CN114564901B - Simulation evaluation method for stone-impact resistance of automobile coating by combining random function - Google Patents

Simulation evaluation method for stone-impact resistance of automobile coating by combining random function Download PDF

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CN114564901B
CN114564901B CN202210465020.0A CN202210465020A CN114564901B CN 114564901 B CN114564901 B CN 114564901B CN 202210465020 A CN202210465020 A CN 202210465020A CN 114564901 B CN114564901 B CN 114564901B
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臧孟炎
钱嘉诚
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South China University of Technology SCUT
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Abstract

The invention discloses a simulation evaluation method for stone-impact resistance of an automobile coating by combining a random function. The method obtains the simulation time t through coupling 1 The position of the impact of the inner particles, and the instantaneous speed and angle information of the particles impacting the coating sample plate; dividing the part of the coating sample plate impacted by the particles into a plurality of rectangular areas, obtaining the final impact probability of all the areas, and respectively predicting t-t by utilizing a random function 1 The area of particle impact over time and the specific impact location coordinates; counting the simulation time t in each region 1 Inner particle impact velocity and impact angle; predicting t-t with random function respectively 1 Impact velocity and impact angle of the particles over time; and (5) equivalent the particle impact in the time t to the abrasion quality of the coating sample plate, and evaluating the stone impact resistance of the coating. The invention not only eliminates the defect of poor test repeatability, but also solves the problems that the calculated amount of multi-particle impact simulation of the automobile coating is large and the engineering application requirements are not met.

Description

Simulation evaluation method for stone-impact resistance of automobile coating by combining random function
Technical Field
The invention relates to the field of testing of stone-impact resistance of an automobile coating, in particular to a simulation evaluation method of stone-impact resistance of the automobile coating by combining a random function.
Background
The automobile coating has great effects on corrosion prevention, rust prevention and ageing resistance of the surface of an automobile. However, the automobile may splash up the road debris during driving, and the impact of the debris on the surface of the coating may cause the coating to break, thereby adversely affecting the aesthetic and safety performance of the vehicle. Therefore, the method has important significance for researching the damage phenomenon of the coating when the coating is impacted by particles and finding an accurate and efficient method for realizing the stone impact resistance evaluation of the coating.
Currently, the evaluation of the stone chip resistance of automotive coatings is mainly carried out by means of standard tests. However, relevant standards are not issued in China at present to standardize the automobile, and a unified standard is not formed among various automobile companies. The most commonly used test standards at present are DIN55996-1 for the DIN system, ISO 20567-1 standard and SAE J400 and JIS standards for the SAE system. On the other hand, tests, although intuitive and quick, are limited by their low repeatability and high cost. In recent years, with the rapid development of computer technology, computer simulation gives new possibility to an automobile coating stone-impact resistance evaluation method. The CFD-DEM coupling simulation method can effectively simulate the process from the movement of particles in the device to the impact of the particles on the coating sample plate, is very helpful for researching the movement of the impactor under different conditions and the damage degree of the sample plate under impact, and can be used for guiding and even replacing a standard test to evaluate the stone impact resistance of the automobile coating.
Through retrieval, the simulation benchmarking method of the automobile coating stone-impact resistance standard experiment in the prior art document shows the capability of the CFD-DEM coupling simulation method for reproducing the coating damage in the experiment, thereby disclosing the possibility of evaluating the stone-impact resistance of the automobile coating by the simulation method. Although the patent performs the comparison of the simulation calculation result and the test result of the coating damage through a plurality of evaluation methods, the factor of the simulation calculation efficiency is not considered. Because the phenomenon time of the test is long, if the whole course simulation calculation is carried out on the test process, great challenge is brought to the simulation efficiency.
Disclosure of Invention
The invention provides a method for combining a random distribution function to improve the efficiency of evaluating the stone-impact resistance of an automobile coating by using a CFD-DEM coupling simulation method. Firstly, a short simulation time length is calculated, 1/5-1/3 of the total simulation time length is generally taken, then the distribution rule of particle impact positions, speeds and angles is counted, and the motion characteristics of particles in the same time length in the test are estimated by using a random function. The simulation evaluation method for the stone-impact resistance of the automobile coating combined with the random distribution function can greatly shorten the calculation time of simulation and provide a feasible way for efficiently and accurately evaluating the stone-impact resistance of the automobile coating.
The purpose of the invention is realized by at least one of the following technical solutions.
A simulation evaluation method for stone-impact resistance of an automobile coating combined with a random function comprises the following steps:
s1: according to the conditions of the automobile coating stone-impact resistance test standard, a CFD-DEM coupling simulation model is arranged in an open source coupling frame CFDEM, and comprises a CFD grid model, a turbulence model and a multi-spherical particle model; obtaining a period of simulation time t through coupling operation 1 The position of all the particles in the coating sample, and the instantaneous speed and angle information of the particles impacting the coating sample, t 1 <Test ofTotal time of phenomenon t;
s2: establishing a two-dimensional coordinate system on a coating sample plate plane, and simulating a period of time t 1 The positions of all particle impacts in the coordinate system are expressed by coordinates in the coordinate system;
s3: dividing the part of the coating sample plate impacted by the particles into a plurality of rectangular areas along the vertical direction and the horizontal direction, calculating the final impact probability of all the areas, and respectively predicting t-t by using a random function 1 The area of particle impact and the specific impact location coordinates of the particles for all impact coating templates within time;
s4: counting a period of simulation time t in each region 1 Internally simulating the obtained particle impact speed and impact angle; respectively predicting t-t by referring to the mean value and the range of the impact speed and the impact angle and assisting a random function 1 Impact velocity and impact angle of all particles over time;
s5: and (4) utilizing a wear model, equating all particle impact in the time t as the wear quality on the coating sample plate, and presenting the wear quality in a wear quality cloud chart mode, so as to evaluate the stone impact resistance of the coating.
Further, in step S1, drawing a CFD mesh model and setting boundary conditions according to the arrangement of the stone-impact device, setting physical parameters of the air flow and turbulence model according to the test conditions, constructing a multi-spherical particle model according to the shape of the used impactor and setting physical parameters of the particles, and setting particle-fluid two-phase force; obtaining the total time t of the test phenomenon according to the test standard;
capturing the motion trail of the particles in the stone-impact device in the simulation process by a CFD-DEM coupling simulation calculation method, thereby obtaining the motion state of the particles at the moment of impacting the surface of the coating sample plate and further obtaining a period of simulation time t 1 The position of all the particles in the coating sample, and the instantaneous speed and angle information of the particles impacting the coating sample, t 1 <t。
Further, in step S1, the time length t calculated by simulation 1 1/5-1/3 of the total duration t of the test phenomenon is taken, and meanwhile, the simulation time t must be ensured 1 The undercoating template is subjected to at least 50 particle impacts.
Further, in step S2, when the two-dimensional coordinate system is established on the plane of the coating sample plate, the axis X, Y is established with the center or four corners of the coating sample plate as the origin of coordinates and parallel to the two sides of the coating sample plate, so as to determine and coordinate the impact point.
Further, in step S3, when dividing the regions, the regions on both sides of the center line of the coating sample plate in the vertical direction are made symmetrical;
when dividing the region, the region should be divided equally as much as possible on the premise of guaranteeing the distribution rule of particle impact, as follows:
firstly, uniformly dividing the impacted area of the coating sample plate in parallel to the short side direction of the coating sample plate, wherein the dividing can show the distribution rule that the impact of particles is most dense in the area near the center of the coating sample plate and is gradually sparse along the areas at two sides;
for the edge region with less than 6% of total impact of particles, the area of the edge region can be amplified, and the area of the amplified edge region cannot exceed 2 times of the area of the adjacent region;
after the preliminary division, if the particle impact frequency of a certain area is more than twice of the particle impact frequency of two adjacent areas, the area is divided equally for the first time;
and after the division in the direction parallel to the short side of the coating sample plate is finished, performing secondary uniform division on the shot area of the coating sample plate in the direction parallel to the long side of the sample plate according to the same principle.
Further, in step S3, the frequency of the particles impacting a certain region and the simulation time t 1 The ratio of the total times of impacting the sample plate by the particles in the sample plate is used as the probability that the particles fall in the region, namely the initial hit probability of the region;
because the structures and conditions on the two sides of the central line of the coating sample plate in the vertical direction are completely symmetrical, and the hit probabilities of two areas symmetrical relative to the central line of the coating sample plate in the vertical direction are the same, the hit probabilities of the two areas symmetrical relative to the central line of the coating sample plate in the vertical direction are summed and averaged to obtain the final hit probability of the two areas;
for any region, the probability of the particles falling to any position in the region is set to be the same, and the sum is equal to the final hit probability of the region.
Further, in step S3, when determining the region impacted by the particle, the random function used is a uniformly distributed random function set in the (0, 1) interval, and the region impacted by the particle is predicted by a random number obtained by the random function in combination with the final impact probability of each region;
when the specific impact position coordinate of the particle is determined, the used random function is a two-dimensional uniformly distributed random function, and the interval of the random function is set according to the area coordinate.
Further, in step S4, since the impact velocity and the impact angle of the particles have a distribution rule in the vertical direction, the regions with the same vertical coordinate are regarded as the same group of regions, and at least 5 simulation times t are ensured in each group of regions 1 Internally simulating the obtained particle impact points; the impact speed and the impact angle of the particle impact points in the same group of areas are counted together;
when the impact speed and the angle of the particles are determined, the used random function is a uniformly distributed random function, and the interval is determined according to the average value A and the extreme value R of the impact speed and the angle of the impact point of the particles in the same group, and is specifically determined as (A-R/2, A + R/2);
when the mean value A and the extreme value R are calculated, particle impact points with impact angles larger than 110% of the median impact angle or less than 90% of the median impact angle can be ignored as error samples.
Further, in step S5, the wear model is a Finnie wear model, and the wear quality on the coating template is calculated by the following formula:
Figure 355053DEST_PATH_IMAGE001
；
wherein the content of the first and second substances,EMis the wear mass;kis a constant obtained by calibration of simulation and test;v p is the magnitude of the particle impact velocity;cis the vector pointing from the particle centroid to the impact point;t c for joining particles to wallsThe total time of the contact is as follows,f c is the contact force of the particles with the wall surface for the simulation time t 1 For the particle impact point calculated by simulation,t c andf c for intermediate variables in the simulation calculation process, for t-t 1 For a particle impact point predicted by a random function over time,t c andf c all get the simulation time t 1 Of points of impact of particles calculated by internal simulationt c Andf c the mean value of (a);
Figure 990303DEST_PATH_IMAGE002
is a dimensionless function of the angle of impact, expressed as:
Figure 908056DEST_PATH_IMAGE003
；
Figure 80761DEST_PATH_IMAGE004
is the impact angle between the impact trajectory of the particle and the wall.
Further, in step S5, dividing a grid on the coating sample plate, quantifying the wear mass for the damage caused by the impact of particles at different positions, and classifying the wear mass into the grid at the corresponding position;
finally, after-treatment software is used for presenting the simulated coating sample plate in the form of a wear quality cloud chart, so that comparison with a test result is facilitated.
Compared with the prior art, the invention has the following advantages and technical effects:
according to the method, the result of CFD-DEM coupling simulation is combined with a random function to obtain the motion characteristics of all particles impacting the coating sample in the total test duration, so that the damage condition of the automobile coating caused by multi-particle impact is obtained, and the stone impact resistance of the automobile coating is evaluated according to the damage condition. The method not only eliminates the defect of poor test repeatability, but also solves the problems that the multi-particle impact simulation calculation amount of the automobile coating is large and the engineering application requirements are not met, greatly shortens the calculation time length on the premise of ensuring certain accuracy, and provides a feasible scheme with practical engineering significance for realizing the stone impact resistance evaluation of the automobile coating.
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FIG. 1 is a schematic diagram of an experimental setup of SAE J400 standard in example 1 of the present invention;
FIG. 2 is a schematic diagram of non-spherical particles formed by the rigid nodules of the pellets in example 1 of the present invention;
fig. 3 is a schematic diagram of particle impact position distribution, coating template area division and two-dimensional coordinate establishment obtained by preliminary coupling simulation calculation in embodiment 1 of the present invention;
FIG. 4 is a schematic diagram of the principle of estimating the impact area of a particle by a random function in example 1 of the present invention;
FIG. 5 is a graph showing the distribution of impact positions of particles over time of a test phenomenon obtained by combining random functions in example 1 of the present invention;
fig. 6 is a schematic diagram of region grouping in embodiment 1 of the present invention;
FIG. 7 is a schematic view of a wear cloud obtained from a wear model in example 1 of the present invention;
fig. 8 is a schematic diagram of particle impact position distribution, coating template area division and two-dimensional coordinate establishment obtained by preliminary coupling simulation calculation in embodiment 2 of the present invention;
FIG. 9 is a graph of the distribution of the impact positions of particles over the total time of the experimental phenomenon obtained by combining the random functions in example 2 of the present invention;
FIG. 10 is a schematic view of a wear cloud obtained from a wear model in example 2 of the present invention;
FIG. 11 is a schematic diagram of the experimental setup of DIN55996-1 standard in example 3 of the present invention;
FIG. 12 shows non-spherical particles formed by the rigid nodules of the pellets in example 3 of the present invention;
fig. 13 is a schematic diagram of particle impact position distribution, coating template area division and two-dimensional coordinate establishment obtained by preliminary coupling simulation calculation in embodiment 3 of the present invention;
FIG. 14 is a graph showing the distribution of impact positions of particles over time of experimental phenomena obtained by combining random functions in example 3 of the present invention;
fig. 15 is a schematic view of a wear cloud obtained from a wear model in example 3 of the present invention.
Detailed Description
The following further describes the method and process of using the present invention with reference to specific examples, which are only preferred embodiments of the present invention, but the scope of the present invention is not limited thereto.
Example 1:
in this example, the practice of the present invention will be further specifically described by taking as an example the evaluation of the stone chip resistance of an automobile coating based on the SAE J400 standard test conditions.
A simulation evaluation method for stone-impact resistance of an automobile coating combined with a random function comprises the following steps:
s1: according to SAE J400 automobile coating stone-impact resistance evaluation standard, a CFD-DEM coupling simulation model is arranged in an open source coupling frame CFDEM, flow field grids are divided and boundary conditions are set according to the size of a stone-impact resistance tester used for a test and the arrangement of the position of a coating sample plate, as shown in figure 1, physical parameters of air flow are set in open source CFD software OpenFOAM according to actual test conditions, a turbulence model and the boundary conditions of an inlet, an outlet and a wall surface are set according to the air flow during the test, and in the embodiment, the air density is 1.225kg/m 3 Dynamic viscosity is set to 1.79X 10 -5 Ns·m -2 Use ofk-εA turbulent flow model, wherein an airflow inlet is set as a speed inlet boundary with the air speed of 31.3 m/s, a pressure outlet boundary (standard atmospheric pressure) is set at an outlet at the lower part of an inlet box body, and a wall surface is set as a non-slip boundary and is used for calculating a Computational Fluid Dynamics (CFD) part;
pellets having a particle size of 2mm were used to form non-spherical particles having a corresponding shape by rigid agglomeration depending on the shape of the cobblestones used in the test. In this example, the test standard was set to specify a total test duration of 7s and a pellet density of 2800 kg/m 3 Its Young's modulus is 60.0GPa, its Poisson ratio is 0.25, and is used for calculation of Discrete Element (DEM) part. The particle shape is shown in figure 2. According to the test requirements, the total time of the test phenomenon is required to be within 7s1pt of cobble granules, i.e. about 210 granules, were injected into a macadam impact tester. In the simulation calculation process, according to test conditions, 30 non-spherical particles are generated and participate in calculation in turn in the simulation duration of each second;
a model of particle-fluid two-phase acting force is set, in the embodiment, a 'De Felice' drag model is adopted to calculate the coupling drag force, and a 'Mei Lift' Lift model is adopted to calculate the coupling Lift force;
the motion trail of the particles in the stone-hitting device in the simulation process is captured through a CFD-DEM coupling simulation calculation method, so that the motion state of the particles at the moment when the particles hit the surface of the coating sample plate is obtained, and further the positions where all the particles hit within a period of simulation time 2s, and the speed and angle information of the particles at the moment when the particles hit the coating sample plate are obtained.
In this embodiment, a 2s duration simulation calculation is performed on the particle-fluid coupling simulation model to obtain the position of the coating sample plate subjected to particle impact within 2s duration, and the information of the instantaneous speed and angle of the particle impact sample plate. A total of 62 particles moved to the vicinity of the coating template within 2s, of which 51 particles impacted the template. The position at which the particles impact the template and the velocity and angle of the particles upon impact are shown in table 1.
TABLE 1 impact velocity and impact angle of particles from SAE calculation example of CFD-DEM coupled simulation
Particle ID Speed of impact Angle of impact Y-direction coordinate Z-direction coordinate
1 8.928567003 84.85773158 0.0290928 0.0354409
2 8.685611417 88.41689002 0.00740963 0.00889328
3 8.641503993 86.4884169 0.00796854 0.0174268
4 9.061937557 80.65981592 0.00689176 -0.0576313
5 7.365916781 76.5971986 0.00365573 -0.0348645
6 8.553682546 89.40443015 0.00892371 -0.00886549
7 9.493480923 77.63466033 0.0193861 -0.0800801
8 8.770831369 79.39931163 0.00981038 -0.0658591
9 9.507159156 89.24835809 0.011736 -0.0107021
10 8.758331944 83.08735146 0.0199551 -0.0471259
11 8.409611278 85.58690559 0.0195652 0.0192783
12 8.635358927 88.59924704 0.0299545 0.0122706
13 8.992532105 87.14673361 0.00482236 0.0176066
14 9.230518079 85.97995963 0.0035002 0.0210608
15 8.433435333 88.89893928 0.00574173 0.00242879
16 9.097038969 89.68684642 0.000700726 -0.000982055
17 9.120707105 82.00578681 0.015504 -0.0526566
18 8.852260763 89.58501937 0.0344412 -0.010795
19 8.852065421 76.92720732 0.00559648 -0.0888872
20 9.001976691 86.59300527 0.0146653 -0.0253553
21 9.284358106 89.67984389 0.0237018 -0.0095489
22 8.287156312 85.60455313 0.0116333 0.016339
23 8.634718997 87.30679668 0.0150537 0.0174228
24 9.343192601 84.22981829 0.00712546 0.0311758
25 8.877383406 83.28909448 0.0288046 0.0429223
26 9.082576466 86.93741814 0.00570635 -0.025021
27 8.869538221 86.09729825 0.00772626 0.0148464
28 8.629203667 77.76248087 0.000806415 -0.0810709
29 7.463167607 85.08321508 0.0226613 -0.0463974
30 9.867410611 82.4028443 0.0125443 -0.0417521
31 9.462932846 78.81291554 0.0132286 -0.0757369
32 9.524265387 77.83140594 0.0101149 -0.0816263
33 8.566143615 88.57241133 0.0240361 -0.0176411
34 8.903064081 78.44330875 0.0182581 -0.0750596
35 8.759525387 88.28330157 0.0110373 0.0147577
36 8.942758514 89.4379103 0.0155901 -0.00711407
37 8.370840201 85.05655762 0.00419307 0.0221521
38 9.068929041 85.50859935 0.0181802 0.0228716
39 9.550773899 80.51380632 0.00707989 -0.0659086
40 8.361131733 82.9846617 0.0283325 0.0294572
41 8.154011747 88.06525296 0.0172654 0.0128668
42 9.019139231 81.78381941 0.0234438 -0.05318
43 8.899875637 87.55326021 0.00957587 -0.0230437
44 9.482620289 79.7065972 0.027089 -0.0679425
45 9.0506808 89.99757486 0.00834059 -0.00760574
46 8.977943206 86.31073721 0.0246777 0.0148568
47 9.20365418 89.24940544 0.0241495 -0.00755532
48 8.537033196 87.06972667 0.0332893 0.0121967
49 6.700038824 88.02779362 0.0356745 0.0122478
50 8.954382001 88.96372958 0.0124526 -0.00521115
51 9.102207011 88.78577563 -0.012039 0.010660
S2: establishing a two-dimensional coordinate system on a coating sample plate plane, and simulating a period of time t 1 The positions of all particle impacts in the coordinate system are expressed by coordinates in the coordinate system;
in this example, the size of the coating template used was 100mm × 300 mm; a two-dimensional rectangular coordinate system shown in fig. 3 is established with the center of the template as the origin.
S3: dividing the part of the coating sample plate impacted by the particles into a plurality of rectangular areas along the vertical direction and the horizontal direction, calculating the final impact probability of all the areas, and respectively predicting t-t by using a random function 1 The area of particle impact and the specific impact location coordinates of the particles for all impact coating templates within time;
when the areas are divided, the areas on two sides of the central line of the coating sample plate in the vertical direction are symmetrical;
when dividing the region, the region should be divided equally as much as possible on the premise of guaranteeing the distribution rule of particle impact, as follows:
firstly, uniformly dividing the impacted area of the coating sample plate in parallel to the short side direction of the coating sample plate, wherein the dividing can show the distribution rule that the impact of particles is most dense in the area near the center of the coating sample plate and is gradually sparse along the areas at two sides;
for the edge region with less than 6% of total impact of particles, the area of the edge region can be amplified, and the area of the amplified edge region cannot exceed 2 times of the area of the adjacent region;
after the preliminary division, if the particle impact frequency of a certain area is more than twice of the particle impact frequency of two adjacent areas, the area is divided equally for the first time;
and after the division in the direction parallel to the short side of the coating sample plate is finished, performing secondary uniform division on the shot area of the coating sample plate in the direction parallel to the long side of the sample plate according to the same principle.
Frequency of impact of particles on certain area and simulation time t 1 The ratio of the total times of impacting the sample plate by the particles is taken as the probability that the particles fall in the region, namely the initial impact probability of the region;
because the structures and conditions on the two sides of the central line of the coating sample plate in the vertical direction are completely symmetrical, and the hit probabilities of two areas symmetrical relative to the central line of the coating sample plate in the vertical direction are the same, the hit probabilities of the two areas symmetrical relative to the central line of the coating sample plate in the vertical direction are summed and averaged to obtain the final hit probability of the two areas;
for any region, the probability of the particles falling to any position in the region is set to be the same, and the sum is equal to the final hit probability of the region.
When the area impacted by the particles is determined, the used random function is a uniformly distributed random function set in a (0, 1) interval, and the area impacted by the particles is predicted through a random number obtained by the random function in combination with the final impact probability of each area;
when the specific impact position coordinate of the particle is determined, the used random function is a two-dimensional uniformly distributed random function, and the interval of the random function is set according to the area coordinate.
In this embodiment, according to the particle impact position obtained by simulation calculation, the part of the coating sample plate impacted by the particles is divided into a plurality of rectangular areas along the horizontal and vertical directions, and the areas on both sides of the y-axis of the sample plate are symmetrical. After the coating sample plate is divided primarily in the direction parallel to the short side of the coating sample plate, because the particle impact frequency of a certain area near the x coordinate axis is more than twice of the particle impact frequency of two adjacent areas, the area is divided equally again. And secondly, uniformly dividing the shot area of the sample plate twice in parallel to the long edge direction of the coating sample plate by the same principle. According to the principle of area division, the hit area is divided into 40 blocks in the present embodiment, as shown in fig. 3.
In this embodiment, it is known from the actual test conditions that if a full-scale simulation of 7s is performed, a total of 210 particles will be generated and moved by the airflow. Of these particles, about 173 particles will impact the template according to the rule simulated in step S2. Except the data of 51 particle impacts obtained by simulation, the data of 122 particle impacts are still unknown. Based on the probability of the particles impacting each region obtained in step S3, the regions impacted by the remaining 122 particles are estimated using a uniformly distributed random function set in the (0, 1) interval, the principle of which is shown in fig. 4. The hit probabilities of all the regions are arranged on the one-dimensional coordinate axis in the form of intervals according to the region ID sequence, and the size of the interval is determined by the size of the hit probability. It is assumed that for a certain particle, the random number 0.195687 is obtained by a random function, i.e., the region where the particle impacts can be obtained by querying the region ID on the coordinate axis.
Since the coating template has been divided into several tens of regions having a small area in the above step, it can be assumed that the probability of a particle striking any position in a certain region is the same, and the coordinates of the remaining 122 particles striking are estimated using a uniformly distributed two-dimensional random function, and the intervals of the function are determined based on the coordinates of the vertices of the rectangular region. The predicted particle positions are shown in fig. 5.
S4: counting a simulation time t in each region 1 Internally simulating the obtained particle impact speed and impact angle; respectively predicting the impact speeds and the impact angles of the rest 122 particles by referring to the mean value and the extreme difference of the impact speeds and the impact angles and assisting a random function;
since the impact velocity and the impact angle of the particles are distributed regularly in the vertical direction, the regions with the same vertical coordinate are regarded as the same group of regions as shown in fig. 6, and at least 5 simulation times t in each group of regions are ensured 1 Internally simulating the obtained particle impact points; the impact speed and the impact angle of the particle impact points in the same group of areas are counted together;
when the impact speed and the angle of the particles are determined, the used random function is a uniformly distributed random function, and the interval is determined according to the average value A and the extreme value R of the impact speed and the angle of the impact point of the particles in the same group, and is specifically determined as (A-R/2, A + R/2);
when calculating the mean value a and the extreme value R, some particle impact points with impact angles greater than 110% or less than 90% of the median of the set of impact angles can be ignored as error samples.
S5: utilizing a wear model, equating all particle impact in t time to be the wear quality on the coating sample plate, and presenting the wear quality in a wear quality cloud picture mode, so as to evaluate the stone impact resistance of the coating;
the wear model is a Finnie wear model, and the wear mass on the coating template is calculated by the following formula:
Figure 347663DEST_PATH_IMAGE005
；
wherein the content of the first and second substances,EMis the wear mass;kis a constant obtained by calibration of simulation and test;v p is the magnitude of the particle impact velocity;cis the vector pointing from the particle centroid to the impact point;t c is the total time of contact of the particles with the wall surface,f c is the contact force of the particles with the wall surface for the simulation time t 1 For the particle impact point calculated by simulation,t c andf c for intermediate variables in the simulation calculation process, for t-t 1 For the point of impact of a particle predicted by a random function over time,t c andf c all get the simulation time t 1 Of points of impact of particles calculated by internal simulationt c Andf c the mean value of (a);
Figure 478781DEST_PATH_IMAGE006
is a dimensionless function of the angle of impact, expressed as:
Figure 526240DEST_PATH_IMAGE007
；
Figure 237057DEST_PATH_IMAGE008
is the impact angle between the impact trajectory of the particle and the wall.
Dividing grids on the coating sample plate, quantifying the abrasion quality for damage caused by particle impact at different positions, and enabling the abrasion quality to be in the grids at the corresponding positions;
the simulated coating template was finally presented in the form of a wear quality cloud using post-processing software, as shown in fig. 7, for ease of comparison with the test results.
Example 2:
the implementation procedure of this embodiment is the same as that of embodiment 1, except that the manner of area division is adjusted. In the present embodiment, the hit area is divided into 42 blocks, as shown in fig. 8. The embodiment divides the area into smaller areas in the primary division parallel to the short side direction of the coating sample plate, so that the area does not need to be divided equally after the primary division. After predicting the impact coordinates of the remaining 5s particles using the random function, the coordinates of the impact of all particles are shown in fig. 9. The final result is presented in the form of a cloud of wear masses, as shown in fig. 10.
Example 3:
the procedure of this example was the same as in example 1, except that the simulation was carried out in accordance with DIN55996-1, the dimensions of the rock burst tester apparatus used in the test and the arrangement of the positions of the coated panels, as shown in FIG. 11; in the test, the standard stipulated that the phenomenon takes a total of 10s, the mass of the granules is 500g and the total number is approximately 709 granules. The particles used were non-spherical particles formed by the rigid agglomeration of small spheres having a particle diameter of 1mm, and the shape of the particles was as shown in FIG. 12. The pellet density was set at 7890 kg/m3, its Young's modulus was 208GPa, and its Poisson ratio was 0.3, for calculation of the Discrete Element (DEM) section. Simulation calculations of the 2s event time were performed and the positions where some particles impacted the template and the particle velocities and angles at impact are shown in table 2.
TABLE 2 impact velocity and impact angle of partial particles obtained from the DIN calculation of CFD-DEM coupled simulation
Particle ID Speed of impact Angle of impact Y-direction coordinate Z-direction coordinate
1 -0.0259396 0.0624322 4.699077204 81.89286038
2 0.0172276 0.0484663 6.133445992 83.68693124
3 0.0112858 0.0484446 5.818129075 84.47970395
4 0.00832178 0.048094 5.592366348 85.02615691
5 0.026896 0.04751 6.587516638 83.46377725
6 -0.01456 0.0370317 5.579500113 88.7539728
7 -0.0291506 0.0340664 5.228889233 88.85226895
8 0.00821588 0.0317187 6.317617128 88.7340391
9 0.029954 0.0275944 4.009544067 89.88474259
10 -0.0148108 0.0273361 6.194797638 87.7740237
11 0.020204 0.0266989 5.622371059 89.49157151
12 -0.0181605 0.0265759 5.516619981 89.95419705
13 0.00863168 0.0265012 5.588794391 89.90584071
14 -0.00787 0.0257124 4.711212228 89.43804246
15 0.0230111 0.022993 4.15421969 87.63354669
16 0.030705 0.0198136 5.977862855 89.93527645
17 -0.0116271 0.0173415 5.245275916 89.13483331
18 -0.0092388 0.0172051 4.867678486 88.43573318
19 -0.00652 0.0162443 5.510402463 89.21144288
20 -0.0257899 0.0151583 5.323901685 87.68116836
21 0.019175 0.0136942 5.964568332 88.77382258
22 -0.0229395 0.013636 6.526288558 88.70266765
23 0.00114556 0.0117135 6.090035996 88.98533711
24 -0.0312339 0.0115175 6.089590185 89.46548214
25 0.0246368 0.0106242 5.510877756 88.86456003
26 0.00890712 0.0093278 6.423864404 89.553949
27 0.0105849 0.0091447 5.295642157 87.71149128
28 -0.02742 0.0074921 6.049099743 89.76728294
29 -0.0291395 0.0073668 5.869159583 89.08754377
30 0.00135781 0.0070225 6.045442568 87.36902397
31 -0.0012039 0.0060174 5.852394731 87.01376416
32 -0.0028524 0.0056997 5.530861763 87.31192182
33 -0.0044076 0.0049232 5.552061824 86.49756684
34 -0.00602 0.0026833 6.090527196 87.88433784
35 0.028203 0.0014678 5.746684389 87.34045462
36 -0.0246662 -0.002005 5.747642345 86.8176527
37 -0.0227953 -0.004311 5.534115722 85.59564676
38 -0.0024 -0.005563 6.0956943 86.40028378
39 0.0316839 -0.005977 4.90302678 84.47277187
40 -0.0289972 -0.009306 5.474144051 84.01794959
41 -0.02908 -0.009346 6.507020899 85.36754346
42 -0.0265 -0.010109 6.696582683 86.39134836
43 -0.0279197 -0.011023 5.74862613 84.35612008
44 -0.0041434 -0.011909 5.196072354 84.32988382
45 0.019084 -0.012066 5.562366041 83.02805595
46 -0.0177159 -0.015863 6.260045111 84.60137006
47 0.0290577 -0.017358 5.035516471 83.01535329
48 0.030274 -0.017822 4.72346185 83.90579986
49 -0.0121158 -0.018529 5.490474398 83.8350127
50 -0.0065 -0.019123 5.755619709 85.0019679
The coordinates of the 92 particle impacts calculated by simulation are shown in fig. 13. After predicting the impact coordinates of the remaining 8s particles using the random function, the coordinates of the impact of all particles are shown in fig. 14. The final result is presented in the form of a cloud of wear mass, as shown in fig. 15.

Claims (10)

1. A simulation evaluation method for stone-impact resistance of an automobile coating combined with a random function is characterized by comprising the following steps:
s1: according to the conditions of the automobile coating stone-impact resistance test standard, a CFD-DEM coupling simulation model is arranged in an open source coupling frame CFDEM, and comprises a CFD grid model, a turbulence model and a multi-spherical particle model; obtaining a period of simulation time t through coupling operation 1 The position of all the particles in the coating sample, and the instantaneous speed and angle information of the particles impacting the coating sample, t 1 < total time t of test phenomenon;
s2: establishing a two-dimensional coordinate system on a coating sample plate plane, and simulating a period of time t 1 The positions of all particle impacts in the coordinate system are expressed by coordinates in the coordinate system;
s3: dividing the part of the coating sample plate impacted by the particles into a plurality of rectangular areas along the vertical direction and the horizontal direction, calculating the final impact probability of all the areas, and respectively predicting t-t by using a random function 1 The area of particle impact and the specific impact location coordinates of the particles for all impact coating templates within time;
s4: counting a simulation time t in each region 1 Particle impact speed and impact angle obtained by internal simulation; respectively predicting t-t by referring to the mean value and the range of the impact speed and the impact angle and assisting with a random function 1 Impact velocity and impact angle of all particles within time;
s5: and (4) utilizing a wear model, equating all particle impact in the time t to the wear mass on the coating sample plate, and presenting the wear mass in a wear mass cloud chart mode, so as to evaluate the stone chip resistance of the coating.
2. The simulation evaluation method for stone chip resistance of automobile coating combined with random function as claimed in claim 1, wherein in step S1, a CFD mesh model is drawn according to stone chip device layout and boundary conditions are set, physical parameters of air flow and turbulence model are set according to test conditions, a multi-spherical particle model is constructed according to used impactor shape and physical parameters of particles are set, and particle-fluid two-phase force is set; obtaining the total time t of the test phenomenon according to the test standard;
capturing the motion trail of the particles in the stone-impact device in the simulation process by a CFD-DEM coupling simulation calculation method, thereby obtaining the motion state of the particles at the moment of impacting the surface of the coating sample plate and further obtaining a period of simulation time t 1 The position of all the particles in the coating sample, and the instantaneous speed and angle information of the particles impacting the coating sample, t 1 ＜t。
3. The method for simulation evaluation of stone-impact resistance of automobile coating combined with random function as claimed in claim 2, wherein in step S1, the time length t calculated by simulation 1 1/5-1/3 of the total duration t of the test phenomenon is taken, and meanwhile, the simulation time t must be ensured 1 The undercoating template is subjected to at least 50 particle impacts.
4. The method as claimed in claim 1, wherein in step S2, when the coating template plane is formed into a two-dimensional coordinate system, the center or four corners of the coating template are used as the origin of coordinates, and X, Y axes are formed parallel to the two sides of the coating template, so as to determine and coordinate the impact point.
5. The simulation evaluation method for stone chip resistance of automobile coating combined with random function as claimed in claim 1, wherein in step S3, when dividing the regions, the regions on both sides of the center line of the coating sample plate in the vertical direction are made symmetrical;
when dividing the region, the region should be divided equally on the premise of guaranteeing the distribution rule of particle impact, specifically as follows: firstly, uniformly dividing the impacted area of the coating sample plate in parallel to the short side direction of the coating sample plate, wherein the dividing can show the distribution rule that the impact of particles is most dense in the area near the center of the coating sample plate and is gradually sparse along the areas at two sides; amplifying the area of the edge region with less than 6% of total impact of particles, wherein the area of the amplified edge region cannot exceed 2 times of the area of the adjacent region;
after the preliminary division, if the particle impact frequency of a certain area is more than twice of the particle impact frequency of two adjacent areas, the area is divided equally for the first time;
after the division in the direction parallel to the short side of the coating sample plate is finished, the secondary uniform division is carried out on the shot area of the coating sample plate in the direction parallel to the long side of the sample plate according to the same principle.
6. The method for simulation evaluation of stone-chip resistance of automobile coating by combining random function as claimed in claim 5, wherein in step S3, the frequency of impact of particles on a certain area and the simulation time t are determined 1 The ratio of the total times of impacting the sample plate by the particles is taken as the probability that the particles fall in the region, namely the initial impact probability of the region;
because the structures and conditions on the two sides of the central line of the coating sample plate in the vertical direction are completely symmetrical, and the hit probabilities of two areas symmetrical relative to the central line of the coating sample plate in the vertical direction are the same, the hit probabilities of the two areas symmetrical relative to the central line of the coating sample plate in the vertical direction are summed and averaged to obtain the final hit probability of the two areas;
for any region, the probability of the particles falling to any position in the region is set to be the same, and the sum is equal to the final hit probability of the region.
7. The method for simulation evaluation of stone chip resistance of automobile coating combined with random function as claimed in claim 6, wherein in step S3, when determining the region of particle impact, the random function used is a uniformly distributed random function set in the interval of (0, 1), and the region of particle impact is predicted by the random number obtained by the random function in combination with the final impact probability of each region;
when the specific impact position coordinate of the particle is determined, the used random function is a two-dimensional uniformly distributed random function, and the interval of the random function is set according to the area coordinate.
8. The method for simulation evaluation of stone-chip resistance of automotive coatings according to claim 1, wherein in step S4, since the impact velocity and the impact angle of the particles are distributed regularly along the vertical direction, the regions with the same vertical coordinate are regarded as the same group of regions, and at least 5 simulation times t are ensured in each group of regions 1 Internally simulating the obtained particle impact points; the impact speed and the impact angle of the particle impact points in the same group of areas are counted together;
when the impact speed and the impact angle of the particles are determined, the used random function is a uniformly distributed random function, the interval of the random function is determined according to the mean value and the extreme value of the impact speed of the impact points of the same group of particles and the mean value and the extreme value of the impact angle of the impact points, and the range is specifically determined as (A-R/2, A + R/2), wherein A represents the mean value, and R represents the extreme value;
when the mean value A and the extreme value R are calculated, particle impact points with impact angles larger than 110% of the median impact angle or less than 90% of the median impact angle are ignored as error samples.
9. The method according to claim 8, wherein in step S5, the wear model is a Finnie wear model, and the wear quality of the coating template is calculated by the following formula:
Figure FDA0003710461410000031
wherein EM is wear mass; k is a constant obtained by calibration of simulation and test; v. of p Is the magnitude of the particle impact velocity; c is a vector pointing from the particle centroid to the impact point; t is t c Total contact time of the particles with the wall surface, f c Is the contact force of the particles with the wall surface for the simulation time t 1 For the particle impact point internally calculated by simulation, t c And f c For intermediate variables in the simulation calculation process, for t-t 1 For the point of impact of the particle predicted by a random function over time, t c And f c All get the simulation time t 1 T of particle impact point calculated by internal simulation c And f c The mean value of (a); f (γ) is a dimensionless function of the impingement angle, expressed as:
Figure FDA0003710461410000032
gamma is the impact angle between the particle impact trajectory and the wall.
10. The method for simulation evaluation of stone chip resistance of automobile coating combined with random function according to any one of claims 1 to 9, wherein in step S5, grids are divided on the coating sample plate, and the damage caused by particle impact at different positions is quantified by wear quality and is included in the grids at corresponding positions;
finally, after-treatment software is used for presenting the simulated coating sample plate in the form of a wear quality cloud chart, so that comparison with a test result is facilitated.
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