CN113343605B - Simulation benchmarking method for stone-impact resistance standard experiment of automobile coating - Google Patents

Simulation benchmarking method for stone-impact resistance standard experiment of automobile coating Download PDF

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CN113343605B
CN113343605B CN202110732650.5A CN202110732650A CN113343605B CN 113343605 B CN113343605 B CN 113343605B CN 202110732650 A CN202110732650 A CN 202110732650A CN 113343605 B CN113343605 B CN 113343605B
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臧孟炎
熊书春
钱嘉诚
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South China University of Technology SCUT
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Abstract

The invention discloses a simulation benchmarking method for a stone-impact resistance standard experiment of an automobile coating, which comprises the following steps of: s1, performing a coating sample plate multi-particle impact experiment according to the two coating stone-impact resistance experiment standard systems, and processing experiment result data; s2, dividing flow field grids according to the sizes of the two experimental devices respectively, and establishing a CFD model; s3, setting particle materials and size parameters according to the requirements of the two experiments, and establishing a DEM model; s4, arranging a Finnie abrasion model on the coating sample plate; s5, establishing a CFD-DEM coupling calculation model; s6, simulating two standard experimental processes based on the CFD-DEM coupling method to obtain the motion states of flow fields and particles in the standard experimental processes, and processing simulation result data; and S7, evaluating the accuracy and effectiveness of the CFD-DEM coupling method according to the experimental result data and the simulation result data.

Description

Simulation benchmarking method for stone-impact resistance standard experiment of automobile coating
Technical Field
The invention relates to an automobile coating stone-impact resistance performance experiment and simulation, in particular to a simulation benchmarking method of an automobile coating stone-impact resistance standard experiment.
Background
Due to the complexity of the coating materials, until now, the research on the stone-chip resistance of the coating has mainly relied on experiments. However, no special standard is issued in China for specification, no uniform test and evaluation method exists in each large automobile company, and the detection standards of the stone-impact resistance of coatings adopted abroad are not uniform. At present, the coating stone-impact resistance test and evaluation method mainly comprises two standard systems abroad: german DIN systems and American SAE systems, and research work was limited to the solid mechanics range where particles impact a coating specimen to cause damage (Turkey. automotive coating Standard and influencing factor [ J ]. Shanghai coating, 2008(03): 41-43.). The whole experimental process in a standard experimental setup is a complex multi-physics problem involving solids, particles and fluids.
At present, no simulation method is used for replacing experiments to research the stone-impact resistance of the automobile coating. And because of the non-repeatability of the experiment, the information such as the particle speed, the position and the like of each experiment can not be directly obtained through an experimental measurement method, and the simulation of the particle impact coating can not be carried out (Chilobrachys warrior. mechanical property experimental study [ D ] of the vehicle body coating, university of south China's science 2020.). A CFD-DEM coupling simulation method is adopted to obtain state parameters of a flow field and a particle field in the experimental process so as to be further applied to the evaluation of the stone-impact resistance of the coating; and carrying out benchmarking evaluation on the CFD-DEM coupling method, and verifying the effectiveness and accuracy of the simulation experiment process.
Disclosure of Invention
The invention provides a simulation benchmarking method for a standard test of stone-impact resistance of an automobile coating, aiming at solving the problem that the stone-impact resistance of the automobile coating cannot be simulated and evaluated in the existing method.
The purpose of the invention is realized by at least one of the following technical solutions.
A simulation benchmarking method for an automobile coating stone-impact resistance standard experiment comprises the following steps:
s1, carrying out a multi-particle impact experiment on the coating sample plate to obtain experiment result data;
s2, dividing flow field grids according to the sizes of experimental devices respectively, and establishing a CFD model of the experimental device;
s3, setting the diameter, density, Young modulus and Poisson ratio of the particles according to the experimental requirements, and establishing a DEM model of the particles;
s4, arranging a Finnie abrasion model on the coating sample plate;
s5, establishing a CFD-DEM coupling calculation model in an open source software framework CFDEM;
s6, performing simulation on the standard experiment process based on the CFD-DEM coupling method to obtain the motion states of a flow field and particles in the standard experiment process to obtain simulation result data;
and S7, evaluating the accuracy and effectiveness of the CFD-DEM coupling method according to the experimental result data and the simulation result data.
Further, in step S1, the movement process of the particles is photographed by a high-speed camera to obtain a distribution state diagram of the particles in the box of the experimental apparatus, a plurality of particles are randomly selected, and the horizontal velocity value before the particles impact the coating sample plate is estimated according to the displacement of the particles between two frames.
Further, in step S2, a CFD model required for simulation is established in OpenFOAM software, and a flow field mesh is divided corresponding to the experimental apparatus.
Further, in step S3, a DEM model required for simulation is established in the light software, and large-size particles are formed by a method of rigid binding of small spherical particles;
the method for rigid bonding of the small spherical particles is characterized in that a plurality of small spheres are bound together through rigid body to form a multi-sphere model without overlapping amount; firstly, counting the size and position information of all small balls, and calculating the mass center and the rotational inertia of the formed large particles:
Figure BDA0003139627390000021
Figure BDA0003139627390000022
Figure BDA0003139627390000031
wherein m iscIs the total mass of the entire rigidified composite particle, XcAs a vector of global centroid coordinates, IcIs the integral moment of inertia; m isiFor the mass of each pellet, XiA centroid coordinate vector for each particle; n is the number of the formed pellets;
in the particle motion process, the total force and moment acting on the whole combined particles are correspondingly calculated based on the fluid acting force, gravity and contact force borne by each combined ball, the integral particle motion equation is solved, and the integral speed and angular speed are obtained.
Further, in the Finnie erosion wear model, the wear rate is related to the kinetic energy of the particle striking the surface:
Figure BDA0003139627390000032
wherein E is a dimensionless mass, vpIs the magnitude of the particle impact velocity, k is a constant, γ is the impact angle between the impact trajectory and the wall surface, and f (γ) is a dimensionless function of the impact angle, expressed as:
Figure BDA0003139627390000033
further, in step S5, the CFD-DEM coupled computation model is a parallel bidirectional CFD-DEM coupled solver; the method comprises the steps of realizing interaction and momentum exchange between fluid and particles based on a coupling model, calculating the volume fraction occupied by the particles in the CFD grid based on a segmentation method, calculating the drag force of the fluid acting on each small spherical particle by adopting a Gidapow drag force model or a Di Felice drag force model, calculating the lift force of the fluid acting on each particle under the condition of high Reynolds number by adopting a Saffman lift force model, and correcting the force model to ensure the accuracy of the movement of the rigid large particles.
Further, in step S6, the data post-processing is performed by using Paraview software to obtain a distribution state diagram of particles in the box of the simulation result, and a plurality of particles are randomly selected to derive a horizontal velocity value before the particles impact the coating template.
Further, in step S7, the accuracy of the CFD-DEM coupling method is evaluated according to the difference between the simulated and experimental horizontal velocity values.
Further, in step S7, the effectiveness of the CFD-DEM coupling method is evaluated according to the number and distribution rule of particles in the box body of the simulation and experiment.
Further, in step S7, the effectiveness and accuracy of the CFD-DEM coupling method are simultaneously evaluated according to the wear position of the coating sample plate impacted by the particles and the range of the impacted point of the coating sample plate in the experiment obtained through simulation.
Compared with the prior art, the invention has the following advantages and effects:
the method can obtain the motion parameters of the particles in the stone-impact resistance performance test of the automobile coating by using a simulation analysis method, and verifies and evaluates the effectiveness and accuracy of the simulation method by using a proper simulation and test result calibration method. The CFD-DEM coupling method can be used for accurately and efficiently carrying out simulation on the experimental process to obtain specific position and speed information when each particle impacts a coating sample, and a basis is provided for the evaluation of the stone impact resistance of the coating.
Drawings
FIG. 1 is a schematic diagram of an experimental setup of the German DIN standard system in an example of the present invention;
FIG. 2 is a diagram illustrating the effect of a large ball formed by the rigid knot of a small ball according to an embodiment of the present invention;
FIG. 3 is a graph comparing horizontal velocities of 20 randomly selected particles from experiments and simulations before impacting a coated sample in accordance with an embodiment of the present invention;
FIG. 4a is a graph showing the results of comparative experiments on the state of particle motion in the experimental and simulated box according to an embodiment of the present invention;
FIG. 4b is a graph of a simulation result comparing the motion states of particles in the experimental and simulated box according to an embodiment of the present invention;
FIG. 5a is a graph showing the results of comparative experiments on failure of the coating templates in the experiments and simulations of the present invention;
fig. 5b is a cloud graph of wear rates of pattern layers obtained by simulation in an embodiment 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.
Reference will now be made in detail to the present preferred embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to like elements throughout.
In the description of the present invention, it should be understood that the orientation or positional relationship referred to in the description of the orientation, such as the upper, lower, front, rear, left, right, etc., is based on the orientation or positional relationship shown in the drawings, and is only for convenience of description and simplification of description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
In the description of the present invention, the meaning of a plurality of means is one or more, the meaning of a plurality of means is two or more, and larger, smaller, larger, etc. are understood as excluding the number, and larger, smaller, inner, etc. are understood as including the number. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
The embodiments and features of the embodiments of the invention may be combined with each other without conflict.
A simulation benchmarking method for an automobile coating stone-impact resistance standard experiment comprises the following steps:
s1, carrying out a multi-particle impact experiment on the coating sample plate, shooting the particle motion process through a high-speed camera to obtain a particle distribution state diagram in a box body of the experimental device, randomly selecting a plurality of particles, and estimating a horizontal velocity value before the particles impact the coating sample plate according to the particle displacement between two frames;
at present, the coating stone-impact resistance test and evaluation method mainly comprises two standard systems abroad: the German DIN system and the American SAE system. In the two types of automobile coating multi-particle impact standard experiments, high-speed gas is obtained by utilizing high-pressure air of an air storage tank under a certain condition, then a certain amount of broken stone, steel balls or steel sand is driven to impact a coating sample within a certain time, and the stone impact resistance of the coating sample is evaluated according to the damage or peeling degree of the coating.
S2, dividing flow field grids according to the sizes of experimental devices respectively, and establishing a CFD model of the experimental device;
s3, setting particle materials and size parameters according to experimental requirements, establishing a DEM model, and forming large-size particles by a method of rigid bonding of small spherical particles; the method for rigid bonding of the small spherical particles is characterized in that a plurality of small spheres are bound together through rigid body to form a multi-sphere model without overlapping amount; firstly, counting the size and position information of all small balls, and calculating the mass center and the rotational inertia of the formed large particles:
Figure BDA0003139627390000061
Figure BDA0003139627390000062
Figure BDA0003139627390000063
wherein m iscIs the total mass of the entire rigidified composite particle, XcAs a vector of global centroid coordinates, IcIs the integral moment of inertia; m isiFor the mass of each pellet, XiA centroid coordinate vector for each particle; n is the number of the formed pellets;
in the particle motion process, the total force and moment acting on the whole combined particles are correspondingly calculated based on the fluid acting force, gravity and contact force borne by each combined ball, the integral particle motion equation is solved, and the integral speed and angular speed are obtained.
S4, arranging a Finnie abrasion model on the coating sample plate; in the Finnie erosion wear model, the wear rate is related to the kinetic energy of the particle striking the surface:
Figure BDA0003139627390000064
wherein E is a dimensionless mass, vpIs the magnitude of the particle impact velocity, k is a constant, γ is the impact angle between the impact trajectory and the wall surface, and f (γ) is a dimensionless function of the impact angle, expressed as:
Figure BDA0003139627390000065
s5, establishing a CFD-DEM coupling calculation model in an open source software framework CFDEM; the CFD-DEM coupling calculation model is a parallel bidirectional CFD-DEM coupling solver; the CFDEM coupling framework combines the advantages of fluid computing open source software OpenFOAM and discrete element computing open source software LIGGGHTS, and provides a parallel bidirectional CFD-DEM coupling solver.
The method comprises the steps of realizing interaction and momentum exchange between fluid and particles based on the existing coupling model, calculating the volume fraction occupied by the particles in the CFD grid based on a segmentation method, calculating the drag force of the fluid acting on each small spherical particle by adopting a Gidasslow drag force model or a Di Felice drag force model, calculating the lift force of the fluid acting on each particle under the condition of high Reynolds number by adopting a Saffman lift force model, and correcting the force model to ensure the accuracy of the movement of the rigid large particles.
S6, performing simulation on the standard experiment process based on the CFD-DEM coupling method to obtain the motion states of a flow field and particles in the standard experiment process to obtain simulation result data; and (3) carrying out data post-processing by using Paraview software to obtain a distribution state diagram of the particles in the box body of the simulation result, randomly selecting 20 particles, and deriving a horizontal velocity value before the particles impact the coating sample plate.
For accuracy in calculating fluid-particle interaction forces, the constituent spheres in the same particle do not overlap during the initial and entire simulation, there is no contact force between the spheres so that their relative positions do not change and the volume fraction calculation does not produce an overlap error.
S7, randomly selecting a plurality of particles from the experimental result and the simulation result respectively, comparing the horizontal speeds of the particles before impacting the coating sample, and evaluating the accuracy of the CFD-DEM coupling method; the horizontal velocity values of the simulation and the experiment have small difference and are all in a similar range, so that the CFD-DEM coupling method can be evaluated to have high accuracy.
S8, comparing the distribution state diagram of the particles in the box body obtained by shooting with a high-speed camera in the experimental process and obtained by processing with simulated Paraview software, wherein the quantity of the particles in the box body is basically the same as that in the box body in the experiment and the distribution rule is similar, and the CFD-DEM coupling method can be evaluated to have higher efficiency.
S9, comparing the damage condition of the coating sample plate after being impacted by the multi-particle in the experiment with the coating sample plate abrasion condition obtained according to the Finnie abrasion model in the simulation, and evaluating the effectiveness and the accuracy of the CFD-DEM coupling method.
Example one
The simulation benchmarking method for the stone-impact resistance standard experiment of the automobile coating specifically comprises the following steps:
(1) the coating stone-impact resistance test is carried out according to DIN standards, and the specific steps are as follows:
(a) setting and fixing an experimental device according to various size parameters required by DIN standards;
(b) setting the working pressure to be 0.2MPa, and selecting 500g of steel balls with the diameter of about 5 mm;
(c) setting a high-speed camera, starting an experiment with the time length of 10s and recording experimental data;
(2) establishing a CFD model in open source software OpenFOAM: dividing flowA field grid; the fluid is air and the density is 1.225kg/m3Dynamic viscosity is set to 1.79X 10-5Ns·m-2(ii) a Solving the flow field by using a standard k-epsilon turbulence model; the right-end pipe orifice is set as a speed inlet boundary, and the inlet air speed is 42.1 m/s; the outlet at the lower part of the box body is provided with a pressure outlet boundary (standard atmospheric pressure); the wall surface is set as a non-slip boundary; the CFD calculation time step is set to 2 x 10-6s; wherein the inner diameter D of the particle accelerating tube is 30mm, the distance between an outlet and the center of the coating sample plate is 290mm, and the included angle between the coating sample plate and the horizontal line is 54 degrees;
(3) as shown in fig. 2, a DEM model is established in open source software ligghts: 615 small balls with the diameter of 0.5mm are just knotted to form large ball particles with the diameter of 5 mm; the density of the pellets is 7890kg/m3Young's modulus of 2.0X 1011The Poisson ratio is 0.27, and the DEM calculation time step is set to be 2 multiplied by 10-7s；
(4) Arranging a Finnie abrasion model on the coating sample plate;
(5) in an open source software framework CFDEM, establishing a CFD-DEM coupling calculation model: calculating the particle volume fraction in the mesh using a "segmentation method"; calculating coupling drag force by adopting a Gidasow drag force model; calculating the lifting force of the fluid to the particles by adopting a Saffman lifting force model; correcting the force model by using the correction coefficient;
(6) simulating two standard experimental processes based on a CFD-DEM coupling method to obtain the motion states of a flow field and particles in the standard experimental processes, and processing simulation result data;
(7) as shown in fig. 3, the movement of the particle group is complicated in the experiment process due to the unrepeatability of the experiment and the frequent collisions between the particles and the wall surface during the movement of the plurality of particles. From the experiments and simulations, 20 particles were randomly selected and compared in terms of their horizontal velocity before impact on the coated sample. Due to the non-repeatability of the experiment, and the frequent collision among the particles and between the particles and the wall surface in the moving process of the particles, the horizontal velocity values of the 20 particles in the simulation and the experiment before the particles touch the coating sample are small in difference and are all in a similar range, and the CFD-DEM coupling method can be evaluated to have high accuracy.
Example two
In this embodiment, based on the first embodiment, another evaluation method is used instead, and a distribution state diagram of particles in a box, which is obtained by shooting with a high-speed camera in an experimental process and is obtained by processing with simulated Paraview software, is compared, as shown in fig. 4a and 4 b. Due to the complexity and the non-repeatability of the experiment, the particle groups in the box body are distributed in a mess manner in the process of impacting the coating sample by the particles; but the simulation is basically the same as the number of particles in the box body of the experiment and the distribution rule is similar, the effectiveness of the CFD-DEM coupling method can be evaluated.
EXAMPLE III
In this embodiment, based on the first embodiment, but using another evaluation method, a wear rate cloud chart of the coated sample plate after the wall surface is impacted by the multi-particles is obtained by using Paraview software post-processing, and the damage condition of the coated sample plate after the multi-particle impact in the experiment is compared with the wear condition of the coated sample plate obtained according to the Finnie wear model in the simulation, as shown in fig. 5a and 5 b. The wear position of the coating sample plate impacted by the particles obtained by simulation and the impact point of the coating sample plate in the experiment are in the basically consistent range, so that the CFD-DEM coupling method can be evaluated to have higher effectiveness and accuracy.
Example four
In an open source software framework CFDEM, establishing a CFD-DEM coupling calculation model: calculating the particle volume fraction in the mesh using a "segmentation method"; and (3) calculating the drag force of the fluid on the particles by adopting a Di Felice drag force model, wherein the simulation result is similar to the Gidasow drag force model.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims (7)

1. A simulation benchmarking method for an automobile coating stone-impact resistance standard experiment is characterized by comprising the following steps:
s1, carrying out a multi-particle impact experiment on the coating sample plate to obtain experiment result data;
carrying out a multi-particle impact experiment on the coating sample plate, shooting the particle motion process through a high-speed camera to obtain a particle distribution state diagram in a box body of the experimental device, randomly selecting a plurality of particles, and estimating a horizontal velocity value before the particles impact the coating sample plate according to the particle displacement between two frames;
s2, dividing flow field grids according to the sizes of experimental devices respectively, and establishing a CFD model of the experimental device;
establishing a CFD model in open source software OpenFOAM: dividing a flow field grid; the fluid is air and the density is 1.225kg/m3Dynamic viscosity is set to 1.79X 10-5Ns·m-2(ii) a Solving the flow field by using a standard k-epsilon turbulence model; the right-end pipe orifice is set as a speed inlet boundary, and the inlet air speed is 42.1 m/s; the outlet at the lower part of the box body is provided with a pressure outlet boundary (standard atmospheric pressure); the wall surface is set as a non-slip boundary; the CFD calculation time step is set to 2 x 10-6s; wherein the inner diameter D of the particle accelerating tube is 30mm, the distance between an outlet and the center of the coating sample plate is 290mm, and the included angle between the coating sample plate and the horizontal line is 54 degrees;
s3, setting the diameter, density, Young modulus and Poisson ratio of the particles according to the experimental requirements, and establishing a DEM model of the particles;
setting particle materials and size parameters according to the experimental requirements, establishing a DEM model, and forming large-size particles by a method of rigid bonding of small spherical particles; the method for rigid bonding of the small spherical particles is characterized in that a plurality of small spheres are bound together through rigid body to form a multi-sphere model without overlapping amount; firstly, counting the size and position information of all small balls, and calculating the mass center and the rotational inertia of the formed large particles:
Figure FDA0003463894150000011
Figure FDA0003463894150000012
Figure FDA0003463894150000013
wherein m iscIs the total mass of the entire rigidified composite particle, XcAs a vector of global centroid coordinates, IcIs the integral moment of inertia; m isiFor the mass of each pellet, XiA centroid coordinate vector for each particle; n is the number of the formed pellets;
in the particle motion process, the total force and moment acting on the whole combined particles are correspondingly calculated based on the fluid acting force, gravity and contact force borne by each component ball, the integral particle motion equation is solved, and the integral speed and angular speed are obtained;
establishing a DEM model in open source software LIGGGHTS: 615 small balls with the diameter of 0.5mm are just knotted to form large ball particles with the diameter of 5 mm; the pellet density was 7890kg/m3, and the Young's modulus was 2.0X 1011The Poisson ratio is 0.27, and the DEM calculation time step is set to be 2 multiplied by 10-7s；
S4, arranging a Finnie abrasion model on the coating sample plate;
s5, establishing a CFD-DEM coupling calculation model in an open source software framework CFDEM;
s6, performing simulation on the standard experiment process based on the CFD-DEM coupling method to obtain the motion states of a flow field and particles in the standard experiment process to obtain simulation result data;
and S7, evaluating the accuracy and effectiveness of the CFD-DEM coupling method according to the experimental result data and the simulation result data.
2. The method according to claim 1, wherein in the Finnie erosion wear model, the wear rate is related to the kinetic energy of the particles striking the surface:
Figure FDA0003463894150000021
wherein E is a dimensionless mass, vpIs the magnitude of the particle impact velocity, k is a constant, γ is the impact angle between the impact trajectory and the wall surface, and f (γ) is a dimensionless function of the impact angle, expressed as:
Figure FDA0003463894150000022
3. the simulation benchmarking method of the automobile coating rock-hammer resistance standard experiment as claimed in claim 2, wherein in step S5, the CFD-DEM coupling calculation model is a parallel bidirectional CFD-DEM coupling solver; the method comprises the steps of realizing interaction and momentum exchange between fluid and particles based on a coupling model, calculating the volume fraction occupied by the particles in the CFD grid based on a segmentation method, calculating the drag force of the fluid acting on each small spherical particle by adopting a Gidapow drag force model or a Di Felice drag force model, calculating the lift force of the fluid acting on each particle under the condition of high Reynolds number by adopting a Saffman lift force model, and correcting the force model to ensure the accuracy of the movement of the rigid large particles.
4. The method for calibrating the simulation of the stone chip resistance standard experiment of the automobile coating according to claim 3, wherein in step S6, Paraview software is used for data post-processing to obtain a distribution state diagram of particles in the box body of the simulation result, a plurality of particles are randomly selected, and a horizontal velocity value before the particles impact the coating sample plate is derived.
5. The simulation benchmarking method of the standard experiment of the stone chip resistance of the automobile coating as claimed in claim 4, wherein in step S7, the accuracy of the CFD-DEM coupling method is evaluated according to the difference between the horizontal velocity values of the simulation and the experiment.
6. The simulation benchmarking method of the standard test for stone chip resistance of the automobile coating as claimed in claim 5, wherein in step S7, the effectiveness of the CFD-DEM coupling method is evaluated according to the quantity and distribution rule of the particles in the box body of the simulation and test.
7. The method as claimed in claim 6, wherein in step S7, the effectiveness and accuracy of the CFD-DEM coupling method are evaluated simultaneously according to the wear position of the coating sample plate impacted by the particles and the range of the impact point of the coating sample plate in the test.
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