CN114564901A - Simulation evaluation method for stone-impact resistance of automobile coating by combining random function - Google Patents
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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 coupling1The 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 function1The area of particle impact over time and the specific impact location coordinates; the simulation time t in each area is counted1Inner particle impact velocity and impact angle; predicting t-t with random function respectively1Impact velocity and impact angle of the particles over time; the particle impact in the time t is equivalent to the abrasion quality on the coating sample plate to carry out coating stone resistanceEvaluation of pounding Properties. 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
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 by combining 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 operation1The position of all the particles in the coating sample, and the instantaneous speed and angle information of the particles impacting the coating sample, t1<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 t1The 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 function1The 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 region1Internally 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 with a random function1Impact velocity and impact angle of all particles within 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 gas flow physical parameters and turbulence models according to test conditions, constructing a multi-ball particle model according to the shape of the used impactor and setting physical parameters of particles, and setting particle-fluid two-phase acting 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 t1The position of all the particles in the coating sample, and the instantaneous speed and angle information of the particles impacting the coating sample, t1<t。
Further, in step S1, the time length t calculated by simulation 11/5-1/3 of the total duration t of the test phenomenon is taken, and meanwhile, the simulation time t must be ensured1The undercoating template withstood 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 t1The 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.
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 regions1Internally 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:
wherein,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 t1For the particle impact point calculated by simulation,t c andf c for intermediate variables in the simulation calculation process, for t-t1For a particle impact point predicted by a random function over time,t c andf c all get the simulation time t1Of points of impact of particles calculated by internal simulationt c Andf c the mean value of (a);is a dimensionless function of the angle of impact, expressed as:
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.
Drawings
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 according to DIN55996-1 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 description further illustrates the method and process of 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 gravel impact resistance tester used in 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 according to actual test conditions in open source CFD software OpenFOAM, and gas physical parameters during the test are set according to the actual test conditionsThe flow sets the turbulence pattern and boundary conditions of the port and wall, in this example, an air density of 1.225kg/m3Dynamic viscosity is set to 1.79X 10-5 Ns·m-2Use 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/m3Its 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, 1pt of cobble particles, namely about 210 particles, are required to be injected into the broken stone impact tester within 7s of the total test phenomenon. 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 t1The 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 then respectively predicting t-t by utilizing a random function1The 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 t1The 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 initial division is carried out in the direction parallel to the short side of the coating sample plate, 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, so that the area is equally divided again. And secondly, uniformly dividing the shot area of the sample plate for the second time in parallel to the long side 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 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 region1Internally 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 are ensured in each group of regions1Internally 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 quality on the coating sample plate is calculated by the following formula:
wherein,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 t1For the particle impact point calculated by simulation,t c andf c for intermediate variables in the simulation calculation process, for t-t1For a particle impact point predicted by a random function over time,t c andf c all get the simulation time t1Of points of impact of particles calculated by internal simulationt c Andf c the mean value of (a);is a dimensionless function of the angle of impact, expressed as:
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: strip according to the test standard for stone-impact resistance of automotive coatingsThe method comprises the following steps that a CFD-DEM coupling simulation model is arranged in an open source coupling frame CFDEM, and comprises a CFD mesh model, a turbulence model and a multi-spherical particle model; obtaining a period of simulation time t through coupling operation1The position of all the particles in the sample plate, and the instantaneous speed and angle information of the particles impacting the coating sample plate, t1<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 t1The 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 function1The 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 region1Internally 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 with a random function1Impact velocity and impact angle of all particles within 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.
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-hitting device in the simulation process by a CFD-DEM coupling simulation calculation method, thereby obtaining the particlesThe particles impact the instantaneous motion state of the surface of the coating sample plate, and then a period of simulation time t is obtained1The position of all the particles in the coating sample, and the instantaneous speed and angle information of the particles impacting the coating sample, t1<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 simulation11/5-1/3 of the total duration t of the test phenomenon is taken, and meanwhile, the simulation time t must be ensured1The undercoating template is subjected to at least 50 particle impacts.
4. The method as claimed in claim 1, wherein in step S2, when the two-dimensional coordinate system is established in the plane of the coating template, the center or four corners of the coating template are used as the origin of coordinates, and X, Y axes are established 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;
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.
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 determined1The 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 automobile coating combined with random function as claimed in claim 1, wherein in step S4, the impact velocity and impact angle of particles are verticalThe areas with the same vertical coordinate are regarded as the same group of areas with a distribution rule, and at least 5 simulation times t in each group of areas are ensured1Internally 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, the 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:
wherein,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 t1For the particle impact point calculated by simulation,t c andf c for intermediate variables in the simulation calculation process, for t-t1For a particle impact point predicted by a random function over time,t c andf c all get the simulation time t1Of points of impact of particles calculated by internal simulationt c Andf c the mean value of (a);is a dimensionless function of the angle of impact, expressed as:
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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