-tested microcluster is calculated to obtain the current distribution information of the to-be-tested microcluster.The present application can improve the accuracy of cloud and fog area topography simulation.

Description

Technical Field

[0001] This application relates to the field of data processing, specifically to a method, apparatus, electronic device, and storage medium for determining droplet distribution information. Background Technology

[0002] Under the influence of aerodynamic forces, droplets undergo aerodynamic fragmentation during high-speed dispersion, breaking into numerous small droplet particles and forming a cloud-like region. Investigating the distribution morphology of this cloud-like region can have a positive impact on both natural phenomena and practical engineering problems related to aerodynamic dispersion processes. For example, in the detonation of fuel-air explosives (FAE), obtaining information on the cloud-like region formed by fuel under aerodynamic forces can guide secondary ignition schemes and optimize the detonation damage effect of the cloud-like region.

[0003] During the simulation of the expansion motion of the cloud and fog region, the distribution of droplets when each droplet breaks up in the cloud and fog region can be simulated, thereby obtaining the distribution of droplets in the cloud and fog region and the evolution process of the cloud and fog region.

[0004] However, the morphology of the cloud and fog area simulated by the above method is not accurate. Summary of the Invention

[0005] In view of the above, embodiments of this application provide a method, apparatus, electronic device, and storage medium for determining droplet distribution information, which can accurately simulate the distribution morphology of cloud and fog areas.

[0006] One embodiment of this application provides a method for determining droplet distribution information, comprising: acquiring neighborhood information of a micro-particle to be tested in a current cloud / fog area; determining, based on the neighborhood information, whether the micro-particle to be tested will break into sub-droplets; if the micro-particle to be tested will break into sub-droplets, calculating the motion information of each sub-droplet formed by the breakup of the micro-particle to be tested, and obtaining the current distribution information of each sub-droplet; if the micro-particle to be tested will not break into sub-droplets, calculating the motion information of the micro-particle to be tested, and obtaining the current distribution information of the micro-particle to be tested.

[0007] This technical solution can determine whether the micro-particles to be tested in the cloud area have broken down by combining the influence of the surrounding microparticles on the micro-particles to be tested. Then, based on the judgment result, the motion trajectory of the micro-particles to be tested or the sub-droplets of the micro-particles to be tested is simulated to obtain distribution information, thereby accurately simulating the morphology of the cloud area.

[0008] In some embodiments, determining whether the micro-particle to be tested will break into sub-droplets based on the neighborhood information includes: determining the shielding effect of each particle in the neighborhood on the micro-particle to be tested based on the neighborhood information, and obtaining the breakage probability of the micro-particle to be tested; and determining whether the micro-particle to be tested will break into sub-droplets based on the breakage probability of the micro-particle to be tested.

[0009] In some embodiments, determining the shielding effect of each particle in the neighborhood on the micro-cluster under test based on the neighborhood information, and obtaining the breakage probability of the micro-cluster under test, includes: calculating the total volume of each particle in the neighborhood of the micro-cluster under test; obtaining the neighborhood volume based on the neighborhood information; calculating the ratio of the total volume of each particle to the neighborhood volume to obtain a target local volume fraction; obtaining a target relationship model characterizing the relationship between the breakage probability and the local volume fraction; and inputting the target local volume fraction into the target relationship model to obtain the breakage probability of the micro-cluster under test.

[0010] In some embodiments, determining the shielding effect of each particle in the neighborhood on the micro-cluster to be tested based on the neighborhood information, and obtaining the breakage probability of the micro-cluster to be tested, includes: calculating the total volume of each particle in the neighborhood of the micro-cluster to be tested; obtaining the neighborhood volume based on the neighborhood information; calculating the ratio of the total volume of each particle to the neighborhood volume to obtain a target local volume fraction; obtaining a target relational model characterizing the relationship between the breakage probability and the local volume fraction; and inputting the target local volume fraction into the target relational model to obtain the breakage probability of the micro-cluster to be tested.

[0011] In some embodiments, obtaining a target relational model characterizing the relationship between the breakage probability and the local volume fraction includes: calculating the Weber number of the microcluster to be tested; inputting the Weber number into a preset first relational model to obtain a first local volume fraction when the breakage probability is one; inputting the Weber number into a preset second relational model to obtain a second local volume fraction when the breakage probability is zero; and determining the target relational model based on the first local volume fraction and the second local volume fraction.

[0012] In some embodiments, determining whether the micro-group to be tested will break into sub-droplets based on the breakage probability of the micro-group to be tested includes: if the breakage probability is within a preset first probability range, determining that the micro-group to be tested will break into sub-droplets; if the breakage probability is within a preset second probability range, determining that the micro-group to be tested will not break into sub-droplets, wherein the minimum breakage probability in the first probability range is not less than the maximum breakage probability in the second probability range.

[0013] In some embodiments, calculating the motion information of the micro-element to be measured to obtain its distribution information includes: acquiring the drag force applied to the micro-element to be measured, the mass of the micro-element to be measured, and the initial velocity of the micro-element to be measured; determining the Newtonian equation of motion of the micro-element to be measured based on the drag force and the mass of the micro-element to be measured; determining the acceleration of the micro-element to be measured based on the Newtonian equation of motion of the micro-element to be measured; and determining the current position and current velocity of the micro-element to be measured based on the acceleration and the initial velocity to obtain the current distribution information of the micro-element to be measured.

[0014] In some embodiments, calculating the motion information of each sub-droplet formed by the breakup of the micro-group to be tested, and obtaining the current distribution information of each sub-droplet, includes: acquiring the initial velocity of the micro-group to be tested; acquiring the dispersion angle, the mass of each sub-droplet, and the drag force applied to each sub-droplet, wherein the dispersion angle is the angle between the initial velocity direction of the micro-group to be tested and the initial velocity direction of each sub-droplet; determining the initial velocity of each sub-droplet based on the initial velocity and the dispersion angle; determining the Newtonian equation of motion corresponding to each sub-droplet based on the drag force on each sub-droplet and the mass of each sub-droplet; determining the acceleration of each sub-droplet based on the Newtonian equation of motion corresponding to each sub-droplet; and determining the current position and current velocity of each sub-droplet based on the acceleration and the initial velocity of each sub-droplet, thereby obtaining the current distribution information of each sub-droplet.

[0015] One embodiment of this application also provides a device for determining droplet distribution information, comprising: an acquisition module for acquiring neighborhood information of a micro-particle to be tested in a current cloud / fog area; a judgment module for determining, based on the neighborhood information, whether the micro-particle to be tested will break into sub-droplets; and a calculation module for calculating, if the micro-particle to be tested will break into sub-droplets, the motion information of each sub-droplet formed by the breakup of the micro-particle to be tested, to obtain the current distribution information of each sub-droplet; and if the micro-particle to be tested will not break into sub-droplets, the motion information of the micro-particle to be tested, to obtain the current distribution information of the micro-particle to be tested.

[0016] One embodiment of this application also provides an electronic device, which includes a processor and a memory. The memory is used to store instructions, and the processor is used to call the instructions in the memory to cause the electronic device to execute the above-described method for determining droplet distribution information.

[0017] One embodiment of this application also provides a computer-readable storage medium that stores computer instructions that, when executed on an electronic device, cause the electronic device to perform the above-described method for determining droplet distribution information. Attached Figure Description

[0018] Figure 1 This is a flowchart of the steps of a method for determining droplet distribution information provided in an embodiment of this application;

[0019] Figure 2 This is a schematic diagram of the neighborhood range of the micro-group to be tested provided in an embodiment of this application;

[0020] Figure 3 This is a schematic diagram illustrating the bag-packing and shearing breakage of micro-clusters provided in an embodiment of this application;

[0021] Figure 4 This is a schematic diagram illustrating the shielding effect of upstream and downstream droplets as a function of dimensionless spacing s / d and Weber number, according to an embodiment of this application.

[0022] Figure 5 This is a schematic diagram illustrating the variation of downstream droplet breakup modes with the dimensionless distance between two droplets at different Weber numbers, provided in an embodiment of this application.

[0023] Figure 6 This is a flowchart of a sub-step of step 102 provided in an embodiment of this application;

[0024] Figure 7 This is a flowchart of a sub-step of step 1022 provided in an embodiment of this application;

[0025] Figure 8 This is a schematic diagram of the structure of an experimental apparatus provided in one embodiment of this application;

[0026] Figure 9 This is a schematic diagram of a target relation model provided in an embodiment of this application;

[0027] Figure 10 This is a flowchart of a sub-step of step 103 provided in an embodiment of this application;

[0028] Figure 11 This is a schematic diagram of the asymptotes formed by the fragmentation of the micro-particle to be tested into droplets, provided in one embodiment of this application.

[0029] Figure 12 This is a schematic diagram illustrating the distribution of velocity coefficients in each sub-droplet according to an embodiment of this application;

[0030] Figure 13 This is a flowchart of a sub-step of step 104 provided in an embodiment of this application;

[0031] Figure 14 This is a schematic diagram of a scenario involving the large-scale fragmentation of fuel microparticles provided in an embodiment of this application;

[0032] Figure 15 This is a schematic diagram of the structure of a device for determining droplet distribution information provided in an embodiment of this application;

[0033] Figure 16 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0034] To better understand the above-mentioned objectives, features, and advantages of this application, the application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0035] The following description sets forth many specific details to provide a full understanding of this application. The described embodiments are only some, not all, of the embodiments of this application.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the specification of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application.

[0037] It should be further noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0038] In this application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and drawings of this application are used to distinguish similar objects, not to describe a specific order or sequence.

[0039] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0040] During the explosion of a fuel-air explosive (FAE), the FAE, driven by the explosion of the central explosive, rapidly disperses the solid-liquid mixed cloud fuel over a large area, thus forming a relatively stable multiphase cloud region with uniformly dispersed fuel. Then, through secondary ignition, cloud detonation is formed, causing large-scale damage.

[0041] The fuel explosion is divided into a near-field phase and a far-field phase. The near-field phase generally occurs after detonation (O(10)). 0 )-(10 1 Within a timescale of ), during this stage, the fuel cloud annulus expands to O(10) of the initial fuel shell outer diameter. 1 On a spatial scale several times larger than the central explosion load and the flow field of detonation products, the fuel shell accelerates and expands under the drive of the central explosion load and the detonation product flow field, forming a jet structure and decomposing into fuel microparticles. In the far field stage, the fuel cloud region is freed from the influence of the central detonation products and the shock wave flow field, and a large number of non-uniformly distributed high-speed fuel microparticles rapidly disperse in the near-static flow field, while undergoing aerodynamic breakup to form fuel sub-droplets.

[0042] The fuel fragmentation effect is generally described using a mass stripping model. According to Engel's stripping model, due to the convective shearing caused by rapid gas flow, the droplet surface begins to peel away, forming smaller droplets. The stripping rate is expressed by the following formula:

[0043]

[0044] Where ρ is the density of air, ρ L ini Let μ be the density of the fuel, and μ be the air viscosity coefficient. L U is the fuel viscosity coefficient, u is the air velocity, u L Let be the velocity of the fuel, and l be the average radius of the fuel droplet.

[0045] Accurately obtaining the characteristic time of stability of multiphase fuel cloud regions, characteristic configurations of cloud regions, and spatial distribution of fuel concentration are crucial for formulating secondary ignition schemes, optimizing and adjusting warhead structures, and predicting and optimizing cloud detonation damage effects.

[0046] During the simulation of the expansion motion of the cloud and fog region, the fragmentation scenario of all fuel micro-particles in the cloud and fog region can be simulated, thereby obtaining the distribution of droplets in the cloud and fog region and the evolution process of the cloud and fog region.

[0047] However, the fuel cloud at the end of the near-field process of the explosion dispersion contains tens of millions or even hundreds of millions of fuel microparticles. The volume fraction of fuel in the fuel cloud region is in the range of 1%-10%. This means that fuel particles will affect each other through flow field disturbances. Therefore, the breakup of fuel microparticles is also likely to be affected by the surrounding fuel particles. For example, upstream droplets may have a breakup shielding effect on downstream droplets, causing the breakup intensity of downstream droplets to change or not to break up at all.

[0048] If the influence of surrounding fuel particles is ignored, and all fuel particles in the cloud area are considered as fuel particles that will break apart, the following situation will occur:

[0049] 1. If we assume that all fuel microparticles generated in a single crushing operation have a characteristic velocity of ~O(10) 2 Aerodynamic fragmentation occurs at speeds of 10 m / s, and the characteristic size of the fragmented fuel particles ranges from SMD to O(10). 1 )-O(10 2 The radial distance traveled under aerodynamic drag is much smaller than the experimentally observed value.

[0050] 2. If all micro-clusters undergo simultaneous aerodynamic fragmentation, regardless of significant differences in cloud size and internal fuel concentration among different fuel explosions, clouds exhibiting the same expansion rate at the end of the near-field phase of each fuel explosion will have a consistent diameter when their expansion approaches stabilization. However, in actual static explosion tests of thermobaric warheads, the time required for the cloud region to stabilize and the final cloud region radius are strongly correlated with the total mass of the fuel, meaning that experimental observations significantly contradict the above inferences.

[0051] In conclusion, the evolutionary morphology of the cloud and fog region simulated by the above methods is not accurate.

[0052] In view of the above, embodiments of this application propose a method for determining droplet distribution information, including: acquiring neighborhood information of a micro-particle to be tested in a current cloud / fog area; determining, based on the neighborhood information, whether the micro-particle to be tested will break into sub-droplets; if the micro-particle to be tested will break into sub-droplets, calculating the motion information of each sub-droplet formed by the breakup of the micro-particle to be tested, and obtaining the current distribution information of each sub-droplet; if the micro-particle to be tested will not break into sub-droplets, calculating the motion information of the micro-particle to be tested, and obtaining the current distribution information of the micro-particle to be tested.

[0053] The embodiments of this application can determine whether the micro-particle to be tested in the cloud area has broken by combining the influence of the surrounding microparticles on the micro-particle to be tested. Then, based on the judgment result, the motion trajectory of the micro-particle to be tested or the sub-droplets of the micro-particle to be tested is simulated to obtain distribution information, thereby accurately simulating the morphology of the cloud area.

[0054] The method for determining droplet distribution information in this application can be applied to one or more electronic devices. The electronic device is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, processors, microprogrammed control units (MCUs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc. The electronic device can be a portable electronic device (such as a mobile phone or tablet), a personal computer, a server, etc.

[0055] Figure 1 This is a flowchart illustrating one embodiment of the method for determining droplet distribution information according to this application. The order of the steps in this flowchart can be changed, and some steps can be omitted, depending on different requirements.

[0056] See Figure 1 As shown, the method for determining the droplet distribution information may include the following steps.

[0057] Step 101: Obtain the neighborhood information of the microcluster to be tested in the current cloud and fog area.

[0058] The micro-particles to be tested can be liquid micro-particles, such as fuel micro-particles, fire extinguishing material micro-particles, etc.

[0059] Neighborhood information represents information about the particles surrounding the microcluster in the current cloud region. For example, neighborhood information may include the range of the neighborhood, the size of the microcluster within the neighborhood, and the size of the sub-droplets within the neighborhood.

[0060] The range of the neighborhood can be set according to requirements. For example, a spherical range with the micro-particle to be measured as the center and a preset length as the radius. Alternatively, refer to... Figure 2 As shown, the neighborhood range 201 is a ring cylinder with a radius of R on the circular base and the center of the ring cylinder being the micro-cluster to be measured. R can be set according to requirements.

[0061] Step 102: Based on the neighborhood information of the micro-particle to be tested in the current cloud and fog area, determine whether the micro-particle to be tested will break into sub-droplets.

[0062] If the micro-particle to be tested breaks into sub-droplets, proceed to step 103; if the micro-particle to be tested does not break into sub-droplets, proceed to step 104.

[0063] In some embodiments, neighborhood information may include: the distance s between adjacent particles and the micro-group to be tested. Step 102 may include: selecting at least one adjacent particle in the neighborhood range of the micro-group to be tested; obtaining the micro-group size d of the micro-group to be tested; obtaining the Weber number (we) of the micro-group to be tested; determining the shielding effect of the adjacent particle on the micro-group to be tested based on the ratio of the distance s to the micro-group size d (dimensionless distance s / d) and the Weber number; and determining whether the micro-group to be tested will break into sub-droplets based on the shielding effect of the adjacent particle on the micro-group to be tested.

[0064] The Weber number is the ratio of the inertial force to the surface tension of the micro-element under test.

[0065] The shielding effect characterizes the degree to which the fragmentation of the test micro-particle is affected by adjacent particles. The greater the shielding effect, the less likely the test micro-particle is to break. The shielding effect of adjacent particles on the test micro-particle is related to the dimensionless distance between them. The closer the distance between the adjacent particles and the test micro-particle, i.e., the smaller s, the more obvious the shielding effect. When s / d is less than 10, the shielding effect of adjacent particles on the test micro-particle is relatively large.

[0066] For example, refer to Figure 3 As shown, Figure 3 Images show the bag-like breakup (Weber number = 13) and shear breakup (Weber number = 180) of upstream and downstream droplets with a dimensionless spacing s / d = 5.8. In the images, as a group of droplets moves forward, the upstream droplets come into contact with the gas first, and the downstream droplets come into contact with the gas later.

[0067] exist Figure 3 In the mid-stage, the breakup morphology of downstream droplets and the size and distribution of the resulting fragment clouds differ significantly from those of independent droplet breakup. In bag-like breakup, the downstream droplet is covered by the fragment cloud of the upstream droplet, and its breakup intensity is significantly weakened. In shear breakup, the main body of the downstream droplet hardly breaks up, and the resulting tail fragment cloud is more concentrated, clearly different from the bowl-shaped fragment cloud formed by the breakup of the upstream droplet.

[0068] refer to Figure 4 As shown, Figure 4 This is a schematic diagram illustrating the shielding effect of upstream and downstream droplets as a function of the dimensionless distance s / d and the Weber number. Figure 4 It can be seen that as the dimensionless spacing s / d decreases, the radius of the bag-shaped structure formed by droplet deformation is smaller in the bag-shaped breakup mode; while in the shear breakup mode, the cone angle of the cone-shaped fragment cloud formed by droplet breakup is significantly reduced, and the fragments are more concentrated. In both breakup modes, the breakup time of downstream droplets is significantly delayed, and the proportion of large fragments in the formed fragments is significantly increased.

[0069] refer to Figure 5 As shown, Figure 5This diagram illustrates the variation of downstream droplet breakup modes with the dimensionless distance s / d between two droplets at different Weber numbers. Downstream droplet breakup modes can be categorized into free breakup, weakened breakup, and fusion-piercing breakup. Free breakup indicates no shielding effect, weakened breakup indicates partial shielding, and fusion-piercing breakup indicates complete shielding. The boundary line Wecr(s / d) between these three breakup modes exhibits a negative slope, meaning that the critical dimensionless distance for transitioning between the three modes decreases as the Weber number increases.

[0070] Assuming the droplets in the cloud region are distributed in space according to a cubic grid pattern, what is the volume fraction φ of the measured micro-particle within the cube formed by the droplets and its neighboring droplets? drop Approximately equal to π / 6·(s / d) -3 , i.e. φ drop ~π / 6·(s / d) -3 . Figure 5 The top x-coordinate in the diagram transforms the s / d axis into φ. drop Axis. By Figure 5 It can be seen that for fuel particles that undergo shearing and fragmentation, at φ drop When the value is greater than 0.01, the shielding effect of adjacent particles on the micro-group to be tested is relatively large.

[0071] However, the above embodiments involve the qualitative dependence of the shielding effect on the dimensionless distance s / d in a two-droplet system (the microparticle to be tested and its adjacent particles). In contrast, there are billions of particles in the cloud and fog region, each with its own corresponding particle size, and these particles are not uniformly distributed in space. Therefore, it is not accurate to use the dimensionless distance between one or more adjacent particles and the microparticle to be tested to determine the shielding effect of all particles around the microparticle to be tested.

[0072] In view of this, this application proposes another embodiment, in which step 102 may include: determining the shielding effect of each particle in the neighborhood on the micro-particle to be tested based on the neighborhood information, and obtaining the breakup probability of the micro-particle to be tested; and determining whether the micro-particle to be tested will break into sub-droplets based on the breakup probability of the micro-particle to be tested. Here, the breakup probability is a quantitative index of the shielding effect; the greater the shielding effect, the smaller the breakup probability.

[0073] Specifically, the steps are as follows: Figure 6 As shown, step 102 includes:

[0074] Step 1021: Based on neighborhood information, obtain the target local volume fraction of the micro-cluster to be measured.

[0075] In some embodiments, the neighborhood information includes data characterizing the volume of each particle within the neighborhood, as well as the neighborhood volume. For example, the neighborhood information may include the particle density, the radius of each particle, the height of the neighborhood, or the radius of the neighborhood, but is not limited thereto. Particles within the neighborhood include microclusters and subdroplets within the neighborhood.

[0076] For example, the neighborhood volume can be Figure 2 The total volume of the middle neighborhood range of 201.

[0077] The local volume fraction is the ratio of the total volume of all particles in a particle's neighborhood to the volume of that particle's neighborhood, and can be denoted as φ. local .

[0078] The target local volume fraction is the ratio of the total volume of all particles in the neighborhood of the micro-group to the volume of the neighborhood of the micro-group to be measured.

[0079] In some embodiments, step 1021 may include: obtaining the neighborhood volume based on the neighborhood range in the neighborhood information; summing the volumes of each particle within the neighborhood range to obtain the total volume of each particle in the neighborhood; and then calculating the ratio of the total volume of each particle in the neighborhood to the neighborhood volume to obtain the target local volume fraction.

[0080] Step 1022: Obtain the target relationship model representing the relationship between the fragmentation probability and the local volume fraction.

[0081] In some embodiments, refer to Figure 7 As shown, step 1022 may include:

[0082] Step 701: Calculate the Weber number of the microcluster to be tested based on the information of the microcluster to be tested.

[0083] In some embodiments, the Weber number of the micro-group to be measured is the ratio of the inertial force to the surface tension of the micro-group to be measured.

[0084] The Weber number of the microcluster to be tested can be obtained by the following formula:

[0085]

[0086] Among them, w e Let ρ be the Weber number of the micro-group to be tested. g denoted as ρ, v is the current velocity of the micro-particle being measured, d0 is the diameter of the micro-particle being measured, and σ is the surface tension coefficient of the micro-particle being measured.

[0087] Step 702: Input the Weber number into the preset first relational model to obtain the first local volume fraction when the fragmentation probability is one.

[0088] The first relational model can be a function whose input is the Weber number of the micro-cluster to be tested, and whose output is the local volume fraction (first local volume fraction) when the probability of fragmentation of the micro-cluster to be tested is one.

[0089] The breakage probability is denoted as P, indicating that no particles in the neighborhood of the target micro-element exert a shielding effect on the target micro-element. breakup .

[0090] The first relationship model can be obtained by fitting experimental data, such as assuming the first local volume fraction φ. cr,I The first relational model, which follows a directive distribution with respect to the Weiber number *we*, can be represented as follows:

[0091]

[0092] Where, φ cr, I represents the first local volume fraction of the micro-element to be measured, we represents the Weber number of the micro-element to be measured, and a represents the volume fraction of the micro-element to be measured. cr,I and b cr,I All of these are constants, which are parameters that need to be fitted based on experimental data.

[0093] Step 703: Input the Weber number into the preset second relational model to obtain the second local volume fraction when the fragmentation probability is zero.

[0094] The second relational model can be a function, whose input is the Weber number of the micro-element to be tested, and whose output is the local volume fraction (second local volume fraction) when the probability of fragmentation of the micro-element to be tested is zero.

[0095] A breakage probability of zero indicates that each particle in the neighborhood of the micro-particle being tested produces a complete shielding effect on the micro-particle being tested.

[0096] The second relationship model can be obtained by fitting experimental data, such as assuming a second local volume fraction φ. cr,II The second relational model, which follows a directive distribution with respect to the Weiber number *we*, can be represented as follows:

[0097]

[0098] Where, φ cr,II Let a be the first local volume fraction of the micro-element to be measured, we be the Weber number of the micro-element to be measured, and a be the volume fraction of the micro-element to be measured. cr,II and b cr,II All of these are constants, which are parameters that need to be fitted based on experimental data.

[0099] The above a cr,I b cr,I a cr,II and b cr,II It can be done as follows Figure 8 The apparatus shown was obtained through experimental fitting.

[0100] Step 704: Determine the target relationship model based on the first local volume fraction and the second local volume fraction.

[0101] The target relation model can describe the breakage probability P of the micro-cluster to be measured. breakup The local volume fraction φ of the micro-element to be measured local Dependency relationships.

[0102] Specifically, the target relation model f can be represented by the following function:

[0103] P breakup = f(target local volume fraction of the micro-cluster to be measured), where the coefficients in the target relation model f are determined based on the first local volume fraction, the second volume fraction, and the Weber number of the micro-cluster to be measured, and the output of the target relation model is the breakage probability of the micro-cluster to be measured.

[0104] In this target relationship model, the larger the target local volume fraction of the micro-group to be measured, the greater the shielding effect of each particle in the neighborhood of the micro-group to be measured, and the lower the probability of the micro-group to be measured breaking. That is, the target relationship model f is a decreasing function.

[0105] The target relation model f can be obtained by fitting experimental data. The following assumes three functional relationships as the target relation model f, namely: convex function f. I concave function f II and linear function f III .

[0106] refer to Figure 9 As shown, when the Weber number of the micro-element to be measured is We1, the first local volume fraction is... The second local volume fraction is When the Weber number of the micro-cluster to be measured is We², the first local volume fraction is: The second local volume fraction is Table 1 below is Figure 9 The model shown represents the target relation model corresponding to each functional relationship and each Weber number.

[0107] <![CDATA[Convex function f I > <![CDATA[Concave function f II > <![CDATA[Linear function f III > We1 <![CDATA[f I，We1 ]]> <![CDATA[f II，We1 ]]> <![CDATA[f III，We1 ]]> We2 <![CDATA[f I，We2 ]]> <![CDATA[f II，We2 ]]> <![CDATA[f III，We2 ]]>

[0108] Depend on Figure 9 It can be seen that the functional relationship in the target relation model is a convex function f. I In the case of [condition], the shielding effect is very weak in a range where the target local volume fraction is greater than the first local volume fraction, but the shielding effect is significantly enhanced when the target local volume fraction approaches the second local volume fraction.

[0109] The functional relationship in the target relation model is a concave function f. IIIn this case, the shielding effect increases significantly with the increase of the target local volume fraction in a range greater than the first local volume fraction, that is, the fragmentation probability decreases rapidly. When the target local volume fraction is much smaller than the second local volume fraction, the micro团 to be measured can be subjected to a strong shielding effect. Thus, it can be seen that the concave function f II is discontinuous when the target local volume fraction is the first local volume fraction. Therefore, the concave function f II is difficult to reflect the true dependence relationship between the shielding effect and the local volume fraction, that is, it is difficult to reflect the dependence relationship between the fragmentation probability of the micro团 to be measured and the local volume fraction. Therefore, the functional relationship of the objective function relationship is the concave function f II This situation is difficult to hold physically.

[0110] When the functional relationship of the target relationship model is the linear function f IiI the target relationship pattern can be as follows:

[0111]

[0112] where φ local is the target local volume fraction of the micro团 to be measured, φ cr,I is the first local volume fraction, and φ cr,II is the second local volume fraction.

[0113] Step 10,23: Input the target local volume fraction into the target relationship model to obtain the fragmentation probability of the micro团 to be measured.

[0114] Exemplarily, in the case of, the fragmentation probability P breakup of the micro团 to be measured can be directly calculated through this formula.

[0115] Step 10,24: Based on the fragmentation probability of the micro团 to be measured, determine whether the micro团 to be measured will break into sub-droplets.

[0116] In some embodiments, step 10,24 may include: if the fragmentation probability is within a preset first probability range, it is determined that the micro团 to be measured will break into sub-droplets; if the fragmentation probability is within a preset second probability range, it is determined that the micro团 to be measured will not break into sub-droplets.

[0117] where the minimum fragmentation probability in the first probability range is not less than the maximum fragmentation probability in the second probability range.

[0118] For example, determine whether the following inequality holds: |P breakup ·100| < x, where P breakupLet x be the probability of breakage of the micro-particle to be tested, and let x be a preset value that can be set according to requirements. For example, x can be an integer between 1 and 100. If the above inequality holds, it means that the probability of breakage of the micro-particle to be tested is within the preset second probability range, and the micro-particle to be tested will not break into sub-droplets. If the above equation holds, it means that the probability of breakage of the micro-particle to be tested is within the preset first probability range, and the micro-particle to be tested will break into sub-droplets.

[0119] If the micro-particle to be tested breaks into sub-droplets, proceed to step 103.

[0120] Step 103: Calculate the motion information of each sub-droplet formed by the breakup of the micro-cluster to be tested, and obtain the current distribution information of each sub-droplet.

[0121] In some embodiments, reference Figure 10 As shown, step 103 may include:

[0122] Step 1031: Obtain the initial velocity of the micro-particle to be tested.

[0123] The micro-particle to be measured moves in the airflow, and the initial velocity of the micro-particle to be measured can be determined based on the velocity of the airflow.

[0124] Step 1032: Obtain the dispersion angle of each sub-droplet, the mass of each sub-droplet, and the drag force applied to each sub-droplet.

[0125] The dispersion angle is the angle between the direction of the initial velocity of the micro-element to be measured and the direction of the velocity of the sub-droplet.

[0126] The mass of the subdroplet can be calculated using the following formula:

[0127]

[0128] Where m is the mass of the subdroplet. Let ρ be the volume of the subdroplet. fuel Let g be the density of the subdroplet and g be the acceleration due to gravity.

[0129] The drag force F applied to the sub-droplet D It can be calculated using the following formula:

[0130]

[0131] Among them, C D ρ is the drag coefficient. fuel V is the density of the subdroplet. p Let A be the slip velocity of the sub-droplet in the gas flow, where A is the relative velocity between the gas flow and the sub-droplet. p Let be the windward area of ​​the sub-droplet, which is the projected area of ​​the broken particles in the direction of motion.

[0132] The drag coefficient can be determined based on the drag force model, which includes, but is not limited to, the Schiller-Naumann drag force model, the Moore drag force model, the Morsi-Alexander drag force model, the Clift drag force model, etc.

[0133] The Schiller-Naumann drag force model is applicable to fluid-fluid systems, and in some embodiments, the drag coefficient C can be determined using the Schiller-Naumann drag force model. D The formula is shown below:

[0134]

[0135] Where Re is the Reynolds number, a dimensionless number that can be used to characterize fluid flow.

[0136] The dispersion angle is the angle between the initial velocity direction of the micro-element to be measured and the initial velocity direction of each sub-droplet.

[0137] Step 1033: Determine the initial velocity of each sub-droplet based on the initial velocity and dispersion angle.

[0138] In some embodiments, the maximum velocity of the subdroplet at the dispersion angle can be obtained by the following formula, wherein the first preset formula is:

[0139]

[0140] Where V0 is the initial velocity of the micro-particle to be measured, θ is the dispersion angle of the sub-droplet, and V θ The maximum speed of motion, The angle of the asymptote is half the angle between the asymptotes. The angle of the asymptotes is determined based on the motion range of each broken particle. For example, the angle of the asymptotes formed by the sub-droplets after the fragmentation of the micro-cluster to be measured can be referenced. Figure 11 The angle between the dashed lines is shown in the diagram.

[0141] After obtaining the maximum velocity of the subdroplets at the dispersion angle, the number of each subdroplet at that dispersion angle and the velocity coefficient k of each subdroplet at that dispersion angle can be obtained. Multiplying the maximum velocity by the velocity coefficient of each subdroplet yields the initial velocity of each subdroplet. The velocity coefficients of each subdroplet at the dispersion angle can be obtained through experimental fitting; for example, refer to... Figure 12 As shown.

[0142] Figure 12 The horizontal axis represents the velocity coefficient k, which ranges from 0 to 1. The vertical axis represents the ratio of the number of subdroplets with the velocity coefficient at that dispersion angle to the total number of subdroplets at that dispersion angle.

[0143] Step 1034: Determine the Newtonian equation of motion corresponding to each sub-droplet based on the drag force on the sub-droplet and the mass of each sub-droplet.

[0144] Flying in a static flow field, with a density of O(10) 3 A near-spherical microparticle to be tested, weighing 1000 kg / m3, is subject to gravity and drag force, while buoyancy, pressure gradient force, virtual mass force and lift can be ignored. Therefore, by establishing Newton's equation of motion through gravity and drag force, the force analysis of the broken particles can be made more accurate, and the amount of calculation can be reduced.

[0145] In some embodiments, the Newtonian equation of motion for the subdroplet can be as follows:

[0146]

[0147] Where m is the mass of the subdroplet, V p F represents the glide velocity of the subdroplet in the gas flow, t represents time, and F represents the velocity of the subdroplet. D The drag force applied to the subdroplet is m, where m is the mass of the broken particles and g is the gravitational acceleration.

[0148] Step 1035: Determine the acceleration of each sub-droplet based on the Newtonian equation of motion corresponding to each sub-droplet.

[0149] Acceleration reflects the rate of change of the velocity of the sub-droplets over time, thus allowing us to obtain the velocity change of the sub-droplets over time based on the acceleration, which facilitates the determination of the trajectory of each sub-droplet.

[0150] Step 1036: Based on the acceleration and initial velocity of each sub-droplet, determine the current position and velocity of each sub-droplet to obtain the current distribution information of each sub-droplet.

[0151] Based on acceleration and initial velocity, the current position and velocity of the sub-droplets can be obtained, thereby obtaining the current distribution information of the sub-droplets. Furthermore, the morphological evolution process of each sub-droplet formed after the aerodynamic breakup of the micro-group under test can be obtained.

[0152] If the micro-particle to be tested does not break into sub-droplets, proceed to step 104.

[0153] Step 104: Calculate the motion information of the micro-cluster to be measured to obtain the current distribution information of the micro-cluster.

[0154] In some embodiments, reference Figure 13 As shown, step 104 may include:

[0155] Step 1041: Obtain the drag force applied to the micro-group to be tested, the mass of the micro-group to be tested, and the initial velocity of the micro-group to be tested.

[0156] The drag force F applied to the micro-group under test D It can be calculated using the following formula:

[0157]

[0158] Among them, C D ρ is the drag coefficient. fuel V represents the density of the micro-group to be measured. p Let A be the glide velocity of the micro-element to be measured in the airflow, where A is the relative velocity between the airflow and the micro-element to be measured. p The windward area of ​​the micro-particle to be measured is the projected area of ​​the micro-particle in the direction of motion.

[0159] Step 1042: Determine the Newtonian equation of motion for the micro-element under test based on the drag force on the micro-element and the mass of the micro-element under test.

[0160] In some embodiments, the Newtonian equation of motion for the micro-element to be measured can be as follows:

[0161]

[0162] Where m is the mass of the micro-group to be tested, and V p Let F be the glide velocity of the micro-particle being measured in the airflow, t represent time, and F be the velocity of the micro-particle being measured. D The drag force applied to the micro-element under test is m, the mass of the micro-element under test is g, and g is the acceleration due to gravity.

[0163] Step 1043: Determine the acceleration of the micro-element to be measured based on Newton's equation of motion.

[0164] For example, the acceleration of the micro-element to be measured can be as described above.

[0165] Step 1044: Based on the acceleration and initial velocity of the micro-particle to be measured, determine the current position and current velocity of the micro-particle to be measured, and obtain the current distribution information of the micro-particle to be measured.

[0166] Acceleration reflects the rate of change of the velocity of the micro-particle under test with time. Therefore, the velocity change of the micro-particle under test with time can be obtained based on the acceleration. Based on the velocity change of the micro-particle under test with time, the trajectory of the micro-particle under test can be obtained, thereby obtaining the morphological evolution process of the cloud and fog region.

[0167] It is worth mentioning that the location of the micro-particle to be tested will change over time. Therefore, after determining the current location and current velocity of the micro-particle to be tested and obtaining the current distribution information of the micro-particle to be tested, steps 101 to 104 can be re-executed to determine whether the micro-particle to be tested will break down again, so as to obtain the subsequent evolution process of the micro-particle to be tested.

[0168] The above method can be used to simulate the shape of cloud and fog areas. (For reference...) Figure 14 As shown, Figure 14 This is a schematic diagram illustrating a scenario of large-scale fuel particle fragmentation.

[0169] The embodiments of this application can determine whether the micro-particle to be tested in the cloud area has broken by combining the influence of the surrounding microparticles on the micro-particle to be tested. Then, based on the judgment result, the motion trajectory of the micro-particle to be tested or the sub-droplets of the micro-particle to be tested is simulated to obtain distribution information, thereby accurately simulating the morphology of the cloud area.

[0170] Based on the same idea as the method for determining droplet distribution information in the above embodiments, this application also provides a device for determining droplet distribution information, which can be used to execute the above-described method for determining droplet distribution information. For ease of explanation, the schematic diagram of the embodiment of the device for determining droplet distribution information only shows the parts related to the embodiments of this application. Those skilled in the art will understand that the illustrated structure does not constitute a limitation on the device, and it may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0171] like Figure 15 As shown, the device for determining droplet distribution information includes an acquisition module 1501, a judgment module 1502, and a calculation module 1503. In some embodiments, the above modules can be programmable software instructions stored in memory and executable by a processor. It is understood that in other embodiments, the above modules can also be program instructions or firmware embedded in the processor.

[0172] The acquisition module is used to acquire neighborhood information of the microcluster to be tested in the current cloud and fog area;

[0173] The judgment module is used to determine, based on the neighborhood information, whether the micro-particle to be tested will break into sub-droplets;

[0174] The calculation module is used to calculate the motion information of each sub-droplet formed by the breakup of the micro-particle under test, and obtain the current distribution information of each sub-droplet, if the micro-particle under test will break up into sub-droplets; and to calculate the motion information of the micro-particle under test, and obtain the current distribution information of the micro-particle under test, if the micro-particle under test will not break up into sub-droplets.

[0175] Figure 16 This is a schematic diagram of an embodiment of the electronic device of this application.

[0176] The electronic device 100 includes a memory 20, a processor 30, and a computer program 40 stored in the memory 20 and executable on the processor 30. When the processor 30 executes the computer program 40, it implements the steps in the above-described method embodiment for determining droplet distribution information, for example... Figure 1 Steps 101 to 104 are shown.

[0177] For example, computer program 40 can also be divided into one or more modules / units, which are stored in memory 20 and executed by processor 30. The one or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 40 in electronic device 100. For example, it can be divided into... Figure 15 The acquisition module 1501, the judgment module 1502, and the calculation module 1503 are shown.

[0178] Those skilled in the art will understand that the schematic diagram is merely an example of the electronic device 100 and does not constitute a limitation on the electronic device 100. It may include more or fewer components than shown in the diagram, or combine certain components, or different components. For example, the electronic device 100 may also include input / output devices, network access devices, buses, etc.

[0179] Processor 30 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, a single-chip microcomputer, or any conventional processor.

[0180] The memory 20 can be used to store computer programs 40 and / or modules / units. The processor 30 implements various functions of the electronic device 100 by running or executing the computer programs and / or modules / units stored in the memory 20 and by calling data stored in the memory 20. The memory 20 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0181] If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0182] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the electronic device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and other division methods may be used in actual implementation.

[0183] Furthermore, the functional units in the various embodiments of this application can be integrated into the same processing unit, or each unit can exist physically separately, or two or more units can be integrated into the same unit. The integrated units described above can be implemented in hardware or in the form of hardware plus software functional modules.

[0184] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered exemplary and not restrictive in all respects. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or electronic devices recited in the electronic device claims may also be implemented by the same unit or electronic device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

[0185] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application.

Claims

1. A method for determining droplet distribution information, characterized in that, The method includes: Obtain the neighborhood information of the microcluster to be tested in the current cloud and fog area; Based on the neighborhood information, it is determined whether the micro-particle to be tested will break into sub-droplets; If the micro-particle to be tested breaks into sub-droplets, calculate the motion information of each sub-droplet formed by the breakup of the micro-particle to be tested, and obtain the current distribution information of each sub-droplet; If the micro-particle to be tested does not break into sub-droplets, calculate the motion information of the micro-particle to be tested to obtain the current distribution information of the micro-particle to be tested.

2. The method for determining droplet distribution information as described in claim 1, characterized in that, The step of determining whether the micro-particle to be tested will break into sub-droplets based on the neighborhood information includes: Based on the neighborhood information, the shielding effect of each particle in the neighborhood on the micro-group to be tested is determined, and the breakage probability of the micro-group to be tested is obtained. Based on the breakup probability of the micro-group to be tested, it is determined whether the micro-group to be tested will break into sub-droplets.

3. The method for determining droplet distribution information as described in claim 2, characterized in that, The step of determining the shielding effect of each particle in the neighborhood on the micro-cluster under test based on the neighborhood information, and obtaining the breakage probability of the micro-cluster under test, includes: Calculate the total volume of all particles in the neighborhood of the micro-group to be tested; Based on the neighborhood information, the neighborhood volume is obtained; The ratio of the total volume of each particle to the volume of its neighborhood is calculated to obtain the target local volume fraction. The goal is to obtain a relational model that represents the relationship between the probability of breakage and the local volume fraction. The target local volume fraction is input into the target relationship model to obtain the breakage probability of the micro-cluster to be tested.

4. The method for determining droplet distribution information as described in claim 3, characterized in that, The target relational model for obtaining the relationship between the fragmentation probability and the local volume fraction includes: Calculate the Weber number of the micro-element to be tested; The Weber number is input into a preset first relational model to obtain the first local volume fraction when the fragmentation probability is one. The Weber number is input into a preset second relational model to obtain the second local volume fraction when the breakage probability is zero. The target relationship model is determined based on the first local volume fraction and the second local volume fraction.

5. The method for determining droplet distribution information as described in claim 2, characterized in that, The step of determining whether the micro-particle to be tested will break into sub-droplets based on the breakup probability of the micro-particle to be tested includes: If the breakage probability is within a preset first probability range, it is determined that the micro-particle to be tested will break into sub-droplets; If the breakage probability is within a preset second probability range, it is determined that the micro-particle to be tested will not break into sub-droplets, and the minimum breakage probability in the first probability range is not less than the maximum breakage probability in the second probability range.

6. The method for determining droplet distribution information as described in any one of claims 1 to 5, characterized in that, The calculation of the motion information of the micro-element to be measured, to obtain the distribution information of the micro-element to be measured, includes: The drag force applied to the micro-group under test, the mass of the micro-group under test, and the initial velocity of the micro-group under test are obtained. The Newtonian equation of motion for the micro-element under test is determined based on the drag force on the micro-element under test and the mass of the micro-element under test. Based on Newton's equation of motion of the micro-element to be measured, the acceleration of the micro-element to be measured is determined; Based on the acceleration and the initial velocity, the current position and velocity of the micro-cluster to be measured are determined, and the current distribution information of the micro-cluster to be measured is obtained.

7. The method for determining droplet distribution information as described in any one of claims 1 to 5, characterized in that, The calculation of the motion information of each sub-droplet formed by the breakup of the micro-cluster under test, to obtain the current distribution information of each sub-droplet, includes: Obtain the initial velocity of the micro-group to be tested; The dispersion angle, mass of each sub-droplet, and drag force applied to each sub-droplet are obtained. The dispersion angle is the angle between the initial velocity direction of the micro-group to be tested and the initial velocity direction of each sub-droplet. The initial velocity of each sub-droplet is determined based on the initial velocity and the dispersion angle; Based on the drag force on each sub-droplet and the mass of each sub-droplet, determine the Newtonian equation of motion corresponding to each sub-droplet; The acceleration of each sub-droplet is determined based on Newton's equation of motion corresponding to each sub-droplet; Based on the acceleration and initial velocity of each sub-droplet, the current position and velocity of each sub-droplet are determined, and the current distribution information of each sub-droplet is obtained.

8. A device for determining droplet distribution information, characterized in that, include: The acquisition module is used to acquire neighborhood information of the microcluster to be tested in the current cloud and fog area; The judgment module is used to determine, based on the neighborhood information, whether the micro-particle to be tested will break into sub-droplets; The calculation module is used to calculate the motion information of each sub-droplet formed by the breakup of the micro-particle under test, and obtain the current distribution information of each sub-droplet, if the micro-particle under test will break up into sub-droplets; and to calculate the motion information of the micro-particle under test, and obtain the current distribution information of the micro-particle under test, if the micro-particle under test will not break up into sub-droplets.

9. An electronic device, the electronic device comprising a processor and a memory, characterized in that, The memory is used to store instructions, and the processor is used to call the instructions in the memory to cause the electronic device to execute the method for determining droplet distribution information as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed on an electronic device, cause the electronic device to perform the method for determining droplet distribution information as described in any one of claims 1 to 7.

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