Method for estimating limit water skiing speed of vehicle tread wear on wet and slippery road surface

Abstract

The invention discloses a method for estimating the limit water sliding speed of vehicle tread abrasion on a wet and slippery road surface, and relates to the technical field of vehicle engineering, and the method comprises the following steps: building a three-dimensional finite element model of a tire, a water layer and the road surface by using ABAQUS simulation software, selecting a fluid motion model, and building a fluid-solid coupling model for simulating the water sliding state of the tire; according to the established finite element model; according to the method, a tire finite element model and a fluid-solid coupling model among a tire, a water layer and a road surface are established, and the influence of tire inflation pressure, water layer thickness, tire vertical load and tread rubber on the vehicle limit water skiing speed under different vehicle tire tread wear is simulated and analyzed by using a finite element method; in combination with the relationship among the tire inflation pressure, the tire vertical load, the tread rubber, the water layer thickness and the limit water skiing speed, the BP neural network is utilized to predict the limit water skiing speed, and compared with an NASA water skiing equation, the precision is greatly improved.

Description

Technical Field

[0001] The invention relates to the technical field of vehicle engineering, and in particular to a method for estimating the limit hydroplaning speed of a vehicle tread wear on a slippery road surface. Background Art

[0002] Hydroplaning refers to the loss of tire grip on slippery roads. When the reduction in tire tread groove depth leads to insufficient drainage capacity and the water film pressure is greater than the contact pressure between the tire and the road, the tire will float on the water film, seriously affecting the vehicle's braking, power transmission and steering capabilities. Therefore, real-time and accurate prediction of the hydroplaning limit speed of vehicles with worn tire treads is of great significance for improving driving safety and reducing traffic accidents. Drivers can adjust their driving strategies based on the predicted values ​​to avoid vehicle loss of control in the hydroplaning state.

[0003] Domestic and foreign scholars have conducted a lot of theoretical and simulation analysis on the hydroplaning characteristics of tires. Studies have shown that factors such as tire pattern form, groove depth, tire pressure, load, vehicle speed, road roughness and water film thickness all have an impact on the hydroplaning characteristics of tires. WB Horne and other researchers from NASA constructed the NASA hydroplaning equation based on a large amount of hydroplaning test data; AWGilbert and others compared hydroplaning test data and found that the tread pattern depth, road roughness and tire inflation pressure were positively correlated with tire hydroplaning performance; B. Wies and others explored the influence of pattern structure, pattern arrangement and pattern type on tire hydroplaning performance through experiments. The above research on the vehicle's limit hydroplaning speed is based on theoretical experience and field measurements. The results are not accurate enough, and there are a lot of gaps in the real-time estimation of the vehicle's limit hydroplaning speed. Summary of the invention

[0004] The object of the present invention is to provide a method for estimating the limit hydroplaning speed of vehicle tread wear on a wet road surface, so as to solve the problems existing in the prior art mentioned in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for estimating the limit hydroplaning speed of a vehicle tread wear on a slippery road surface comprises the following steps:

[0007] S1: Use ABAQUS simulation software to establish a three-dimensional finite element model of the tire, water layer and road surface, select a fluid motion model, and establish a fluid-solid coupling model to simulate the hydroplaning state of the tire;

[0008] S2: Based on the established finite element model, the vertical stress distribution curve of the tire center under different working conditions is obtained by setting different tread wear rates, tire inflation pressures, tire vertical loads, and tread rubber materials. The vertical stress distribution of the tire center corresponds to the "three areas" of tire hydroplaning. The hydroplaning water flow traces of tires with different tread wear at different speeds are compared and verified with the Dunlap model.

[0009] S3: The influence of tire inflation pressure, water layer thickness, tire vertical load, different tread wear rates and different tread rubber materials on the vehicle's maximum hydroplaning speed is analyzed based on the finite element method, and the maximum hydroplaning speed is predicted using the BP neural network.

[0010] Preferably, the specific steps of constructing the three-dimensional finite element model of the tire, the water layer and the road surface and the fluid-solid coupling model for simulating the hydroplaning state of the tire in S1 are:

[0011] S11: Draw a two-dimensional cross-sectional view of the tire using the two-dimensional drawing software CAD, then perform meshing using the pre-processing software HyperMesh, and finally import the processed two-dimensional model into ABAQUS and rotate it to generate a three-dimensional tire model;

[0012] S12: Use ABAQUS to establish a finite element model of the water layer and perform meshing using HyperMesh software;

[0013] S13: The tire and the water layer are coupled to obtain a fluid-solid coupling model, and the fluid-solid coupling model is verified.

[0014] Preferably, the specific steps of S2 are:

[0015] S21: According to the established fluid-solid coupling model, simulation is performed under different tread wear rates, different water layer thicknesses, different tire pressures, vertical loads and tread rubber materials;

[0016] S22: Obtaining the “three areas” of tire hydroplaning, namely, the water flow surrounding area, the water film area and the contact area;

[0017] S23: Obtaining a hydroplaning water flow cloud map of tires with different tread wear and different speeds;

[0018] S24: The Dunlap worn tire critical hydroplaning speed empirical model is used to compare the critical speeds and verify its effectiveness.

[0019] Preferably, the specific steps of S3 are:

[0020] S31: The number of neurons in the input layer is 5, which are respectively inflation pressure, water layer thickness, vertical load, tread wear rate and tread rubber. The number of neurons in the output layer is 1, which is the limit hydroplaning speed. The number of hidden layers can be divided into three categories: no hidden layer, single hidden layer, and multiple hidden layers. The neural network without hidden layer can only represent linearly separable functions or decisions.

[0021] S32: Determine the number range by using an empirical formula for determining the number of neurons in the hidden layer;

[0022] S33: Use the 'itertools.product' function in Python to generate all possible combinations, and then use the 'random.sample' function to randomly select samples from all combinations. Input the tire inflation pressure, water layer thickness, tire vertical load, tread wear rate and tread rubber value corresponding to each group of samples into the "fluid motion model" for simulation calculation;

[0023] S34: The entire data set is divided into a training set, a validation set, and a test set. In order to obtain more test samples, the validation set accounts for 10%, the test set accounts for 20%, and the training set accounts for 70%;

[0024] S35: Obtaining the limit hydroplaning speed of the vehicle tread wear on a wet road surface.

[0025] Preferably, the specific steps of verifying the fluid-solid coupling model in S13 are:

[0026] The critical hydroplaning speed v given by NASA is used. h Compare with the critical hydroplaning speed obtained by simulation for verification:

[0027]

[0028] Where P is the tire inflation pressure.

[0029] Preferably, the critical hydroplaning speed empirical model of Dunlap worn tire in S24 is:

[0030]

[0031] Where W is the tread width, d t is the groove depth, d w is the rainwater depth.

[0032] Preferably, the empirical formula for the number of neurons in the hidden layer in S32 is:

[0033]

[0034] Where node is the number of neurons in the hidden layer, i is the number of neurons in the input layer, e is the number of neurons in the output layer, and ζ is a regulation constant between 1 and 10.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. The present invention establishes a tire finite element model and a fluid-solid coupling model among the tire, the water layer and the road surface, and uses the finite element method to simulate and analyze the influence of tire inflation pressure, water layer thickness, tire vertical load and tread rubber on the vehicle's limit hydroplaning speed under different tire tread wear. Based on the simulation analysis data and in combination with the relationship between tire inflation pressure, tire vertical load, tread rubber, water layer thickness and limit hydroplaning speed, the BP neural network is used to predict the limit hydroplaning speed, which has a huge improvement in accuracy compared to the NASA hydroplaning equation.

[0037] 2. The present invention refines the mesh of the tire finite element model according to different regions and optimizes the model, thereby improving the simulation operation efficiency while ensuring the accuracy of the model.

[0038] 3. The present invention adopts a "fluid motion model", which greatly reduces the number of grids and improves the solution efficiency compared to the traditional "tire rolling model". BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a diagram of the tire-water layer-road surface fluid-solid coupling model.

[0040] Figure 2 Schematic diagram of the tire hydroplaning principle.

[0041] Figure 3 Cloud diagrams of hydroplaning water flow traces of tires with 0% tread wear at different speeds: (a) 20 km / h, (b) 60 km / h, (c) 90 km / h, and (d) 107.24 km / h.

[0042] Figure 4 This is a cloud diagram of the water flow trace of the tire hydroplaning at different speeds when the tire tread is worn to 25%.

[0043] Figure 5 Cloud diagram of the hydroplaning water flow trace when the tire tread is worn by 50% at different speeds.

[0044] Figure 6 This is a cloud diagram of the water flow trace of the tire hydroplaning at different speeds when the tire tread is worn to 75%.

[0045] Figure 7 This is a cloud diagram of the hydroplaning water flow trace of a tire at different speeds when the tire tread is 100% worn.

[0046] Figure 8 The graph of the change in contact force between the tire and the ground when the tread is worn 25% at different water film thicknesses.

[0047] Fig. 9 The following is a graph showing the change in contact force between a tire and the ground when the tire tread is worn by 50% with different tire pressures and patterns.

[0048] Fig.10 The graph of the change in contact force between the tire and the ground when the tread is 0% worn under different loads.

[0049] Fig.11 This is the change curve of tire tread wear 25% and ground contact force when the water depth is 15mm.

[0050] Fig.12 This is the neural network model diagram of the limit water skiing speed estimation algorithm.

[0051] Fig.13 This is the prediction effect diagram of the BP neural network on the extreme water skiing speed.

[0052] Fig.14 is the prediction error percentage of the ultimate hydroplaning speed.

[0053] Fig.15 This is a comparison chart of the prediction effects of BP neural network and NASA hydroplaning equation.

[0054] Fig.16 is the prediction error percentage between the neural network and the NASA hydroplaning equation. DETAILED DESCRIPTION

[0055] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0056] See also Figure 1-16 , the present invention provides the following technical solutions:

[0057] The present invention takes the 205 / 55R16 complex pattern tire as the research object and establishes a tire finite element model. Then, a fluid-solid coupling model between tire, water layer and road surface is established, and the complex pattern is jointly simulated using SOLIDWORKS-HYPERMESH-ABAQUS alone, and the effectiveness of the fluid-solid coupling model is verified by using the critical hydroplaning speed. Then, the critical hydroplaning speed is compared using the Dunlap wear tire critical hydroplaning speed empirical model to verify the effectiveness of the critical hydroplaning speed. Then, the contact force change curves between the tire with 25% pattern wear and the ground at different water film thicknesses, the contact force change curves between the tire with 50% pattern wear at different tire pressures and the ground, the contact force change curves between the tire with 0% pattern wear and the ground at different loads, the contact force change curves between the tire with different tread pattern wear and the ground, and the contact force change curves between the tire with 25% pattern wear and the ground when the water depth is 15 mm are fitted. Then, the nonlinear fitting regression between the various parameters is performed through the BP neural network, and finally the BP neural network fitting is used for estimation.

[0058] A method for estimating the limit hydroplaning speed of a vehicle tread wear on a slippery road surface comprises the following steps:

[0059] S1: Use ABAQUS simulation software to establish a three-dimensional finite element model of the tire, water layer and road surface, select a fluid motion model, and establish a fluid-solid coupling model to simulate the hydroplaning state of the tire;

[0060] Fluid-solid coupling uses a "fluid motion model", that is, the tire rotates in place, and water layer pumping and road translation are set to simulate the tire driving on the water layer. Compared with the traditional "tire rolling model", this model does not roll forward for a distance, but rotates in place. It only needs to establish a fluid model of the tire contact area, and there is no need to set a water film for the entire road section, which greatly reduces the number of grids and improves the solution efficiency. The specific steps of S1 are:

[0061] S11: Draw a two-dimensional cross-sectional view of the tire using the two-dimensional drawing software CAD, then perform meshing using the pre-processing software HyperMesh, and finally import the processed two-dimensional model into ABAQUS and rotate it to generate a three-dimensional tire model;

[0062] S12: Use ABAQUS to establish a finite element model of the water layer and perform meshing using HyperMesh software;

[0063] S13: The tire and the water layer are coupled to obtain a fluid-solid coupling model, such as Figure 1 As shown in the figure, the fluid-solid coupling model is verified. The specific steps for verifying the fluid-solid coupling model are as follows:

[0064] The critical hydroplaning speed v given by NASA is used. hCompare with the critical hydroplaning speed obtained by simulation for verification:

[0065]

[0066] Where P is the tire inflation pressure.

[0067] S2: According to the established finite element model, the vertical stress distribution curve of the tire center under different working conditions is obtained by setting different tread wear rates, tire inflation pressures, tire vertical loads, and tread rubber materials. The vertical stress distribution of the tire center corresponds to the "three areas" of tire hydroplaning. The hydroplaning water flow traces of different tread wear at different speeds are compared and verified with the Dunlap model. The specific steps are as follows:

[0068] S21: According to the established fluid-solid coupling model, under different tread wear rates (0%-100%), different water layer thicknesses (2mm-20mm), different tire pressures (1.7bar-2.9bar), vertical loads (2000N-5000N) and tread rubber (0.8C 10 , C 10 , 1.2C 10 ) to perform simulation;

[0069] S22: Obtain the “three regions” of tire hydroplaning, namely, the water flow surrounding region, the water film region and the contact region, such as Figure 2 As shown in the figure; when the tire just contacts the water layer, vertical stress is generated and increases linearly. At this time, the tire is separated from the road surface, and the stress is only applied by the water layer, with almost no adhesion; in the water film area, the tire is lifted by the water film and partially contacts the road surface. At this time, the tire stress remains almost unchanged; in the contact area, the tire is completely in contact with the ground, and the vertical stress distribution is saddle-shaped, which is similar to the vertical stress distribution of a dry road surface.

[0070] S23: Obtaining a hydroplaning water flow cloud map of tires with different tread wear and different speeds; Figure 3-7 As shown in the figure, the cloud map of water flow imprint of tire hydroplaning at different speeds with different tread wear is obtained. Figure 4 The 20km / h shows the initial contact stage when the tread wear is 25%. Figure 5 The 60km / h shows the complete immersion stage when the tread is worn to 50%. Figure 6 The 75km / h shows the critical hydroplaning stage when the tread wear is 75% and Figure 7The figure shows the dynamic changes of water flow in the complete hydroplaning stage when the tread wear is 100%. The height of the hydroplaning water flow footprint in the four figures decreases significantly with the increase of tire tread wear rate, and the corresponding critical hydroplaning speed also decreases accordingly; as the pumping speed continues to increase, the water flow lift becomes larger and larger, and when it is larger than the vertical load of the tire, the pressure distribution between the tire and the ground is uneven and gradually loses stable contact to form a gap, which is then lifted up by the water film, and hydroplaning occurs.

[0071] S24: The critical speed is compared and verified by using the Dunlap worn tire critical hydroplaning speed empirical model. The Dunlap worn tire critical hydroplaning speed empirical model is:

[0072]

[0073] Where W is the tread width, d t is the groove depth, d w is the rain depth, and the verification results are shown in Table 1:

[0074] Table 1 Comparison and verification of critical speed of Dunlap model

[0075]

[0076] According to the Dunlap model, by comparing the simulation speed and empirical speed of the tire under different water depth, tire pressure, tread wear rate and groove depth conditions, the key factors affecting the hydroplaning performance of the tire were deeply discussed. The results of the Dunlap model show that the increase in water depth significantly weakens the tire's grip performance. At the same time, the increase in tire pressure enhances the tire's drainage performance to a certain extent. In addition, the increase in tread wear rate and the decrease in groove depth have a negative impact on the tire's hydroplaning performance, especially when the groove depth is shallow, the tire's drainage capacity is significantly reduced and the risk of hydroplaning is significantly increased. The simulation model shows high accuracy in predicting the tire's hydroplaning performance, and the error is controlled within a reasonable range, thereby verifying the effectiveness of the simulation method in tire performance analysis.

[0077] S3: Based on the finite element method, the influence of tire inflation pressure, water layer thickness, tire vertical load, different tread wear rates and different tread rubber materials on the vehicle's limit hydroplaning speed is analyzed. Considering that the mechanical relationship between tire, water layer and road surface is complex and not just a simple linear system, the relationship between it and the limit hydroplaning speed cannot be directly constructed. Therefore, the BP neural network is used to predict the limit hydroplaning speed. The specific steps are as follows:

[0078] S31: The number of neurons in the input layer is 5, which are respectively inflation pressure, water layer thickness, vertical load, tread wear rate and tread rubber. The number of neurons in the output layer is 1, which is the maximum hydroplaning speed. The number of hidden layers can be divided into three categories: no hidden layer, single hidden layer, and multiple hidden layers. The neural network without hidden layer can only represent linearly separable functions or decisions; a single hidden layer can fit any function of "continuous mapping from one finite space to another finite space", and multiple hidden layers can learn complex descriptions, such as some automatic feature engineering; considering that the number of input layers and output layers is small and the data sample is not large, the number of hidden layers of the BP neural network established in the present invention is set to 1.

[0079] S32: Determine the number range by using an empirical formula for determining the number of neurons in the hidden layer. The empirical formula for the number of neurons in the hidden layer is:

[0080]

[0081] In the formula, node is the number of neurons in the hidden layer, i is the number of neurons in the input layer, e is the number of neurons in the output layer, and ζ is a regulation constant between 1 and 10. The input layer of the present invention is 4, the output layer is 1, so the number of neurons in the hidden layer is 3-12. Then, the data sample is trained according to the algorithm developed by the present invention, and the number of neurons in the hidden layer is determined by RMSE. RMSE is the root mean square error that can represent the performance of the BP neural network. The smaller the root mean square error, the better the fitting effect of the network. According to Fig.12 The number of hidden layers of the BP neural network constructed by the present invention is 6. The activation function of the BP neural network designed by the present invention is the tanh function, the solver is LBFGS, the learning rate is set to 0.01, and the maximum number of iterations is 1000.

[0082] S33: Use the 'itertools.product' function in Python to generate all possible combinations, and then use the 'random.sample' function to randomly select 110 samples from all the combinations. Input the tire inflation pressure, water layer thickness, tire vertical load, tread wear rate and tread rubber value corresponding to each group of samples into the "fluid motion model" for simulation calculation;

[0083] S34: The entire data set is divided into a training set, a validation set, and a test set. In order to obtain more test samples, the validation set accounts for 10%, the test set accounts for 20%, and the training set accounts for 70%;

[0084] S35: Obtaining the limit hydroplaning speed of the vehicle tread wear on a wet road surface.

[0085] The present invention establishes a tire finite element model and a fluid-solid coupling model among the tire, the water layer and the road surface, and uses the finite element method to simulate and analyze the influence of tire inflation pressure, water layer thickness, tire vertical load and tread rubber material on the vehicle's maximum hydroplaning speed under different tire tread wear. Based on the simulation analysis data and in combination with the relationship between tire inflation pressure, tire vertical load, tread rubber material, water layer thickness and maximum hydroplaning speed, the BP neural network is used to predict the maximum hydroplaning speed. Compared with the NASA hydroplaning equation, the accuracy is greatly improved, and the vehicle's maximum hydroplaning speed can be estimated in real time. The driver can adjust the driving strategy according to the predicted value to avoid the vehicle from losing control in the hydroplaning state, thereby improving driving safety.

[0086] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for estimating the limit hydroplaning speed of a vehicle tread wear on a slippery road, characterized in that: The following steps are involved: S1: Use ABAQUS simulation software to establish a three-dimensional finite element model of the tire, water layer and road surface, select a fluid motion model, and establish a fluid-solid coupling model to simulate the hydroplaning state of the tire; S2: Based on the established finite element model, the vertical stress distribution curve of the tire center under different working conditions is obtained by setting different tread wear rates, tire inflation pressures, tire vertical loads, and tread rubber materials. The vertical stress distribution of the tire center corresponds to the "three areas" of tire hydroplaning. The hydroplaning water flow traces of different tread wear at different speeds are compared and verified with the Dunlap model. S3: The influence of tire inflation pressure, water layer thickness, tire vertical load, different tread wear rates and different tread rubber materials on the vehicle's maximum hydroplaning speed is analyzed based on the finite element method, and the maximum hydroplaning speed is predicted using the BP neural network.

2. The method for estimating the limit hydroplaning speed of a vehicle tread wear on a wet road according to claim 1, characterized in that: The specific steps of constructing the three-dimensional finite element model of the tire, water layer and road surface and the fluid-solid coupling model for simulating the hydroplaning state of the tire in S1 are as follows: S11: Draw a two-dimensional cross-sectional view of the tire using the two-dimensional drawing software CAD, then perform meshing using the pre-processing software HyperMesh, and finally import the processed two-dimensional model into ABAQUS and rotate it to generate a three-dimensional tire model; S12: Use ABAQUS to establish a finite element model of the water layer and perform meshing using HyperMesh software; S13: The tire and the water layer are coupled to obtain a fluid-solid coupling model, and the fluid-solid coupling model is verified.

3. The method for estimating the limit hydroplaning speed of a vehicle tread wear on a slippery road according to claim 1, characterized in that: The specific steps of S2 are: S21: According to the established fluid-solid coupling model, simulation is performed under different tread wear rates, different water layer thicknesses, different tire pressures, vertical loads and tread rubber materials; S22: Obtaining the "three areas" of tire hydroplaning, namely, the water flow surrounding area, the water film area and the contact area; S23: Obtaining a hydroplaning water flow cloud map of tires with different tread wear and different speeds; S24: The Dunlap worn tire critical hydroplaning speed empirical model is used to compare the critical speeds and verify its effectiveness.

4. The method for estimating the limit hydroplaning speed of a vehicle tread wear on a wet road according to claim 1, characterized in that: The specific steps of S3 are: S31: The number of neurons in the input layer is 5, which are respectively inflation pressure, water layer thickness, vertical load, tread wear rate and tread rubber. The number of neurons in the output layer is 1, which is the limit hydroplaning speed. The number of hidden layers can be divided into three categories: no hidden layer, single hidden layer, and multiple hidden layers. The neural network without hidden layer can only represent linearly separable functions or decisions. S32: Determine the number range by using an empirical formula for determining the number of neurons in the hidden layer; S33: Use the 'itertools.product' function in Python to generate all possible combinations, and then use the 'random.sample' function to randomly select samples from all combinations. Input the tire inflation pressure, water layer thickness, tire vertical load, tread wear rate and tread rubber value corresponding to each group of samples into the "fluid motion model" for simulation calculation; S34: The entire data set is divided into a training set, a validation set, and a test set. In order to obtain more test samples, the validation set accounts for 10%, the test set accounts for 20%, and the training set accounts for 70%; S35: Obtaining the limit hydroplaning speed of the vehicle tread wear on a wet road surface.

5. The method for estimating the limit hydroplaning speed of a vehicle tread wear on a wet road according to claim 1, characterized in that: The specific steps for verifying the fluid-solid coupling model in S13 are: The critical hydroplaning speed v given by NASA is used. h Compare with the critical hydroplaning speed obtained by simulation for verification: Where P is the tire inflation pressure.

6. The method for estimating the limit hydroplaning speed of a vehicle tread wear on a wet road according to claim 1, characterized in that: The empirical model of critical hydroplaning speed of Dunlap worn tire in S24 is: Where W is the tread width, d t is the groove depth, d w is the rainwater depth.

7. The method for estimating the limit hydroplaning speed of a vehicle tread wear on a wet road according to claim 1, characterized in that: The empirical formula for the number of neurons in the hidden layer in S32 is: Where node is the number of neurons in the hidden layer, i is the number of neurons in the input layer, e is the number of neurons in the output layer, and ζ is a regulation constant between 1 and 10.

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