Quantitative evaluation and prediction method and system for road surface-tire vibration noise

Abstract

The invention provides a quantitative evaluation and prediction method and system for pavement-tire vibration noise, and relates to the technical field of pavement material design, and the method comprises the steps: obtaining pavement surface texture data; obtaining viscoelastic characteristic parameters through a dynamic modulus test; constructing a pavement-tire-air coupling finite element model; setting tire rolling working conditions including applied load, rolling speed and contact conditions, executing pavement-tire dynamic simulation, extracting vertical displacement time history data of the tire in the rolling process, and calculating a noise reduction quantitative index representing the pavement noise reduction capability; applying the vertical displacement time history data to the three-dimensional tire model as a forced displacement boundary condition, executing tire-air acoustic simulation, extracting a sound pressure signal of vibration noise, and performing frequency domain processing to obtain a predicted noise level; and evaluating the noise reduction performance of the pavement test piece with different surface textures and viscoelastic characteristic parameters based on the quantitative index and the noise level, and outputting a pavement material composition result with the optimal noise reduction capability.

Description

Technical Field

[0001] This invention relates to the field of road material design technology, and in particular to a method and system for quantitative assessment and prediction of road-tire vibration noise. Background Technology

[0002] At present, traffic noise has become a key bottleneck restricting the improvement of ecological environment quality and the construction of livable environment. Among them, the noise generated by tire-road coupling accounts for about 80% of traffic noise. Reducing tire / road noise can effectively solve the problem of traffic noise pollution.

[0003] However, current methods for reducing tire / road noise mainly rely on passive noise reduction measures such as sound barriers or numerical simulations based on simplified road surfaces. Passive measures involve huge investments and are only temporary solutions, while numerical simulations usually simplify the road surface as a rigid plane, completely ignoring its real texture excitation and material viscoelastic properties. This makes it impossible to separate the coupling effect between tire vibration noise and air pumping noise, resulting in low-noise road surface design relying on trial and error for a long time. It is difficult to separate and quantify the influence mechanism of the road surface itself on tire vibration noise at the noise source, and thus cannot effectively guide the material design of noise-reducing asphalt pavement. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for quantitative assessment and prediction of road-tire vibration noise, in order to solve the problem mentioned in the background art that the current methods for reducing tire / road noise mainly rely on passive noise reduction measures such as sound barriers or numerical simulation based on simplified road surfaces, which makes it difficult to separate and quantify the influence mechanism of the road surface itself on tire vibration noise at the noise source, and thus cannot effectively guide the material design of noise-reducing asphalt pavement.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for quantitative assessment and prediction of road-tire vibration noise, comprising the following steps: acquiring elevation point cloud data of the surface texture of a road test specimen; preprocessing the point cloud data to obtain road surface texture data; obtaining viscoelastic characteristic parameters of the road test specimen under different test temperatures and test frequencies through dynamic modulus tests; constructing a three-dimensional road surface model based on the road surface texture data and the viscoelastic characteristic parameters; constructing a three-dimensional tire model with a smooth tread based on preset tire parameters; incorporating air as a sound propagation carrier into the computational domain to construct an air domain model, thereby obtaining a road-tire-air coupled finite element model; and setting parameters including applied load and rolling speed. The system simulates tire rolling conditions under contact conditions and performs road-tire dynamics simulation. It extracts the vertical displacement time history data of the tire during rolling and calculates a noise reduction quantification index characterizing the road surface's noise reduction capability based on this data. The vertical displacement time history data is then applied as a forced displacement boundary condition to the three-dimensional tire model, and tire-aeroacoustic simulation is performed to drive the three-dimensional tire model to generate vibration noise. The sound pressure signal of the vibration noise is extracted and processed in the frequency domain to obtain the predicted noise level. Based on the noise reduction quantification index and the noise level, the noise reduction performance of road test specimens with different surface textures and viscoelastic properties is evaluated, and the optimal road material composition for noise reduction capability is output.

[0006] Optionally, the step of preprocessing the point cloud data specifically includes: considering measurement points in the point cloud data with elevation values ​​less than -10mm and greater than 10mm as invalid points; and repairing the invalid points using a linear interpolation method, the calculation formula of which is: In the formula: The elevation value of the invalid point. The coordinates of the invalid point. These are two valid measurement points before and after the invalid point. The elevations of the two valid measurement points before and after the invalid point are used; the tilt is removed by plane fitting to eliminate systematic errors in the scanning process.

[0007] Optionally, the step of obtaining the viscoelastic property parameters of the road test specimen under different test temperatures and test frequencies through dynamic modulus testing specifically includes: obtaining the dynamic modulus and phase angle of the road test specimen under different test temperatures and test frequencies through dynamic modulus testing; calculating the relaxation modulus based on the dynamic modulus and the phase angle, and constructing a relaxation modulus master curve based on the relaxation modulus; calculating the shear modulus ratio based on the relaxation modulus master curve; and fitting the Prony series parameters based on the shear modulus ratio, wherein the Prony series parameters include material constants and delay time.

[0008] Optionally, the step of constructing a three-dimensional road surface model based on the road surface texture data and the viscoelastic property parameters specifically includes: generating corresponding node coordinates from the road surface texture data according to spatial location and preset sampling precision, connecting nodes with three adjacent nodes as units to form a three-dimensional road surface mesh; generating a curved surface on the surface of the three-dimensional road surface mesh, and stretching the curved surface along the thickness direction to form a three-dimensional road surface model; assigning material properties to the three-dimensional road surface model, wherein the material properties include: dynamic modulus, Poisson's ratio, material constant, and delay time.

[0009] Optionally, the step of constructing a three-dimensional tire model based on preset tire parameters specifically includes: generating a three-dimensional tire model with no tread and a smooth tread according to the preset tire parameters, and assigning corresponding material parameters and inflation pressure to the tread, tire body and rim of the three-dimensional tire model respectively; adjusting the spatial position of the three-dimensional tire model to contact the upper surface of the three-dimensional road surface model, and setting the friction coefficient to simulate the interaction between the tire and the road surface, so as to establish the coupling relationship between the road surface and the tire.

[0010] Optionally, the step of incorporating air as a sound propagation carrier into the computational domain to construct an air domain model specifically includes: setting an acoustic computational domain of a cubic region with the upper surface of the three-dimensional road surface model as the bottom boundary, and setting a cavity structure in the region where the acoustic computational domain overlaps with the three-dimensional tire model; setting the air domain parameters of the acoustic computational domain, and using non-reflective boundary conditions to constrain the outer boundary of the acoustic domain to simulate the sound wave radiation characteristics in an open space, thereby obtaining an air domain model; and setting an acoustic-structure coupling interface between the outer surface of the three-dimensional tire model and the inner surface of the air domain model, and between the upper surface of the three-dimensional road surface model and the bottom surface of the air domain model, through binding constraints, to establish a road-tire-air coupling relationship.

[0011] Optionally, the step of calculating the noise reduction quantification index characterizing the road surface noise reduction capability based on the vertical displacement time history data specifically includes: calculating the root mean square value of acceleration based on the vertical displacement time history data, and correcting the root mean square value of acceleration based on the quantitative relationship between the maximum nominal particle size of the road surface and the noise level to obtain a corrected root mean square value of acceleration, and using the corrected root mean square value of acceleration to quantitatively evaluate the road surface noise reduction capability.

[0012] Optionally, the formula for calculating the root mean square value of acceleration is: In the formula: The root mean square value of acceleration. Based on the vertical displacement of the tire center point, the instantaneous acceleration at the tire center point can be obtained using the center difference method, where T is the sampling period; the formula for calculating the root mean square value of the corrected acceleration is: In the formula: To correct the root mean square value of acceleration, p0 is the reference particle size, and p is the maximum particle size of the road surface type to be corrected. For every 3 mm reduction in particle size, the noise decreases by 2 dB.

[0013] Optionally, the step of extracting the sound pressure signal of the vibration noise and processing it in the frequency domain to obtain the predicted noise level specifically includes: extracting the sound pressure signal and processing it through Hanning window, FFT transform, 1 / 3 octave band and A-weighting to obtain the equivalent A-weighted sound pressure level, and using the equivalent A-weighted sound pressure level as the noise level prediction result.

[0014] On the other hand, the present invention also provides a quantitative assessment and prediction system for road-tire vibration noise, comprising: a surface texture data acquisition module, used to acquire elevation point cloud data of the surface texture of a road test specimen, and preprocess the point cloud data to obtain road surface texture data; a viscoelastic property parameter acquisition module, used to acquire viscoelastic property parameters of the road test specimen under different test temperatures and test frequencies through dynamic modulus tests; a model construction module, used to construct a three-dimensional road surface model based on the road surface texture data and the viscoelastic property parameters, construct a three-dimensional tire model with a smooth tread based on preset tire parameters, and construct an air domain model by incorporating air as a sound propagation carrier into the computational domain to obtain a road-tire-air coupled finite element model; and a noise reduction quantization module, used to set parameters including the application of... The system simulates tire rolling conditions under load, rolling speed, and contact conditions, and performs road-tire dynamics simulation. It extracts the vertical displacement time history data of the tire during rolling and calculates a noise reduction quantification index characterizing the road surface's noise reduction capability based on this data. A noise level prediction module applies the vertical displacement time history data as a forced displacement boundary condition to the three-dimensional tire model, performs tire-aeroacoustic simulation to drive the three-dimensional tire model to generate vibration noise, extracts the sound pressure signal of the vibration noise, and processes it in the frequency domain to obtain the predicted noise level. An output module evaluates the noise reduction performance of road test specimens with different surface textures and viscoelastic properties based on the noise reduction quantification index and the noise level, and outputs the road material composition result with the optimal noise reduction capability.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This application fundamentally eliminates the air pumping effect caused by tire tread grooves by constructing a smooth tire model without tread patterns, thus removing interference from non-vibration noise sources. It accurately recreates the real mechanical interaction between the tire and the road surface using surface texture data and viscoelastic properties, ensuring the reliability of the extracted vibration excitation data. The vertical displacement time history data obtained from coupled simulation is input as a forced boundary condition into the tire-road-air coupled model for acoustic simulation, ensuring that noise simulation is based solely on the pure vibration components of the road surface and tire. This cuts off the transmission path of aerodynamic noise and effectively separates tire vibration noise from air pumping noise, thereby enabling precise quantification of the road surface's influence on vibration noise. This transforms low-noise road surface design from a long-standing reliance on trial and error to a quantifiable, predictable, and optimizable design process, providing key technical support for source control of noise at the material level and fundamentally avoiding the drawbacks of passive measures such as sound barriers, which involve huge investments and only address the symptoms. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the method steps of the present invention.

[0017] Figure 2 This is a schematic diagram of the three-dimensional road surface simulation model of the present invention.

[0018] Figure 3 This is a schematic diagram of the three-dimensional tire simulation model of the present invention.

[0019] Figure 4 This is a schematic diagram of the vertical excitation simulation results of the present invention.

[0020] Figure 5 This is a perspective view of the three-dimensional air domain model of the present invention.

[0021] Figure 6 This is a vibration noise cloud diagram of the present invention.

[0022] Figure 7 This is a schematic diagram of the system structure of the present invention.

[0023] In the diagram: 10 - Surface texture data acquisition module, 20 - Viscoelastic property parameter acquisition module, 30 - Model building module, 40 - Noise reduction and quantization module, 50 - Noise level prediction module, 60 - Output module. Detailed Implementation

[0024] The present invention will now be clearly and completely described in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be used interchangeably where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] Those skilled in the art will understand that, unless explicitly stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in the specification of this application means the presence of features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0027] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0028] It should be understood that the sequence number and size of each step in this embodiment do not imply the order of execution. The execution order of each process is determined by its function and internal logic, and should not constitute any limitation on the implementation process of this application embodiment.

[0029] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0030] Please refer to Figures 1-6This invention discloses a method for quantitative assessment and prediction of road-tire vibration noise, comprising the following steps: acquiring elevation point cloud data of the surface texture of a road test specimen; preprocessing the point cloud data to obtain road surface texture data; obtaining viscoelastic characteristic parameters of the road test specimen under different test temperatures and test frequencies through dynamic modulus tests; constructing a three-dimensional road surface model based on the road surface texture data and the viscoelastic characteristic parameters; constructing a three-dimensional tire model with a smooth tread based on preset tire parameters; incorporating air as the sound propagation carrier into the computational domain to construct an air domain model, thereby obtaining a road-tire-air coupled finite element model; and setting tire parameters including applied load, rolling speed, and contact conditions. The system performs a road-tire dynamics simulation under rolling conditions, extracts the vertical displacement time history data of the tire during the rolling process, and calculates a noise reduction quantification index characterizing the road surface noise reduction capability based on the vertical displacement time history data. The vertical displacement time history data is then applied as a forced displacement boundary condition to the three-dimensional tire model, and a tire-aeroacoustic simulation is performed to drive the three-dimensional tire model to generate vibration noise. The sound pressure signal of the vibration noise is extracted and processed in the frequency domain to obtain the predicted noise level. Based on the noise reduction quantification index and the noise level, the noise reduction performance of road test specimens with different surface textures and viscoelastic properties is evaluated, and the road material composition result with the optimal noise reduction capability is output.

[0031] Specifically, rutted slab specimens, i.e., road test specimens, that meet construction requirements are prepared using the wheel rolling method. A 3D laser scanner is used to acquire surface texture elevation point cloud data of the road test specimens, with an optimal accuracy of 0.05-0.5 mm. During scanning, due to factors such as ambient light source, specimen placement, and instrument positioning, problems such as noise interference, missing data at edges and interior parts, and non-horizontal scanning planes may occur in the data. Therefore, the original data is preprocessed. Furthermore, due to factors such as ambient light source, specimen placement, and instrument positioning, problems such as noise interference, missing data at edges and interior parts, and non-horizontal scanning planes may occur in the data. Therefore, the original data is processed using MATLAB programming.

[0032] To simulate the dynamic response of a real road surface under traffic load, it is necessary to test the viscoelastic properties of the material. Since the Prony series is used to define the material properties of viscoelastic materials in the ABAQUS simulation software, the Prony series of the relaxation modulus is obtained by using the dynamic modulus test results.

[0033] A "smooth tire-road-air" coupling model based on real road surface texture and viscoelastic properties was established. At the same time, the vertical excitation displacement of the tire was used as the boundary constraint condition for tire vibration and noise simulation, separating tire vibration and noise from tire-road coupled noise, realizing tire vibration and noise prediction, and providing a technical means for decoupling analysis of tire-road noise generation mechanism and related influencing factors.

[0034] This value comprehensively reflects the energy and spectral characteristics of the road surface's excitation of tire vibration, and is a direct and reliable indicator for quantifying the road surface's noise reduction capability. The lower the value, the stronger the road surface's ability to suppress tire vibration. Quantitative Indicators of Road Surface Noise Reduction Capability As an intermediate evaluation indicator, it originates from tire-road coupled vibration simulation. Its physical meaning is clear, its computational efficiency is high, and it is directly related to the physical properties of the road surface itself, such as texture and modulus. It is specifically used to guide the rapid design and parameter optimization of noise reduction road surface materials, such as aggregate gradation, asphalt-aggregate ratio, and asphalt type.

[0035] This application fundamentally eliminates the air pumping effect caused by tire tread grooves by constructing a smooth tire model without tread patterns, thus removing interference from non-vibration noise sources. It accurately recreates the real mechanical interaction between the tire and the road surface using surface texture data and viscoelastic properties, ensuring the reliability of the extracted vibration excitation data. The vertical displacement time history data obtained from coupled simulation is input as a forced boundary condition into the tire-road-air coupled model for acoustic simulation, ensuring that noise simulation is based solely on the pure vibration components of the road surface and tire. This cuts off the transmission path of aerodynamic noise and effectively separates tire vibration noise from air pumping noise, thereby enabling precise quantification of the road surface's influence on vibration noise. This transforms low-noise road surface design from a long-standing reliance on trial and error to a quantifiable, predictable, and optimizable design process, providing key technical support for source control of noise at the material level and fundamentally avoiding the drawbacks of passive measures such as sound barriers, which involve huge investments and only address the symptoms.

[0036] In some embodiments, the step of preprocessing the point cloud data specifically includes: considering measurement points in the point cloud data with elevation values ​​less than -10mm and greater than 10mm as invalid points; and repairing the invalid points using a linear interpolation method, the calculation formula of which is: In the formula: The elevation value of the invalid point. The coordinates of the invalid point. These are two valid measurement points before and after the invalid point. The elevations of the two valid measurement points before and after the invalid point are used; the tilt is removed by plane fitting to eliminate systematic errors in the scanning process.

[0037] Specifically, AC-13 type asphalt mixture with a maximum nominal particle size of 13 mm was used. Rutting slab specimens meeting construction requirements were prepared using the wheel rolling method. High-precision laser scanners were used to scan the specimens, obtaining the elevation point cloud matrix data of the rutting slab surface. The gradation is shown in Table 1 below. Table 1: Grading Information Table.

[0038]

[0039] The preprocessing steps for the point cloud data include: Invalid Point Identification: Identifying invalid elevation points is a crucial step in data processing. Due to external interference, equipment errors, and other factors, some data points deviate from the normal measurement range and become invalid. Invalid points need to be corrected. By defining a reasonable invalid point threshold, the program automatically identifies all abnormal elevation points and records their corresponding x-axis coordinates. The x-axis coordinates of invalid points are extracted for subsequent interpolation. The initial setting of this study considers all measurement points with elevation values ​​less than -10mm and greater than 10mm as invalid. However, there are some differences in invalid point identification for different road surface textures. For small-particle-gradation road surfaces, the overall surface elevation is lower, requiring adjustments based on the actual scanned elevation data.

[0040] Extraction of valid data: The remaining measurement points after removing invalid points are considered valid data. Valid data forms the basis of the interpolation algorithm. To ensure the accuracy of the interpolation results, the elevation value of the invalid point is estimated using the two valid data points closest to it. The x-axis coordinates and corresponding elevation values ​​of the valid data points are extracted and used in the subsequent interpolation process.

[0041] Linear interpolation method: Linear interpolation is a commonly used numerical analysis method that assumes the data changes linearly between adjacent points. For each invalid point, its elevation value is calculated by linear interpolation based on the elevation values ​​of its adjacent valid points. The specific calculation is shown in the formula: ; In the formula: The elevation value of the invalid point. The coordinates of the invalid point. These are two valid measurement points before and after the invalid point. The elevations of the two valid measurement points before and after the invalid point are used; the tilt is removed by plane fitting to eliminate systematic errors in the scanning process.

[0042] Invalid point replacement: Invalid points in the original data are replaced by the interpolated result after interpolation, thus obtaining the corrected elevation data. Based on this method, abnormal fluctuations in the original data are eliminated, ensuring the continuity and reliability of the data.

[0043] Tilt Reduction and Zeroing: To correct elevation errors caused by tilt during road surface scanning, a plane fitting-based error correction method is employed. By fitting a plane to the elevation points in the scanned data, an optimal plane is fitted to represent the tilt trend of the entire dataset. The specific method is as follows: First, the x and y coordinates corresponding to the elevation data are extracted to form a grid coordinate system. A plane is fitted using linear regression to calculate the tilt error for each point. Finally, the corrected elevation data is obtained by subtracting the fitted plane value from the actual elevation value, thus eliminating the systematic error caused by tilt. The core idea of ​​this method is to treat road surface tilt as a holistic trend, find a mathematical description of this trend through plane fitting, and use this description to correct the data. The processed elevation data more accurately reflects the original road surface morphology.

[0044] This application ensures the integrity and reliability of road surface texture data through invalid point identification, valid data extraction, linear interpolation repair, and de-tilting and zeroing processing. It eliminates data distortion caused by environmental interference or equipment errors during the scanning process, providing high-quality input for subsequent 3D model construction, thereby improving the accuracy and stability of the entire simulation process and avoiding prediction bias caused by data noise.

[0045] In some embodiments, the step of obtaining the viscoelastic properties of the road test specimen under different test temperatures and test frequencies through dynamic modulus testing specifically includes: obtaining the dynamic modulus and phase angle of the road test specimen under different test temperatures and test frequencies through dynamic modulus testing; calculating the relaxation modulus based on the dynamic modulus and the phase angle; constructing a relaxation modulus master curve based on the relaxation modulus; calculating the shear modulus ratio based on the relaxation modulus master curve; and fitting the Prony series parameters based on the shear modulus ratio, wherein the Prony series parameters include material constants and delay time.

[0046] Specifically, dynamic modulus tests are used to obtain the dynamic modulus of the material at different test temperatures and test frequencies. and phase angle ; Calculate the relaxation modulus: ; ; ; ; ; ; In the formula: To store modulus, For dynamic modulus, The phase angle is k; the slope is k. For shear storage modulus; It is a gamma function; For adjustment functions; This is the relaxation modulus.

[0047] Using 20℃ as the reference temperature, based on the time-temperature equivalence principle and the WLF equation, the displacement factor is calculated using the least squares method, and the master curve of the relaxation modulus Sigmoidal type function is constructed. Calculate the shear modulus ratio: ; ; ; In the formula: Shear modulus; The relaxation modulus; Let be Poisson's ratio, taken as 0.2; The ratio of shear modulus. This is the initial shear modulus.

[0048] Fitting the Prony series: The Prony series is solved using the Marquardt-LM algorithm. ; In the formula: The ratio of shear moduli; gi is a material constant; t is the delay time; n is the time; n is the number of terms in the Prony series.

[0049] The results of the dynamic modulus test are shown in Tables 2 and 3: Table 2: Results of dynamic modulus test.

[0050]

[0051] Table 3: Phase angle test results.

[0052]

[0053] The final fitting results are shown in Table 4: Table 4: Prony parameters.

[0054]

[0055] This application obtains the dynamic modulus and phase angle of the road test specimen under different test temperatures and frequencies through dynamic modulus tests. The relaxation modulus is calculated sequentially, a master relaxation modulus curve is constructed, the shear modulus ratio is calculated, and the Prony series parameters are fitted to accurately characterize the viscoelastic properties of the road material. Simulating the dynamic response of the road surface under real traffic loads makes the model closer to actual working conditions, enhances the realism of tire-road interaction simulation, and provides a foundation of key material parameters for noise reduction performance evaluation.

[0056] In some embodiments, the step of constructing a three-dimensional road surface model based on the road surface texture data and the viscoelastic property parameters specifically includes: generating corresponding node coordinates from the road surface texture data according to spatial location and preset sampling precision, connecting nodes with three adjacent nodes as units to form a three-dimensional road surface mesh; generating a curved surface on the surface of the three-dimensional road surface mesh, and stretching the curved surface along the thickness direction to form a three-dimensional road surface model; and assigning material properties to the three-dimensional road surface model, wherein the material properties include: dynamic modulus, Poisson's ratio, material constant, and delay time.

[0057] Specifically, MATLAB is used to number the elevation data into nodes, and corresponding horizontal and vertical coordinates are generated according to their spatial location and the set sampling accuracy. The nodes are connected to form a three-dimensional road surface mesh: every three adjacent nodes are selected to form a triangular unit. A complete road surface model is divided into a large number of units through MATLAB programming. Different sampling frequencies can be selected to obtain different mesh accuracies.

[0058] Model Import: Using MATLAB programming, a surface is generated from the model's surface data points. A solid volume is formed by extruding a 100mm thickness beneath this surface using C3D8H solid elements, and a corresponding ".inp" file is generated for import into ABAQUS to create the road surface model. The material modulus is then imported into ABAQUS. Poisson's ratio 0.25 and viscoelastic parameter gi, .

[0059] The sampling precision was divided into eight scales ranging from 0.05 mm to 20 mm, and the variation of parameters with scale was analyzed. Based on the variation patterns at different scales, the two-dimensional surface texture parameters can be divided into the following two types: Stable parameters: Within a sampling accuracy of 0.05–2 mm, these parameters remain stable, for example, with a coefficient of variation <5%, indicating that micro-texture features have self-similarity within this scale range. When the sampling accuracy exceeds 2 mm, the parameter values ​​begin to change abruptly; for example, aR increases by 15% within the 2–5 mm scale. However, different road surface types show similar trends, indicating that macro-texture above 2 mm dominates parameter changes. Variational parameters: These parameters always change with the sampling accuracy, decreasing as the sampling accuracy scale increases. A comprehensive comparison of 2D and 3D texture parameters at different sampling accuracies shows that 2 mm is the maximum boundary for most texture parameter indices. Sampling accuracy exceeding 2 mm leads to distortion of texture parameter indices; a 2 mm scale is sufficient to effectively describe the main contours of the surface.

[0060] A 3D road surface model is constructed based on surface texture and Prony series fitting results, as follows: Figure 2 As shown.

[0061] This application constructs a three-dimensional road surface model based on high-precision texture data and viscoelastic parameters, achieving a refined simulation of the micro-texture of the road surface. It accurately reproduces the contact characteristics between the road surface and the tire, overcoming the shortcomings of existing simplified models that ignore the influence of the road surface. This allows for a more effective assessment of the contribution of texture to vibration and noise, supporting targeted optimization in noise reduction design.

[0062] In some embodiments, the step of constructing a three-dimensional tire model based on preset tire parameters specifically includes: generating a three-dimensional tire model with no tread and a smooth tread according to the preset tire parameters, and assigning corresponding material parameters and inflation pressures to the tread, tire body and rim of the three-dimensional tire model respectively; adjusting the spatial position of the three-dimensional tire model to contact the upper surface of the three-dimensional road surface model, and setting the friction coefficient to simulate the interaction between the tire and the road surface, so as to establish the coupling relationship between the road surface and the tire.

[0063] Specifically, to prevent the influence of different tire types on the analysis, the P225 / 60R16 standard reference test tire specified in ASTM F2493-20 was selected. This type of tire is widely used in tire road noise testing. Here, P indicates the tire type is a passenger car tire; 205 indicates the tire's tread width is 205 mm; 60 indicates the tire's aspect ratio, i.e., the ratio of sidewall height to section width is 55%, or 135 mm; R indicates a radial tire; and 16 indicates the inner diameter, i.e., the rim diameter is 16 inches. Tire components include rubber, cord, and fiber reinforcement materials, and the selection of rubber materials varies among these components. The choice of tire materials affects the results of ABAQUS simulation calculations. To ensure the accuracy of the calculation results, the study selects material parameters based on a proven and accurate constitutive model.

[0064] Rubber is the main component of tires and is a typical hyperelastic material with significant large deformation characteristics. Its incompressibility and nonlinear behavior enable it to maintain good recovery ability during use. The Yeoh model was selected as the constitutive model for rubber materials, as it exhibits good stability and accuracy in simulating the hyperelasticity of tire rubber materials. The parameters are shown in Table 5. Table 5: Parameters of the tire constitutive model.

[0065]

[0066] Cord material is a high-performance material with high strength and modulus, while rubber material can undergo large deformations under load and return to its original shape after unloading, but cannot continuously bear loads. Therefore, to reinforce tire rubber material, cord material is embedded into tire rubber to form a cord-rubber composite material. In ABAQUS software, a rebar layer is defined for the cord material. The rebar model mainly consists of rebar elements and solid elements. Rebar elements are used to simulate the cord material, and solid elements are used to simulate the rubber material. Then, the *EMBEDDED ELEMENT keyword is used to embed the cord material rebar elements into the rubber material solid elements. The parameters of the rubber material and the rubber-cord composite material are selected based on a proven accurate constitutive model. The parameters of the cord composite material are shown in Table 6. Table 6: Parameters of rubber-cord composite materials.

[0067]

[0068] Tire model creation, such as Figure 3 As shown: To avoid air pumping noise caused by the interaction between the tire tread and the road surface, a smooth tread model was established. In ABAQUS software, a tread sketch was drawn based on the selected tire size. The sketch was then rotated 12° around the center of mass to obtain a 1 / 30 smooth tread model. This was further rotated by a certain fraction around the center of mass to obtain the complete tread model. The tread and other components were modeled and assigned corresponding material properties. A hexahedral element C3D8H mesh was then created to ensure convergence and accuracy of subsequent calculations. The rim was defined as a rigid body that does not deform to constrain the tire. An SFM3D4R quadrilateral element mesh was created for the reinforcing ribs. Finally, a complete 3D model of each component was obtained. Inflation simulation was performed on the complete 3D tire model. By defining boundary conditions to prevent displacement of the tire's inner surface, and applying a pressure of 0.25 MPa to the tire's inner surface, the inflation pressure was simulated, ultimately achieving a 3D simulation of the tire's inflation forces.

[0069] A three-dimensional road surface component array is formed into a road surface strip, sufficient for the tire to roll one revolution. A completely fixed constraint is applied to the road surface model to ensure that this area does not experience any displacement or deformation in the three orthogonal directions (x, y, z axes). The aforementioned three-dimensional tire model is then imported into the assembly and its spatial position is adjusted so that the tire is positioned above the road surface model.

[0070] Due to the irregularity of the road surface texture, the tire and the road surface cannot be directly set into spatial contact within the assembly. Therefore, the tire is raised and then lowered to achieve initial contact with the road surface; the downward constraint of the tire is then released, allowing the tire to bounce back on the road surface while still maintaining contact. In the parameter setting of the contact surface, a contact method with a fixed friction coefficient is used to simulate the contact behavior. In dynamic simulation, an extremely high friction coefficient can sometimes introduce difficulties in numerical calculation (such as iteration non-convergence and violent oscillation of contact force). Setting the friction coefficient to 0.5 provides good numerical stability, reduces the risk of calculation failure, and ensures computational efficiency.

[0071] Using a quarter of the vehicle weight as a reference, a vertical normal force of 3.5 kN is applied to the tire. Different tire rolling speeds are set using boundary conditions in the Load module, with an optimal rolling speed of 60 km / h to 80 km / h, simulating tire rolling on the road surface. In the rolling analysis step, a test time is set to ensure the tire completes one revolution, and the vertical displacement data of the tire's center point is extracted.

[0072] This application generates a smooth tire model without tread patterns and assigns material parameters, thereby eliminating the interference of air pumping noise caused by tire tread patterns. It focuses on the separation of tire vibration noise, making the evaluation results more focused on the noise reduction capability of the road surface itself, and improving the specificity and reliability of the road surface noise reduction capability evaluation.

[0073] In some embodiments, the step of incorporating air as a sound propagation carrier into the computational domain to construct an air domain model specifically includes: setting an acoustic computational domain of a cubic region with the upper surface of the three-dimensional road surface model as the bottom boundary, and setting a cavity structure in the region where the acoustic computational domain overlaps with the three-dimensional tire model; setting the air domain parameters of the acoustic computational domain, and using non-reflective boundary conditions to constrain the outer boundary of the acoustic domain to simulate the sound wave radiation characteristics in an open space, thereby obtaining an air domain model; and setting an acoustic-structure coupling interface between the outer surface of the three-dimensional tire model and the inner surface of the air domain model, and between the upper surface of the three-dimensional road surface model and the bottom surface of the air domain model, through binding constraints, to establish a road-tire-air coupling relationship.

[0074] Specifically, considering the infinite nature of the air medium, a non-reflective boundary condition is used to constrain the outer boundary of the acoustic domain, effectively simulating the sound wave radiation characteristics in open space. The geometric parameters of the acoustic computation domain are set as a 1000mm×1000mm×800mm cube region, with its bottom surface maintaining the same contact surface as the road surface layer, achieving energy transfer through the acoustic-structure coupling interface. To accommodate the spatial arrangement requirements of the tire model, a cavity structure matching the tire geometry is pre-set within the air medium domain. Regarding the selection of mesh element type, given that noise transmission characteristics are only supported by the acoustic module, the four-node acoustic linear tetrahedral element AC3D4 is used. For the mesh generation of the air domain, its Approximate globalsize parameter is set to 0.05. The modulus and density of the acoustic medium are shown in Table 7. Table 7: Material parameters for the air model.

[0075]

[0076] This application simulates sound wave propagation in open space by defining an acoustic computational domain, non-reflective boundary conditions, and an acoustic-structure coupling interface. It can reproduce the radiation process of noise in the air, ensuring that the prediction results are consistent with the actual acoustic environment and improving the accuracy of noise level prediction.

[0077] In some embodiments, the steps of setting tire rolling conditions including applied load, rolling speed, and contact conditions and performing road-tire dynamics simulation specifically include: During tire rolling, the tire's motion exhibits a combination of translational and rotational characteristics. Its relative motion with the air distorts the surrounding flow field, creating differences in sound pressure distribution along the front and rear directions. Considering that the noise from air disturbances caused by tire rotation accounts for a relatively small proportion of the total noise, the model's operating conditions are simplified to balance computational accuracy and efficiency. The spatial relative positions of the tire, air, and road surface are fixed, and only the tire's rotational angular velocity is applied to simulate in-place rolling. Vertical forced displacement simulates tire vibration caused by road surface excitation. Binding constraints are used to bind the outer surface of the tire to the inner surface of the air, and the upper surface of the road surface to the bottom surface of the air, thereby ensuring the mechanical continuity of the air and the tire-air-road interface.

[0078] Using the built-in Amplitude curve function of the finite element method software, controllable vertical forced displacement boundary conditions can be applied to the tire model. The displacement boundary conditions of this model are controlled by the vertical excitation displacement-time curve obtained from the three-dimensional vertical excitation condition. At the same time, an angular velocity is applied to the tire.

[0079] The aforementioned air, tire, and road surface components are imported into the assembly system. The positions of each component are adjusted, and they are assembled according to their coordinate positions to achieve spatial contact. The tire and air, and the road surface and air are defined by binding constraints *tie. The vibration of the tire is transmitted to the air, causing the air to vibrate, which in turn generates noise. The air pumping noise originates from the air compression-release effect between the tire tread grooves and the road surface. This system uses a smooth tire model with no grooves, fundamentally eliminating the air pumping mechanism. At the same time, the system uses the vertical displacement u(t) obtained from coupled rolling simulation as a forced boundary condition. This displacement only includes the vibration component generated by the tire-road surface contact.

[0080] In noise simulation, solving for the contact between the tire and the road surface texture and the transmission of vibration to the air domain is too complex and difficult to converge. In the vertical excitation case, since vertical displacement excitation based on the road surface texture has already been obtained, the road surface is simplified to a thin panel of 1000mm × 1000mm × 40mm, and the viscoelastic material parameters of the road surface are designed according to the Prony series. C3D8R elements are used for mesh generation.

[0081] Considering that the boundary of the air domain within the model is finite, while air in reality extends infinitely, to ensure that the boundary conditions effectively simulate the absorption effect in the actual environment, nonreflecting contact definitions need to be applied to the perimeter and top of the air domain in the model. Tire vibration noise needs to propagate through the air, and the vibration of the tire structure to the sound wave exhibits a certain acoustic impedance effect. Within the framework of acoustic theory, acoustic impedance is defined as the complex ratio between the sound pressure generated at a point in the medium due to mechanical disturbance and the velocity amplitude of the particle, reflecting the damping characteristics of the medium on the motion of the particle at that point. The complex form of acoustic impedance consists of both a real part and an imaginary part, where acoustic resistance and sonar correspond to the physical meanings of the real and imaginary parts, respectively. Based on the above theory, when setting the contact on the inner surface of the air, the real part of the acoustic impedance value is set to 1.28E-8, and the imaginary part is set to 2E-5i.

[0082] In the noise model, an angular velocity of 101.10 rad / s is applied to the tires to simulate a vehicle speed of 80 km / h.

[0083] Apply to tires Figure 4 The vertical excitation displacement is 80 km / h, and the noise cloud diagram of the model is shown in Figure 6.

[0084] In some embodiments, the step of calculating the noise reduction quantification index characterizing the road surface noise reduction capability based on the vertical displacement time history data specifically includes: calculating the root mean square value of acceleration based on the vertical displacement time history data, and correcting the root mean square value of acceleration based on the quantitative relationship between the maximum nominal particle size of the road surface and the noise level to obtain a corrected root mean square value of acceleration, and using the corrected root mean square value of acceleration to quantitatively evaluate the road surface noise reduction capability.

[0085] Specifically, the vibration noise generated by tire-road interaction is essentially sound radiation formed by the transfer of mechanical vibration energy from the tire to the air medium. To quantify the road surface's ability to suppress tire vibration noise, this invention proposes to modify the root mean square value of acceleration. As a core evaluation indicator.

[0086] When a tire interacts with the road surface, the force exerted by the road surface on the tire causes its surface to vibrate, thus generating vibration noise. Essentially, the road surface texture excites the tire to produce forced vibration, and the vibration of the tire structure generates sound waves. The tire is subjected to a random excitation force F(t) as it rolls on the road surface, and its equation of motion is: ; In the formula: m is the tire mass; c is the tire damping coefficient; k is the tire stiffness; u(t) is the vertical displacement of the tire center point.

[0087] Numerical solution of acceleration using the central difference method: ; Vibrational energy density is proportional to the square of the acceleration: ; Therefore, by characterizing the force exerted by the road surface on the tires through energy, the noise reduction capability of the road surface can be quantified. (Root mean square value of vibration acceleration) This reflects the average energy density of vibration acceleration in the time domain. By processing the root mean square value, random fluctuations can be effectively eliminated, highlighting the average effect of vibration energy and improving the reliability of the evaluation results. The calculation formula is shown below: ; In the formula: The root mean square value of acceleration. Based on the vertical displacement of the tire center point, the instantaneous acceleration of the tire center point can be obtained by the center difference method, where T is the sampling period.

[0088] This reflects the average energy density of vibrational acceleration in the time domain, primarily characterizing the energy aspect and not considering the spectral characteristics of the vibration. However, the vibrational frequency generally shows a trend of gradually increasing with decreasing maximum particle size; therefore, it needs to be corrected based on the maximum particle size. In fact, linking it to both energy and spectrum aspects better aligns with the acoustic mechanism of noise. Using a maximum particle size of 13mm as a baseline, the root mean square of acceleration was corrected. The calculation formula is as follows: ; In the formula: To correct the root mean square value of acceleration, p0 is the reference particle size, and p is the maximum particle size of the road surface type to be corrected. For every 3 mm reduction in particle size, the noise decreases by 2 dB.

[0089] According to acoustic theory, the acoustic radiation power of a vibrating surface is: ; In the formula: c is the air density; c is the speed of sound; S is the radiating surface area. For radiation efficiency; The root mean square of the surface.

[0090] because And acceleration There is a relationship in the frequency domain: ; Therefore, the vibration acceleration spectrum directly determines the radiated acoustic power.

[0091] This application transforms tire vibration energy into a quantifiable index by calculating the root mean square value of acceleration. It provides a direct and efficient evaluation standard for road surface noise reduction capability, simplifies the analysis of complex vibration data, facilitates rapid comparison of the performance of different road surface designs, and supports decision optimization in engineering applications. Furthermore, it introduces a maximum particle size correction factor, enabling the quantifiable index to simultaneously reflect vibration energy and spectral characteristics. This overcomes the limitations of a single energy index, more comprehensively characterizes the correlation between road surface texture and noise, and enhances the scientific rigor and adaptability of the evaluation index, making it particularly suitable for comparing road surfaces with different gradations.

[0092] In some embodiments, the formula for calculating the root mean square value of acceleration is: In the formula: The root mean square value of acceleration. Based on the vertical displacement of the tire center point, the instantaneous acceleration at the tire center point can be obtained using the center difference method, where T is the sampling period; the formula for calculating the root mean square value of the corrected acceleration is: In the formula: To correct the root mean square value of acceleration, p0 is the reference particle size, and p is the maximum particle size of the road surface type to be corrected. For every 3 mm reduction in particle size, the noise decreases by 2 dB.

[0093] In some embodiments, the step of extracting the sound pressure signal of the vibration noise and processing it in the frequency domain to obtain the predicted noise level specifically includes: extracting the sound pressure signal and processing it through Hanning window, FFT transform, 1 / 3 octave band and A-weighting to obtain the equivalent A-weighted sound pressure level, and using the equivalent A-weighted sound pressure level as the noise level prediction result.

[0094] Specifically, the sound pressure level obtained from the simulation analysis is processed using the Hanning window, then converted to the frequency domain using the FFT discrete Fourier transform, and then the frequency band energy is calculated to convert it to a 1 / 3 octave band. A-weighting is then applied, and the equivalent A-weighted sound pressure level is calculated using the following formula: ; ; ; In the formula: The sound pressure level is A-weighted. The sound pressure level is 1 / 3 octave band. This is the A-weighted correction value for the kth 1 / 3 octave band; The total number of 1 / 3 octaves; The equivalent A-weighted sound pressure level; It is the center frequency of the kth 1 / 3 octave band; The A-weighted sound pressure level for the i-th time sample; This represents the total number of time samples.

[0095] This study verifies the quantitative relationship between the maximum nominal particle size of road mixtures and the sound pressure level of tire vibration noise, providing a basis for the application of the method described in this invention in the design of low-noise road materials.

[0096] Five typical asphalt mixture pavements (GT-8, GT-10, SMA-13, and SMA-16) with maximum nominal particle sizes of 8mm, 10mm, 13mm, and 16mm were selected as research objects. Following the method described in this invention, a high-precision laser scanner was first used to acquire surface elevation data of each rutted pavement specimen with a sampling accuracy of 0.05mm, followed by preprocessing steps such as tilt removal and invalid point interpolation. Subsequently, based on the processed three-dimensional surface texture data, a three-dimensional pavement shell model with a sampling accuracy of 2mm was constructed in finite element software. The tire model uniformly adopted the simplified geometry and Yeoh hyperelastic constitutive model of the P225 / 60R16 standard tire from Example 1. In the tire / pavement coupled rolling simulation of all models, the load was uniformly set to 3.5kN and the rolling speed to 80km / h to simulate constant driving conditions.

[0097] After the simulation, the vertical displacement time history curve of the tire center point was extracted, and its root mean square acceleration value was calculated. Further calculations were performed to obtain the corrected root mean square acceleration. Finally, the extracted vertical displacement excitation was used as input to run the tire-road-air coupled acoustic finite element model, acquiring sound pressure data at a distance of 1m from the sound source. After Hanning windowing, FFT transformation, 1 / 3 octave band analysis, and A-weighting, the equivalent continuous A-weighted sound pressure level LAeq was obtained.

[0098] Simulation results show that the noise levels of the five road surfaces are significantly positively correlated with their maximum particle size. Specific data are shown in Table 8 below: Table 8: Correlation data between noise levels and maximum particle size of five types of road surfaces.

[0099]

[0100] Data shows that as the maximum nominal particle size decreases, the sound pressure level of tire vibration noise generally exhibits a downward trend. Calculations indicate that for every 3 mm reduction in the maximum particle size, the noise level can be reduced by approximately 2 dB(A). This result clearly quantifies the impact of the maximum particle size on noise, fully demonstrating that the method described in this invention can effectively assess the contribution of key design parameters of pavement materials to noise. This provides a reliable numerical simulation method and theoretical basis for optimizing and guiding the material design of low-noise pavements by adjusting gradation design, particularly controlling the maximum particle size.

[0101] This application uses Hanning windowing, FFT transform, octave banding, and A-weighting to convert sound pressure signals into noise levels that conform to human hearing. It provides intuitive and practical noise prediction outputs, making the results easy to understand and aligned with actual environmental standards, thus enhancing the method's engineering application value.

[0102] Please refer to Figure 7On the other hand, the present invention also provides a quantitative assessment and prediction system for road surface-tire vibration noise, comprising: a surface texture data acquisition module, used to acquire elevation point cloud data of the surface texture of a road test specimen, and preprocess the point cloud data to obtain road surface texture data; a viscoelastic characteristic parameter acquisition module, used to acquire viscoelastic characteristic parameters of the road test specimen under different test temperatures and test frequencies through dynamic modulus tests; a model construction module, used to construct a three-dimensional road surface model based on the road surface texture data and the viscoelastic characteristic parameters, construct a three-dimensional tire model with a smooth tread based on preset tire parameters, and construct an air domain model by incorporating air as a sound propagation carrier into the computational domain to obtain a road-tire-air coupled finite element model; and a noise reduction quantization module, used to set parameters including... The system simulates tire rolling conditions under applied load, rolling speed, and contact conditions, and performs road-tire dynamics simulation. It extracts the vertical displacement time-history data of the tire during rolling and calculates a noise reduction quantification index characterizing the road surface's noise reduction capability based on this data. A noise level prediction module applies the vertical displacement time-history data as a forced displacement boundary condition to the three-dimensional tire model, performs tire-aeroacoustic simulation to drive the three-dimensional tire model to generate vibration noise, extracts the sound pressure signal of the vibration noise, and processes it in the frequency domain to obtain the predicted noise level. An output module evaluates the noise reduction performance of road test specimens with different surface textures and viscoelastic properties based on the noise reduction quantification index and the noise level, and outputs the road material composition result with the optimal noise reduction capability.

[0103] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0104] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, database, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0105] The above are merely embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention's specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for quantitative assessment and prediction of road-tire vibration noise, characterized by the following steps: include: Obtain elevation point cloud data of road test specimen surface texture, and preprocess the point cloud data to obtain road surface texture data; The viscoelastic properties of the road test specimen under different test temperatures and test frequencies were obtained through dynamic modulus testing. A three-dimensional road surface model is constructed based on the road surface texture data and the viscoelastic property parameters. A three-dimensional tire model with a smooth tread is constructed based on the preset tire parameters. An air domain model is constructed by incorporating air as a sound propagation carrier into the computational domain, resulting in a road-tire-air coupled finite element model. The tire rolling conditions, including applied load, rolling speed and contact conditions, are set and road-tire dynamics simulation is performed. The vertical displacement time history data of the tire during the rolling process is extracted, and the noise reduction quantification index characterizing the road noise reduction capability is calculated based on the vertical displacement time history data. The vertical displacement time history data is applied as a forced displacement boundary condition to the three-dimensional tire model, and tire-air acoustic simulation is performed to drive the three-dimensional tire model to generate vibration noise. The sound pressure signal of the vibration noise is extracted and processed in the frequency domain to obtain the predicted noise level. The noise reduction performance of road test specimens with different surface textures and viscoelastic properties is evaluated based on the noise reduction quantification index and the noise level, and the road material composition with the best noise reduction capability is output.

2. The method for quantitative assessment and prediction of road-tire vibration noise according to claim 1, characterized in that, The preprocessing steps for the point cloud data specifically include: Measurement points in the point cloud data with elevation values ​​less than -10mm and greater than 10mm are considered invalid points; The invalid points are repaired using linear interpolation, and the calculation formula is as follows: ； In the formula: The elevation value of the invalid point. The coordinates of the invalid point. These are two valid measurement points before and after the invalid point. The elevations of the two valid measurement points before and after the invalid point; Tilt removal is performed using a plane fitting method to eliminate systematic errors during the scanning process.

3. The method for quantitative assessment and prediction of road-tire vibration noise according to claim 1, characterized in that, The step of obtaining the viscoelastic property parameters of the road test specimen under different test temperatures and test frequencies through dynamic modulus testing specifically includes: The dynamic modulus and phase angle of the road test specimen under different test temperatures and test frequencies were obtained through dynamic modulus testing. The relaxation modulus is calculated based on the dynamic modulus and the phase angle, and the master curve of the relaxation modulus is constructed based on the relaxation modulus. Calculate the shear modulus ratio based on the relaxation modulus master curve; The Prony series parameters are obtained by fitting the shear modulus ratio, and the Prony series parameters include material constants and delay time.

4. The method for quantitative assessment and prediction of road-tire vibration noise according to claim 3, characterized in that, The steps for constructing a three-dimensional road surface model based on the road surface texture data and the viscoelastic property parameters specifically include: The road surface texture data is used to generate corresponding node coordinates according to spatial location and preset sampling accuracy, and three adjacent nodes are connected to form a three-dimensional road surface mesh. A curved surface is generated on the surface of the three-dimensional road surface mesh, and the curved surface is stretched along the thickness direction to form a three-dimensional road surface model; The three-dimensional road surface model is assigned material properties, including: dynamic modulus, Poisson's ratio, material constant, and delay time.

5. The method for quantitative assessment and prediction of road-tire vibration noise according to claim 1, characterized in that, The steps for constructing a three-dimensional tire model based on preset tire parameters specifically include: A three-dimensional tire model with no tread and a smooth surface is generated based on preset tire parameters, and corresponding material parameters and inflation pressures are assigned to the tread, tire body and rim of the three-dimensional tire model respectively. The spatial position of the three-dimensional tire model is adjusted to make it contact the upper surface of the three-dimensional road surface model, and the friction coefficient is set to simulate the interaction between the tire and the road surface, so as to establish the coupling relationship between the road surface and the tire.

6. The method for quantitative assessment and prediction of road-tire vibration noise according to claim 1, characterized in that, The steps of incorporating air as a sound propagation medium into the computational domain to construct an air domain model specifically include: An acoustic computation domain for a cubic region is defined with the upper surface of the three-dimensional road surface model as the bottom boundary, and a cavity structure is defined in the region where the acoustic computation domain overlaps with the three-dimensional tire model. The air domain parameters of the acoustic computational domain are set, and the outer boundary of the acoustic domain is constrained by the non-reflective boundary condition to simulate the sound wave radiation characteristics in open space, thus obtaining the air domain model. Between the outer surface of the three-dimensional tire model and the inner surface of the air domain model, and between the upper surface of the three-dimensional road surface model and the bottom surface of the air domain model, an acoustic-solid coupling interface is set by binding constraints to establish a coupling relationship between the road surface, tire, and air.

7. The method for quantitative assessment and prediction of road-tire vibration noise according to claim 1, characterized in that, The steps for calculating the noise reduction quantification index characterizing the road surface noise reduction capability based on the vertical displacement time history data specifically include: The root mean square value of acceleration is calculated based on the vertical displacement time history data, and the root mean square value of acceleration is corrected based on the quantitative relationship between the maximum nominal particle size of the road surface and the noise level to obtain the corrected root mean square value of acceleration. The noise reduction capability of the road surface is quantitatively evaluated through the corrected root mean square value of acceleration.

8. The method for quantitative assessment and prediction of road-tire vibration noise according to claim 7, characterized in that, The formula for calculating the root mean square value of acceleration is: ； In the formula: The root mean square value of acceleration. Based on the vertical displacement of the tire center point, the instantaneous acceleration of the tire center point can be obtained by the center difference method, where T is the sampling period; The formula for calculating the root mean square value of the corrected acceleration is: ； In the formula: To correct the root mean square value of acceleration, p0 is the reference particle size, and p is the maximum particle size of the road surface type to be corrected. For every 3 mm reduction in particle size, the noise decreases by 2 dB.

9. The method for quantitative assessment and prediction of road-tire vibration noise according to claim 1, characterized in that, The step of extracting the sound pressure signal of the vibration noise and processing it in the frequency domain to obtain the predicted noise level specifically includes: The sound pressure signal is extracted and processed by Hanning window, FFT transform, 1 / 3 octave band and A weighting to obtain the equivalent A weighted sound pressure level. The equivalent A weighted sound pressure level is used as the noise level prediction result.

10. A quantitative assessment and prediction system for road-tire vibration noise, characterized in that, include: The surface texture data acquisition module is used to acquire the elevation point cloud data of the surface texture of the road test specimen, and to preprocess the point cloud data to obtain the road surface texture data. The viscoelastic property parameter acquisition module is used to obtain the viscoelastic property parameters of the road test specimen under different test temperatures and test frequencies through dynamic modulus testing. The model building module is used to build a three-dimensional road surface model based on the road surface texture data and the viscoelastic property parameters, build a three-dimensional tire model with a smooth tread based on preset tire parameters, and incorporate the air medium as a sound propagation carrier into the computational domain to build an air domain model, thereby obtaining a road-tire-air coupled finite element model. The noise reduction quantization module is used to set the tire rolling conditions including applied load, rolling speed and contact conditions and perform road-tire dynamics simulation, extract the vertical displacement time history data of the tire during the rolling process, and calculate the noise reduction quantization index characterizing the road noise reduction capability based on the vertical displacement time history data. The noise level prediction module is used to apply the vertical displacement time history data as a forced displacement boundary condition to the three-dimensional tire model, perform tire-air acoustic simulation to drive the three-dimensional tire model to generate vibration noise, extract the sound pressure signal of the vibration noise and process it in the frequency domain to obtain the predicted noise level. The output module is used to evaluate the noise reduction performance of road test specimens with different surface textures and viscoelastic properties based on the noise reduction quantification index and the noise level, and output the road material composition result with the best noise reduction capability.

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