Rubber tire detection method based on flow state simulation

Abstract

The invention relates to the technical field of tire detection, in particular to a rubber tire detection method based on flow state simulation, and the method comprises the steps: obtaining the information of a rubber tire to be detected, building a three-dimensional geometric model, and determining a grid processing method for the three-dimensional geometric model based on the complexity of the rubber tire in the three-dimensional geometric model; performing numerical simulation on a discrete grid obtained after gridding processing by using computational fluid dynamics (CFD) software; evaluating the dynamic balance of the tire according to a plurality of parameters in the numerical simulation result, and determining a data verification method for the simulation data result of the rubber tire of the batch based on the fluctuation amplitude of the evaluation value of the dynamic balance of the tire of the batch in a preset period; and determining whether to adjust the parameters of the rubber tire detection process based on the matching degree of the verification data and the simulation data in the corresponding data verification method. According to the method, the accuracy of rubber tire detection is improved by improving the control degree of the flow state simulation process of the tire.

Description

Technical Field

[0001] This invention relates to the field of tire testing technology, and in particular to a method for testing rubber tires based on flow simulation. Background Technology

[0002] Rubber tires are an important component of transportation vehicles, and people have increasingly higher requirements for tire performance and quality. Traditional testing methods may have some limitations, such as high cost and low efficiency. Traditional tire testing requires a lot of time, manpower and material resources, while flow simulation-based methods can be performed on computers, saving a lot of experimental costs, improving efficiency and being more environmentally friendly. However, traditional flow simulation-based rubber tire testing methods have problems with the establishment and calibration of models for tires with complex structures, which leads to the prediction results not matching the actual situation.

[0003] Chinese Patent Application Publication No. CN116818193A discloses a method for detecting tire dynamic balance, relating to the field of sheet metal processing, comprising the following steps: detecting the flatness of the road surface on which the vehicle is driven; detecting the dynamic balance of the vehicle tires; this method for detecting tire dynamic balance, by detecting the flatness of the road surface on which the vehicle is driven and simultaneously detecting the dynamic balance of the vehicle tires, can determine whether the wheels are deflected, and whether the wheel deflection is caused by the wheel itself or by the road surface. At the same time, by detecting the tire rotation angle, it can determine whether multiple wheels have incorrect tire rotation angles during driving. Furthermore, by monitoring the movement distance of multiple tires, it can determine the movement and state of the wheels. By planning a suitable driving route based on the flatness of the road surface and the dynamic balance state of the tires, it can ensure that the measurement is not aggravated by driving on an unsuitable route.

[0004] It is evident that existing technologies suffer from insufficient precision in controlling the flow simulation process of rubber tires, resulting in low accuracy in rubber tire testing. Summary of the Invention

[0005] To address this issue, the present invention provides a rubber tire detection method based on flow simulation, which overcomes the problem of low accuracy in rubber tire detection caused by insufficient precision in the flow simulation process control of rubber tires in the prior art.

[0006] To achieve the above objectives, the present invention provides a rubber tire testing method based on flow simulation, comprising: The process involves acquiring information about the rubber tire to be detected, establishing a three-dimensional geometric model based on the rubber tire information, and determining a meshing method for the three-dimensional geometric model based on the complexity of the rubber tire in the three-dimensional geometric model. The meshing method includes a structured meshing method and an unstructured meshing method. The discrete grid obtained after meshing was numerically simulated using computational fluid dynamics (CFD) software, and the boundary conditions of the simulated flow field were determined based on the working parameter requirements of the rubber tire and its own parameters. The dynamic balance of the tire is evaluated based on several parameters in the numerical simulation results, and a data verification method for the simulated data results of the batch of rubber tires is determined based on the fluctuation range of the evaluation value of the dynamic balance of the batch of tires within a preset period. The data verification method includes a sampling verification method and a centralized verification method. Based on the matching degree between the verification data and the simulation data in the corresponding data verification method, and the conformity of the rubber tire profile when the matching degree between the verification data and the simulation data is less than the preset matching degree, it is determined whether to adjust the parameters of the rubber tire inspection process.

[0007] Furthermore, determining the meshing method for the three-dimensional geometric model includes: Based on the comparison results of the complexity of the rubber tire in the three-dimensional geometric model being greater than or equal to the preset complexity, it is determined that the three-dimensional geometric model will be processed by the unstructured meshing method. Based on the comparison results showing that the complexity of the rubber tire in the 3D geometric model is less than the preset complexity, the 3D geometric model is determined to be processed using a structured meshing method.

[0008] Furthermore, the complexity of the rubber tire in the three-dimensional geometric model is determined based on the number of rubber tire contours and the average curvature change, and the preset complexity is determined based on the average complexity of the rubber tire during historical testing.

[0009] Furthermore, the steps for numerically simulating the discrete mesh obtained after meshing using computational fluid dynamics (CFD) software include: Import the meshed discrete grid into the computational fluid dynamics software; Define a physical model in the computational fluid dynamics software; The boundary conditions of the simulated flow field are determined based on the working parameters and inherent parameters of the rubber tire. Set the simulation parameters and tire conditions, and start the numerical simulation calculation.

[0010] Furthermore, the steps for evaluating the dynamic balance of the tire based on several parameters from the numerical simulation results include: Obtain several key parameters from the numerical simulation results; Based on the dynamic balance analysis of rubber tires, several key parameters were analyzed. The dynamic balance of the rubber tire is evaluated based on the aforementioned key parameters.

[0011] Furthermore, when determining the data validation method for the flow simulation-based rubber tire testing method, the following are included: The simulated data results of the batch of rubber tires are determined by a centralized verification method based on the comparison results of the fluctuation range of the evaluation value of the dynamic balance of the rubber tires within the preset period being greater than or equal to the preset fluctuation range. The simulated data results of the batch of rubber tires are determined by sampling verification method based on the comparison results of the fluctuation range of the evaluation value of the dynamic balance of the rubber tires within the preset period being less than the preset fluctuation range.

[0012] Furthermore, when verifying the simulated data results of the batch of rubber tires using the centralized verification method, a first preset number of rubber tires in the batch are selected for continuous actual measurement verification; when verifying the simulated data results of the batch of rubber tires using the sampling verification method, a second preset number of rubber tires in the batch are selected for interval actual measurement verification.

[0013] Furthermore, determining whether to adjust the parameters of the rubber tire testing process includes: Based on the comparison results of the verification data and simulation data in the corresponding data verification method, which show that the matching degree is greater than or equal to the preset matching degree, it is determined that no adjustment of the parameters of the rubber tire testing process is required. Based on the comparison results of the verification data and simulation data in the corresponding data verification method, which show that the matching degree is less than the preset matching degree, a secondary judgment is made to determine whether to adjust the parameters of the rubber tire testing process.

[0014] Furthermore, the determination of whether to conduct a secondary assessment of the parameters used in the rubber tire testing process includes: Based on the comparison results of the rubber tire profile qualification degree being greater than or equal to the preset profile qualification degree when the matching degree between the verification data and the simulation data is less than the preset matching degree, it is determined that the parameters of the rubber tire inspection process need to be adjusted. The parameters for the rubber tire testing process include preset fluctuation range and preset complexity.

[0015] Furthermore, the adjustment amount of the preset fluctuation amplitude and the matching degree between the verification data and the simulation data are negatively correlated, and the matching degree between the adjustment amount of the preset complexity and the verification data and the simulation data is less than the matching degree of the rubber tire profile qualification is negatively correlated.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention calculates the complexity by considering the number of contours of the rubber tire and the average curvature change of the rubber tire, and determines the meshing method for the three-dimensional geometric model based on the complexity. If the complexity is greater than or equal to a preset complexity, it indicates that the geometry of the rubber tire may be more complex, thus requiring a more refined meshing method to accurately capture its shape, structure, and features, thereby improving the accuracy and precision of the simulation. Based on the comparison results of complexity, an appropriate meshing method can effectively balance computational accuracy and computational efficiency. For more complex rubber tire models, selecting a suitable mesh density and algorithm can reduce computational costs while ensuring accuracy. A suitable meshing method can improve the stability and convergence of the simulation. Especially when dealing with complex geometries, by adjusting the meshing strategy according to the actual complexity, numerical oscillations and convergence difficulties during the simulation process can be avoided. Refined meshing helps to better display the geometric details and features of the rubber tire, improve the visualization effect of the model, and enable users to observe and analyze the model more clearly.

[0017] Furthermore, this invention can obtain key parameters of the tire during operation, such as contact area, air pressure, and rolling resistance, through numerical simulation, thereby accurately evaluating the tire's dynamic balance. This helps to accurately understand the tire's performance under different conditions. Based on the numerical simulation results, the tire design can be optimized. By analyzing the impact of parameter changes on dynamic balance, targeted improvement schemes can be formulated to improve the tire's balance and stability. The numerical simulation results provide tire performance data under different operating conditions, which can be used to predict tire driving stability, fuel efficiency, and other indicators in actual use, helping to formulate more reasonable usage strategies. Evaluating the tire's dynamic balance through numerical simulation can avoid unnecessary trial and error and redesign, thereby saving costs and time. Numerical simulation can evaluate multiple parameters in a short time and quickly obtain results, which helps to accelerate the design iteration process and improve work efficiency. The numerical simulation results provide scientific data support, providing an objective basis for tire dynamic balance evaluation, and helping to accurately judge the tire's performance under different operating conditions. The above methods improve the accuracy of flow simulation process control of rubber tires, thereby improving the accuracy of rubber tire testing.

[0018] Furthermore, this invention determines a data verification method for the rubber tire detection method based on flow simulation by comparing the fluctuation range of the evaluation value of the dynamic balance of the rubber tire within a preset period with a preset fluctuation range. If the fluctuation range is greater than or equal to the preset fluctuation range, it indicates that the detection result is unstable, and the simulation data result of the batch of rubber tires is verified by a centralized verification method. If the fluctuation range is less than the preset fluctuation range, it indicates that the detection result is stable, and the simulation data result of the batch of rubber tires is verified by a sampling verification method. The above method improves the accuracy of the flow simulation process control of rubber tires, thereby improving the accuracy of rubber tire detection.

[0019] Furthermore, this invention determines whether to adjust the rubber tire inspection process parameters based on the comparison result of the matching degree between the verification data and the simulated data in the corresponding data verification method and the preset matching degree. If the matching degree is greater than or equal to the preset matching degree, it indicates that the inspection result is similar to the actual inspection result, and the rubber tire inspection process parameters are not adjusted at this time. If the matching degree is less than the preset matching degree, it determines whether to adjust the rubber tire inspection process parameters based on the comparison result of the rubber tire profile qualification degree (where the matching degree between the verification data and the simulated data is less than the preset matching degree) and the preset profile qualification degree. If the profile qualification degree is greater than or equal to the preset profile qualification degree, it indicates that the inspection result is not similar to the actual result. This indicates that the matching degree is less than the preset matching degree due to excessive selection of centralized verification method caused by the large preset fluctuation range, or that the detection result is inaccurate due to the large preset complexity of the rubber tire, which leads to the use of structured grid processing method to process complex rubber tires. At this time, it is determined to reduce the preset fluctuation range with a first adjustment coefficient and reduce the preset complexity with a second adjustment coefficient. Through the above method, the accuracy of the flow simulation process control of the rubber tire is improved, thereby improving the accuracy of rubber tire inspection. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the workflow of the rubber tire testing method based on flow simulation according to an embodiment of the present invention. Figure 2 This is a flowchart illustrating the process of determining the verification method for simulated rubber tire data results in the flow simulation-based rubber tire testing method of this invention. Figure 3 This is a flowchart illustrating the process of determining whether to adjust the parameters of the rubber tire inspection process in the flow simulation-based rubber tire inspection method of this invention. Detailed Implementation

[0021] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0022] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0023] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0024] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0025] Please see Figures 1-3 As shown, Figure 1 This is a flowchart illustrating the workflow of the rubber tire testing method based on flow simulation according to an embodiment of the present invention. Figure 2 This is a flowchart illustrating the process of determining the verification method for simulated rubber tire data results in the flow simulation-based rubber tire testing method of this invention. Figure 3 This is a flowchart illustrating the process of determining whether to adjust the parameters of the rubber tire inspection process in the flow simulation-based rubber tire inspection method of this invention.

[0026] The present invention provides a rubber tire testing method based on flow simulation, comprising: Step S1: Obtain the information of the rubber tire to be detected, establish a three-dimensional geometric model based on the rubber tire information, and determine the meshing method for the three-dimensional geometric model based on the complexity of the rubber tire in the three-dimensional geometric model. The meshing method includes a structured meshing method and an unstructured meshing method. Step S2: Use computational fluid dynamics (CFD) software to perform numerical simulation on the discrete grid obtained after gridding, and determine the boundary conditions of the simulated flow field based on the working parameter requirements of the rubber tire and its own parameters. Step S3: Evaluate the dynamic balance of the tires based on several parameters in the numerical simulation results, and determine the data verification method for the simulated data results of the batch of rubber tires based on the fluctuation range of the evaluation value of the dynamic balance of the batch of tires within a preset period. The data verification method includes a sampling verification method and a centralized verification method. Step S4: Based on the matching degree between the verification data and the simulation data in the corresponding data verification method, and the conformity of the rubber tire profile when the matching degree between the verification data and the simulation data is less than the preset matching degree, determine whether to adjust the parameters of the rubber tire inspection process.

[0027] Specifically, in step S1, when determining the meshing method for the three-dimensional geometric model, the meshing method for the three-dimensional geometric model is determined based on the comparison result between the complexity G of the rubber tire in the three-dimensional geometric model and the preset complexity G0. When G≥G0, the three-dimensional geometric model is determined to be processed using an unstructured meshing method; When G < G0, the three-dimensional geometric model is determined to be processed using a structured meshing method; The preset complexity level G0 is the average value of the complexity of rubber tires during historical testing. However, the above value is not limited to this, and those skilled in the art can adjust the value according to actual needs.

[0028] Specifically, the complexity G of the rubber tire in the three-dimensional geometric model is calculated by the following formula, which is set as follows: ; Wherein, N represents the number of contours of the rubber tire, N0 represents the preset number of contours, α represents the influence coefficient of the number of contours on the complexity, and its value is preferably 0.52, S represents the average curvature change of the rubber tire, S0 represents the preset average curvature change, and β represents the influence coefficient of the average curvature change on the complexity, and its value is preferably 0.48.

[0029] In this embodiment of the invention, the preset number of contours is the maximum number of contours of the rubber tire during the historical simulation process, and the preset average curvature change is the maximum value of the average curvature change of the rubber tire during the historical simulation process.

[0030] This invention calculates the complexity of a rubber tire by considering the number of contours and the average curvature change of the tire. Based on this complexity, it determines the meshing method for the 3D geometric model. If the complexity is greater than or equal to a preset level, the tire's geometry is likely more complex, requiring a finer meshing method to accurately capture its shape, structure, and features, thereby improving simulation accuracy. By comparing the complexity levels, an appropriate meshing method can effectively balance computational accuracy and efficiency. For more complex tire models, selecting suitable mesh density and algorithms can reduce computational costs while maintaining accuracy. A suitable meshing method can improve simulation stability and convergence, especially when dealing with complex geometries. By adjusting the meshing strategy according to the actual complexity, numerical oscillations and convergence difficulties during simulation can be avoided. Fine meshing helps to better display the geometric details and features of the tire, improving the model's visualization and allowing users to observe and analyze it more clearly.

[0031] Specifically, in step S2, the step of performing numerical simulations on the discrete mesh obtained after meshing using computational fluid dynamics (CFD) software includes: Import the meshed discrete grid into the computational fluid dynamics software; Define a physical model in the computational fluid dynamics software; The boundary conditions of the simulated flow field are determined based on the working parameters and inherent parameters of the rubber tire. Set the simulation parameters and tire conditions, and start the numerical simulation calculation.

[0032] The physical model described in this embodiment of the invention includes, but is not limited to, "turbulence model, physical properties and basic equations". The working parameters of the rubber tire include the dynamic balance requirements of the working environment of the rubber tire. The self-parameters include, but are not limited to, "tread texture, velocity and tire geometric parameters". The boundary conditions include, but are not limited to, "inlet boundary conditions, outlet boundary conditions and wall boundary conditions". The simulation parameters include, but are not limited to, "mesh parameters, simulation time step and total time and turbulence model parameters". The tire state includes motion rotation and stationary states.

[0033] Specifically, in step S3, the step of evaluating the dynamic balance of the tire based on several parameters from the numerical simulation results includes: Obtain several key parameters from the numerical simulation results; Based on the dynamic balance analysis of rubber tires, several key parameters were analyzed. The dynamic balance of the rubber tire is evaluated based on the aforementioned key parameters.

[0034] The key parameters mentioned in this embodiment of the invention include, but are not limited to, "tire deformation and stress distribution, tire-ground contact area and pressure distribution, tire rolling resistance and friction characteristics, tire air pressure and load conditions." The calculation of the dynamic balance evaluation value of the rubber tire based on these key parameters includes selecting a number of key parameters to calculate the evaluation value of the rubber tire's dynamic balance. For example, if the tire-ground contact area, tire air pressure, and tire rolling resistance are selected as key parameters to calculate the evaluation value H of the rubber tire's dynamic balance, then the evaluation value... Wherein, B1 represents the contact area between the tire and the ground, B10 represents the preset contact area, the value of which is preferably nine-tenths of the maximum contact area between the tire and the ground of the same size in the historical testing process, B2 represents the tire pressure, B20 represents the preset tire pressure, the value of which is preferably the average value of the tire pressure of the same size in the historical testing process, B200 is one-tenth of the preset tire pressure, B3 represents the rolling resistance of the tire, B30 represents the preset rolling resistance, the value of which is preferably the average value of the rolling resistance of the same size in the historical testing process, B300 is one-tenth of the preset rolling resistance, but the above values ​​are not limited to these, and those skilled in the art can adjust the values ​​according to actual needs.

[0035] This invention uses numerical simulation to obtain key parameters of a tire during operation, such as contact area, air pressure, and rolling resistance, thereby accurately evaluating the tire's dynamic balance. This helps to accurately understand the tire's performance under different conditions. Based on the numerical simulation results, tire design can be optimized. By analyzing the impact of parameter changes on dynamic balance, targeted improvement schemes can be formulated to improve tire balance and stability. The numerical simulation results provide tire performance data under different operating conditions, which can be used to predict tire performance in actual use, such as driving stability and fuel efficiency, helping to formulate more reasonable usage strategies. Evaluating tire dynamic balance through numerical simulation can avoid unnecessary trial and error and redesign, thus saving costs and time. Numerical simulation can evaluate multiple parameters in a short time and quickly obtain results, which helps to accelerate the design iteration process and improve work efficiency. The numerical simulation results provide scientific data support and objective basis for tire dynamic balance evaluation, helping to accurately judge the tire's performance under different operating conditions. The above methods improve the accuracy of flow simulation process control of rubber tires, thereby improving the accuracy of rubber tire testing.

[0036] Specifically, in step S3, when determining the data verification method for the rubber tire detection method based on flow simulation, the data verification method for the simulated data results of the batch of rubber tires is determined based on the comparison result of the fluctuation amplitude P of the evaluation value of the dynamic balance of the rubber tire within a preset period and the preset fluctuation amplitude P0. When P≥P0, the simulated data results of the batch of rubber tires are determined to be verified by the centralized verification method; When P < P0, the simulated data results of the batch of rubber tires are determined to be verified by sampling verification method; The preset fluctuation range P0 is the average value of the fluctuation range of the dynamic balance evaluation value of the rubber tire within a number of preset periods. However, the above value is not limited to this, and those skilled in the art can adjust the value according to actual needs.

[0037] In this embodiment of the invention, the preset period is preferably 4 hours, and the fluctuation range P of the evaluation value of the dynamic balance of the rubber tire within the preset period is the variance of the evaluation value of the dynamic balance of the rubber tire calculated within the preset period.

[0038] Specifically, when verifying the simulated data results of the batch of rubber tires using a centralized verification method, a first preset number of rubber tires in the batch are selected for continuous actual measurement verification. When verifying the simulated data results of the batch of rubber tires using a sampling verification method, a second preset number of rubber tires in the batch are selected for interval actual measurement verification.

[0039] In this embodiment of the invention, the first preset quantity is preferably one-tenth of the number of rubber tires in the batch, and the second preset quantity is preferably one-twentieth of the number of rubber tires in the batch. However, the above values ​​are not limited to these, and those skilled in the art can adjust the values ​​according to actual needs.

[0040] This invention determines a data verification method for the flow simulation-based rubber tire testing method by comparing the fluctuation range of the evaluation value of the dynamic balance of rubber tires within a preset period with a preset fluctuation range. If the fluctuation range is greater than or equal to the preset fluctuation range, it indicates that the test result is unstable, and the simulation data result of the batch of rubber tires is verified by a centralized verification method. If the fluctuation range is less than the preset fluctuation range, it indicates that the test result is stable, and the simulation data result of the batch of rubber tires is verified by a sampling verification method. The above method improves the accuracy of flow simulation process control of rubber tires, thereby improving the accuracy of rubber tire testing.

[0041] Specifically, in step S4, when determining whether to adjust the parameters of the rubber tire testing process, the determination is made based on the comparison result of the matching degree M between the verification data and the simulation data in the corresponding data verification method and the preset matching degree M0. When M≥M0, it is determined that no adjustment is needed to the parameters of the rubber tire testing process; When M < M0, a secondary determination is made to determine whether to adjust the parameters of the rubber tire testing process.

[0042] In this embodiment of the invention, the matching degree between the simulated data and the verification data of the rubber tires is the ratio of the number of rubber tires in the verification data whose average evaluation value of dynamic balance differs from that in the simulated data by no more than a preset difference to the total number of tires in the verification data. The preset difference is preferably one-tenth of the average evaluation value. The preset matching degree M0 is set to a range of 0.6-1, and the value of the preset matching degree M0 is preferably 0.75. However, the above values ​​are not limited to these values, and those skilled in the art can adjust the values ​​according to actual needs.

[0043] Specifically, when making a secondary judgment on whether to adjust the parameters of the rubber tire inspection process, the determination of whether to adjust the parameters of the rubber tire inspection process is based on the comparison result of the rubber tire profile qualification degree W (where the matching degree between the verification data and the simulation data is less than the preset matching degree) and the preset qualification degree W0. When W≥W0, it is determined that the parameters of the rubber tire testing process need to be adjusted; When W < W0, it is determined that no adjustment is needed to the parameters of the rubber tire testing process.

[0044] The parameters of the rubber tire inspection process in this embodiment of the invention include a preset fluctuation range and a preset complexity. The rubber tire profile qualification is the similarity between the actual profile of the rubber tire and the profile of the rubber tire in the three-dimensional geometric model. The preset qualification W0 is set to a value range of 0.7-1, and the preferred value of the preset qualification W0 is 0.82. However, the above value is not limited to this. Those skilled in the art can also adjust the value according to actual needs.

[0045] Specifically, when it is determined that the parameters of the rubber tire testing process need to be adjusted, the preset fluctuation range is adjusted by a first adjustment coefficient K1, and the preset complexity is adjusted by a second adjustment coefficient K2.

[0046] Specifically, the first adjustment coefficient K1 is calculated and set by the following formula: ; The second adjustment factor K2 is calculated and set by the following formula: ; Set the adjusted preset fluctuation range P0' to P0' = K1 × P0; Set the adjusted preset complexity G0' to G0' = K2 × G0.

[0047] This invention determines whether to adjust the parameters of the rubber tire inspection process based on the comparison between the matching degree of the verification data and the simulated data in the corresponding data verification method and the preset matching degree. If the matching degree is greater than or equal to the preset matching degree, it indicates that the inspection result is similar to the actual inspection result, and the parameters of the rubber tire inspection process are not adjusted. If the matching degree is less than the preset matching degree, it determines whether to adjust the parameters of the rubber tire inspection process based on the comparison between the rubber tire profile qualification degree (where the matching degree of the verification data and the simulated data is less than the preset matching degree) and the preset profile qualification degree. If the profile qualification degree is greater than or equal to the preset profile qualification degree, it indicates that the inspection result is not similar to the actual result. This indicates that the matching degree is less than the preset matching degree due to excessive selection of centralized verification method caused by the large preset fluctuation range, or that the detection result is inaccurate due to the large preset complexity of the rubber tire, which leads to the use of structured grid processing method to process complex rubber tires. In this case, it is determined to reduce the preset fluctuation range with a first adjustment coefficient and reduce the preset complexity with a second adjustment coefficient. Through the above method, the accuracy of the flow simulation process control of the rubber tire is improved, thereby improving the accuracy of rubber tire inspection.

[0048] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0049] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for detecting rubber tires based on flow regime simulation, characterized in that, include: The process involves acquiring information about the rubber tire to be detected, establishing a three-dimensional geometric model based on the rubber tire information, and determining a meshing method for the three-dimensional geometric model based on the complexity of the rubber tire in the three-dimensional geometric model. The meshing method includes a structured meshing method and an unstructured meshing method. The discrete grid obtained after meshing was numerically simulated using computational fluid dynamics (CFD) software, and the boundary conditions of the simulated flow field were determined based on the working parameter requirements of the rubber tire and its own parameters. The dynamic balance of the tire is evaluated based on several parameters in the numerical simulation results, and a data verification method for the simulated data results of the batch of rubber tires is determined based on the fluctuation range of the evaluation value of the dynamic balance of the batch of tires within a preset period. The data verification method includes a sampling verification method and a centralized verification method. Based on the matching degree between the verification data and the simulation data in the corresponding data verification method, and the conformity of the rubber tire profile when the matching degree between the verification data and the simulation data is less than the preset matching degree, it is determined whether to adjust the parameters of the rubber tire inspection process.

2. The rubber tire testing method based on flow simulation according to claim 1, characterized in that, Determining the meshing method for the three-dimensional geometric model includes: Based on the comparison results of the complexity of the rubber tire in the three-dimensional geometric model being greater than or equal to the preset complexity, it is determined that the three-dimensional geometric model will be processed by the unstructured meshing method. Based on the comparison results showing that the complexity of the rubber tire in the 3D geometric model is less than the preset complexity, the 3D geometric model is determined to be processed using a structured meshing method.

3. The rubber tire testing method based on flow simulation according to claim 2, characterized in that, The complexity of the rubber tire in the three-dimensional geometric model is determined based on the number of contours of the rubber tire and the average curvature change. The preset complexity is determined based on the average complexity of the rubber tire during historical testing.

4. The rubber tire testing method based on flow simulation according to claim 3, characterized in that, The steps for numerical simulation of a discrete mesh obtained after meshing using computational fluid dynamics (CFD) software include: Import the meshed discrete grid into the computational fluid dynamics software; Define a physical model in the computational fluid dynamics software; The boundary conditions of the simulated flow field are determined based on the working parameters and inherent parameters of the rubber tire. Set the simulation parameters and tire conditions, and start the numerical simulation calculation.

5. The rubber tire testing method based on flow simulation according to claim 4, characterized in that, The steps for evaluating tire dynamic balance based on several parameters from numerical simulation results include: Obtain several key parameters from the numerical simulation results; Based on the dynamic balance analysis of rubber tires, several key parameters were analyzed. The dynamic balance of the rubber tire is evaluated based on the aforementioned key parameters.

6. The rubber tire testing method based on flow simulation according to claim 5, characterized in that, When determining the data validation method for the flow simulation-based rubber tire testing method, the following methods are included: The simulated data results of the batch of rubber tires are determined by a centralized verification method based on the comparison results of the fluctuation range of the evaluation value of the dynamic balance of the rubber tires within the preset period being greater than or equal to the preset fluctuation range. The simulated data results of the batch of rubber tires are determined by sampling verification method based on the comparison results of the fluctuation range of the evaluation value of the dynamic balance of the rubber tires within the preset period being less than the preset fluctuation range.

7. The rubber tire testing method based on flow simulation according to claim 6, characterized in that, When verifying the simulated data results of the batch of rubber tires using the centralized verification method, a first preset number of rubber tires in the batch are selected for continuous actual measurement verification. When verifying the simulated data results of the batch of rubber tires using the sampling verification method, a second preset number of rubber tires in the batch are selected for interval actual measurement verification.

8. The rubber tire testing method based on flow simulation according to claim 7, characterized in that, When determining whether to adjust the parameters of the rubber tire testing process, the following should be included: Based on the comparison results of the verification data and simulation data in the corresponding data verification method, which show that the matching degree is greater than or equal to the preset matching degree, it is determined that no adjustment of the parameters of the rubber tire testing process is required. Based on the comparison results of the verification data and simulation data in the corresponding data verification method, which show that the matching degree is less than the preset matching degree, a secondary judgment is made to determine whether to adjust the parameters of the rubber tire testing process.

9. The rubber tire testing method based on flow simulation according to claim 8, characterized in that, The process of determining whether to perform a secondary assessment of the parameters used in the rubber tire testing procedure includes: Based on the comparison results of the rubber tire profile qualification degree being greater than or equal to the preset profile qualification degree when the matching degree between the verification data and the simulation data is less than the preset matching degree, it is determined that the parameters of the rubber tire inspection process need to be adjusted. The parameters for the rubber tire testing process include preset fluctuation range and preset complexity.

10. The rubber tire testing method based on flow simulation according to claim 9, characterized in that, The adjustment amount of the preset fluctuation amplitude and the matching degree between the verification data and the simulation data are negatively correlated. The matching degree between the adjustment amount of the preset complexity and the verification data and the simulation data is less than the matching degree of the preset tire profile qualification is negatively correlated.

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