Track beam and track integrated optimal matching design method for multi-level vibration reduction

Abstract

The invention relates to the technical field of railway vehicles, in particular to a multi-level vibration reduction-oriented track beam-track integrated optimal matching design method, which comprises the following steps of: 1, deducing a multi-vibration reduction level coupling design equation of a train track system; step 2, decoupling the coupling design equation of the multiple vibration reduction hierarchies; 3, through Taylor expansion, functions corresponding to different parameters are expanded to n orders for fitting; and 4, solving a plurality of unknown optimal parameters in the equation aiming at the part in each hierarchy in a neural network mode. According to the method, the design parameters can be well obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of rail vehicles, and in particular to a rail-beam-rail integrated optimal matching design method for multi-level vibration reduction. Background Art

[0002] In recent years, urban rail transit systems have been developing rapidly. However, with the construction of urban rails and inter-city high-speed railways, railway lines will inevitably get closer and closer to schools, hospitals, residential areas and facilities that require shock absorption. The vibration and noise problems caused by this have caused great trouble to surrounding residents, facilities and passengers.

[0003] For this reason, various vibration reduction methods have been proposed in recent years. First, the two-layer vibration reduction structure of rail-track bearing plate-foundation is targeted. Since the vibration reduction of the rail-track bearing plate vibration reduction layer and the track bearing plate-foundation vibration reduction layer is relatively simple and there are relatively few adjustable contents, there is still room for optimization and improvement of the vibration reduction effect. Therefore, it is necessary to study the vibration reduction of three-layer and higher-level tracks. However, due to the mutual coupling between the parameters of the multi-level vibration reduction layers and the high dimensionality, it is extremely difficult to obtain the optimal solution. Therefore, it is necessary to design a track beam-track integrated optimal matching design method for multi-level vibration reduction to obtain the design parameters. Summary of the invention

[0004] The present invention provides a track beam-rail integrated optimal matching design method for multi-level vibration reduction, which can better obtain design parameters.

[0005] The track beam-rail integrated optimal matching design method for multi-level vibration reduction according to the present invention comprises the following steps:

[0006] Step 1: Derive the coupled design equations of multiple vibration reduction levels of the train-track system;

[0007] Step 2: Decouple the coupled design equations of multiple vibration reduction levels;

[0008] Step 3: Through Taylor expansion, expand the corresponding functions of different parameters to nth order for fitting;

[0009] Step 4: There are multiple unknown optimal parameters in the equation for each level, which are solved by neural network.

[0010] Preferably, in step 1, the details are as follows:

[0011] The train track system consists of rails, fasteners or elastic vibration-damping filling materials, rail beams, vibration-damping pads, bridge supports, vibration-damping layers and base plates from top to bottom, with three layers of vibration-damping. The fasteners or elastic vibration-damping filling materials, vibration-damping pads and vibration-damping layers are damping springs, and the base plate is rigid.

[0012] The coupling design formula for multiple vibration reduction levels is:

[0013] A=f(x1,x2,…,x n ) (1)

[0014] where x1 to x n are different vibration reduction levels, x1 is the first-level vibration reduction, which is a fastener or elastic vibration reduction filling material, x2 is the second-level vibration reduction, which is a vibration reduction pad, x3 is the third-level vibration reduction, which is a vibration reduction layer; A is the vibration acceleration Z level.

[0015] Preferably, in step 2, specifically:

[0016] The original equation (1) is a linear equation in which the parameters at all levels are coupled to each other, including the second-order strong coupling term:

[0017] Πx i x j (i≠j) (2)

[0018] And multi-order weak coupling terms:

[0019] Πx i x j ....x n (i≠j≠....≠n) (3)

[0020] x i , x j , x n is a hierarchical variable;

[0021] The linear equation is transformed into a nonlinear independent equation and the vibration reduction parameters at each level are solved iteratively and independently to obtain the decoupled equation:

[0022] A=f(x1)f(x2)f(x3)....f(x n ) (4)

[0023] Although the decoupled equations become nonlinear, the optimal solution of the decoupled design equations for multiple vibration reduction levels can be deduced by simply finding the optimal design for the corresponding equations at different levels.

[0024] Preferably, in step 3, specifically:

[0025] Through Taylor expansion, the functions corresponding to different parameters are expanded to nth order for fitting; for a layer of function f(x n ) is expanded to obtain:

[0026]

[0027] o(n) is a high-order error term, and a, b, c, and d are unknown optimal parameters. The low-order terms are retained and the high-order terms are ignored, and a simplified approximate equation is obtained on the basis of ensuring engineering accuracy. The above function is obtained for each layer of the three-layer vibration reduction design.

[0028] Preferably, in step 4, specifically:

[0029] There are multiple unknown optimal parameters for each level in the equation, namely a, b, c, and d, so they are solved through a neural network. First, a neural network model suitable for regression tasks is built. The 2.5D finite element boundary element calculation results are used as input layer parameters, which are passed layer by layer. Each layer contains several neurons, and the mapping relationship with the true function is finally approximated after using a nonlinear activation function. The performance of the model is then evaluated through a cross-validation method to ensure that the model can accurately fit the unknowns. After the model training is completed, the predictions and estimates of the unknowns a, b, c, and d are finally output. These predicted values ​​are brought into the original equation and the optimization results are observed.

[0030] The present invention can provide a design idea for obtaining optimal design parameters for three-layer and higher-level vibration reduction structures, thereby further completing the vibration reduction design and reducing the impact of track noise and vibration on the surrounding environment and passengers. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a flow chart of a method for designing optimal matching of rail beam-rail integration for multi-level vibration reduction in an embodiment;

[0032] Figure 2 Schematic diagram of a track coupling model using a three-layer vibration reduction structure of rail-rail beam-bridge support-base plate in the embodiment;

[0033] Figure 3 Schematic diagram of a parameter fitting neural network model suitable for regression tasks in an embodiment. DETAILED DESCRIPTION

[0034] In order to further understand the content of the present invention, the present invention is described in detail in conjunction with the accompanying drawings and embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.

[0035] Example

[0036] like Figure 1 As shown, this embodiment provides a rail beam-rail integrated optimal matching design method for multi-level vibration reduction, which includes the following steps:

[0037] Step 1: Derive the coupled design equations of multiple vibration reduction levels of the train-track system;

[0038] Step 2: Decouple the coupled design equations of multiple vibration reduction levels;

[0039] Step 3: Through Taylor expansion, expand the corresponding functions of different parameters to nth order for fitting;

[0040] Step 4: There are multiple unknown optimal parameters in the equation for each level, which are solved by neural network.

[0041] In step 1, the details are as follows:

[0042] like Figure 2 As shown, the train track system is composed of rails, fasteners or elastic vibration-damping filling materials, rail beams, vibration-damping pads, bridge supports, vibration-damping layers and base plates from top to bottom, with three layers of vibration-damping; the fasteners or elastic vibration-damping filling materials, vibration-damping pads and vibration-damping layers are damping springs, and the base plate is rigid;

[0043] The coupling design formula for multiple vibration reduction levels is:

[0044] A=f(x1,x2,…,x n ) (1)

[0045] where x1 to x n For different vibration reduction levels, x1 is the first-level vibration reduction, which is a fastener or elastic vibration reduction filling material, that is, the part directly connected to the rail. If it is a fastener type track system, it is a fastener system; if it is an embedded track, it is an elastic vibration reduction filling material; x2 is the second-level vibration reduction, which is a vibration reduction pad; x3 is the third-level vibration reduction, which is a vibration reduction layer; A is the vibration acceleration Z level.

[0046] In step 2, specifically:

[0047] The original equation (1) is a linear equation in which the parameters at all levels are coupled to each other, including the second-order strong coupling term:

[0048] Πx i x j (i≠j) (2)

[0049] And multi-order weak coupling terms:

[0050] Πx i x j ....x n (i≠j≠....≠n) (3)

[0051] x i , x j , x n is a hierarchical variable;

[0052] In the process of solving the problem, the complexity will increase sharply, which makes it difficult to match the design. Therefore, the linear equation is converted into a nonlinear independent equation and the vibration reduction parameters at each level are solved iteratively and independently to obtain the decoupled equation:

[0053] A=f(x1)f(x2)f(x3)....f(x n ) (4)

[0054] Although the decoupled equations become nonlinear, the optimal solution of the decoupled design equations for multiple vibration reduction levels can be deduced by simply finding the optimal design for the corresponding equations at different levels.

[0055] In step 3, specifically:

[0056] Through Taylor expansion, the functions corresponding to different parameters are expanded to nth order for fitting; one layer of functions is expanded to obtain:

[0057]

[0058] o(n) is a high-order error term, and a, b, c, and d are unknown optimal parameters. The low-order terms are retained and the high-order terms are ignored, and a simplified approximate equation is obtained on the basis of ensuring engineering accuracy. The above function is obtained for each layer of the three-layer vibration reduction design.

[0059] In step 4, specifically:

[0060] There are multiple unknown optimal parameters in each level of the equation, namely a, b, c, d, so it is solved by neural network; taking the fitting neural network as an example, Figure 3 As shown in the figure, a neural network model suitable for regression tasks is first constructed; the 2.5D finite element boundary element calculation results are used as input layer parameters, and are passed layer by layer. Each layer contains several neurons, and a nonlinear activation function (including ReLU or Sigmoid) is used to finally approximate the mapping relationship with the true function; the performance of the model is then evaluated by the cross-validation method to ensure that the model can accurately fit the unknowns; after the model training is completed, the predictions and estimates of the unknowns a, b, c, and d are finally output; these predicted values ​​are brought into the original equation and the optimization results are observed.

[0061] This embodiment can provide a design idea for obtaining optimal design parameters for vibration reduction structures of three layers and above, thereby further completing the vibration reduction design and reducing the impact of track noise and vibration on the surrounding environment and passengers.

[0062] The present invention and its embodiments are described schematically above, and the description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. Therefore, if a person skilled in the art is inspired by it and designs a structural method and an embodiment similar to the technical solution without creativity without departing from the purpose of the invention, they shall all fall within the protection scope of the present invention.

Claims

1. An optimal matching design method for rail-beam integration for multi-level vibration reduction, characterized by: The following steps are involved: Step 1: Derive the coupled design equations of multiple vibration reduction levels of the train-track system; Step 2: Decouple the coupled design equations of multiple vibration reduction levels; Step 3: Through Taylor expansion, expand the corresponding functions of different parameters to nth order for fitting; Step 4: There are multiple unknown optimal parameters in the equation for each level, which are solved by neural network.

2. The optimal matching design method for rail-beam integration for multi-level vibration reduction according to claim 1, characterized in that: In step 1, the details are as follows: The train track system consists of rails, fasteners or elastic vibration-damping filling materials, rail beams, vibration-damping pads, bridge supports, vibration-damping layers and base plates from top to bottom, with three layers of vibration-damping. The fasteners or elastic vibration-damping filling materials, vibration-damping pads and vibration-damping layers are damping springs, and the base plate is rigid. The coupling design formula for multiple vibration reduction levels is: A=f(x1,x2,…,x n ) (1) where x1 to x n are different vibration reduction levels, x1 is the first-level vibration reduction, which is a fastener or elastic vibration reduction filling material, x2 is the second-level vibration reduction, which is a vibration reduction pad, x3 is the third-level vibration reduction, which is a vibration reduction layer; A is the vibration acceleration Z level.

3. The track beam-rail integrated optimal matching design method for multi-level vibration reduction according to claim 2, characterized in that: In step 2, specifically: The original equation (1) is a linear equation in which the parameters at all levels are coupled to each other, including the second-order strong coupling term: Πx i x j (i≠j) (2) And multi-order weak coupling terms: Πx i x j ....x n (i≠j≠....≠n) (3) x i , x j , x n is a hierarchical variable; The linear equation is transformed into a nonlinear independent equation and the vibration reduction parameters at each level are solved iteratively and independently to obtain the decoupled equation: A=f(x1)f(x2)f(x3)....f(x n ) (4) Although the decoupled equations become nonlinear, the optimal solution of the decoupled design equations for multiple vibration reduction levels can be deduced by simply finding the optimal design for the corresponding equations at different levels.

4. The optimal matching design method for rail-beam integration for multi-level vibration reduction according to claim 3, characterized in that: In step 3, specifically: Through Taylor expansion, the functions corresponding to different parameters are expanded to nth order for fitting; for a layer of function f(x n ) is expanded to obtain: o(n) is a high-order error term, and a, b, c, and d are unknown optimal parameters. The low-order terms are retained and the high-order terms are ignored, and a simplified approximate equation is obtained on the basis of ensuring engineering accuracy. The above function is obtained for each layer of the three-layer vibration reduction design.

5. The track beam-rail integrated optimal matching design method for multi-level vibration reduction according to claim 4, characterized in that: In step 4, specifically: There are multiple unknown optimal parameters for each level in the equation, namely a, b, c, and d, so they are solved through a neural network. First, a neural network model suitable for regression tasks is built. The 2.5D finite element boundary element calculation results are used as input layer parameters, which are passed layer by layer. Each layer contains several neurons, and the mapping relationship with the true function is finally approximated after using a nonlinear activation function. The performance of the model is then evaluated through a cross-validation method to ensure that the model can accurately fit the unknowns. After the model training is completed, the predictions and estimates of the unknowns a, b, c, and d are finally output. These predicted values ​​are brought into the original equation and the optimization results are observed.

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