Steel spring structure parameter optimization method and device of heavy-duty locomotive, computer equipment and readable storage medium

Abstract

The invention relates to a method and a device for optimizing structural parameters of a steel spring of a heavy-load locomotive, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: based on a rigid-flexible coupling dynamics theory, establishing a heavy-load locomotive rigid-flexible coupling dynamics model; inputting the actually measured wheel-rail excitation data of the heavy-load locomotive into the heavy-load locomotive rigid-flexible coupling dynamic model to obtain a plurality of first steel spring dynamic stresses respectively corresponding to each structure parameter variable of the steel spring; the multiple first steel spring dynamic stresses corresponding to each structure parameter variable are obtained through calculation under the condition that the structure parameter variables adopt multiple parameter values; determining a target structure parameter value of the steel spring according to the change conditions of the multiple first steel spring dynamic stresses corresponding to the structure parameter variables; wherein under the condition that the target structure parameter value is adopted by the steel spring, the steel spring dynamic stress corresponding to the steel spring meets the preset dynamic stress minimization condition. The method can improve the reliability of the steel spring.

Description

Technical Field

[0001] The present application relates to the field of rail transit technology, and in particular to a method, device, computer equipment, computer-readable storage medium, and computer program product for optimizing the structural parameters of a steel spring of a heavy-load locomotive. Background Art

[0002] In recent years, primary spring failures have become a frequent occurrence on heavy-haul locomotives in my country. As a key component of the primary suspension system, springs play a vital role in transmitting and damping vibrations caused by wheel-rail surface irregularities. However, with the increase in heavy-haul train volume and speed, the problem of wheel-rail irregularities has become more severe, exacerbating the high-frequency and high-amplitude vibrations of the primary springs, making them susceptible to fatigue failure or even fracture. Failure or fracture of springs directly impacts the safety of heavy-haul train operation and significantly increases operating and maintenance costs.

[0003] In related technologies, preventive measures for steel spring breakage are mostly limited to controlling external excitation sources (such as rail grinding), but there is still the problem that steel springs are prone to failure or even breakage, affecting the safety of locomotive operation. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for optimizing the structural parameters of steel springs for heavy-duty locomotives, which can improve the reliability of steel springs, in order to address the above technical problems.

[0005] In a first aspect, the present application provides a method for optimizing the structural parameters of a steel spring of a heavy-duty locomotive, comprising:

[0006] Based on the rigid-flexible coupling dynamics theory, a rigid-flexible coupling dynamics model of a heavy-load locomotive is established; the rigid-flexible coupling dynamics model of the heavy-load locomotive is obtained by coupling a rigid body dynamics model of the heavy-load locomotive and a steel spring finite element model of a steel spring of the heavy-load locomotive;

[0007] Inputting the measured wheel-rail excitation data of the heavy-load locomotive into the rigid-flexible coupling dynamic model of the heavy-load locomotive to obtain a plurality of first steel spring dynamic stresses corresponding to each structural parameter variable of the steel spring; the plurality of first steel spring dynamic stresses corresponding to each structural parameter variable are calculated when the structural parameter variable adopts multiple parameter values;

[0008] The target structural parameter value of the steel spring is determined based on changes in the dynamic stresses of multiple first steel springs corresponding to each of the structural parameter variables; wherein, when the steel spring adopts the target structural parameter value, the dynamic stress of the steel spring corresponding to the steel spring satisfies a preset dynamic stress minimization condition.

[0009] In one embodiment, determining the target structural parameter value of the steel spring according to the change of the dynamic stress of the plurality of first steel springs corresponding to each of the structural parameter variables includes:

[0010] At least one target structural parameter variable is selected from each of the structural parameter variables; the target structural parameter variable has a greater influence on the change of the dynamic stress of the corresponding first steel spring than the unselected structural parameter variables have on the change of the dynamic stress of the corresponding first steel spring;

[0011] Obtain target parameter values ​​corresponding to the target structural parameter variables as target structural parameter values ​​of the steel spring; wherein, when the target structural parameter variables adopt corresponding target parameter values, the dynamic stress of the steel spring corresponding to the steel spring satisfies the dynamic stress minimization condition.

[0012] In one embodiment, screening out at least one target structural parameter variable from each of the structural parameter variables comprises:

[0013] According to the plurality of first steel spring dynamic stresses corresponding to the respective structural parameter variables, obtaining a change in the first steel spring dynamic stress corresponding to each of the structural parameter variables as a dynamic stress change;

[0014] According to the dynamic stress variation corresponding to each of the structural parameter variables, at least one structural parameter whose corresponding dynamic stress variation meets a preset variation condition is screened out as the target structural parameter variable.

[0015] In one embodiment, obtaining the target parameter value corresponding to each target structural parameter variable includes:

[0016] Obtaining the second steel spring dynamic stress corresponding to each of a plurality of parameter value combinations of the steel spring; wherein each parameter value combination includes a parameter value corresponding to each of the target structural parameter variables;

[0017] At least one set of parameter value combinations that minimize the dynamic stress of the corresponding second steel spring is determined, and the parameter values ​​in the at least one set of parameter value combinations are used as the target parameter values.

[0018] In one embodiment, establishing a rigid-flexible coupling dynamics model of a heavy-load locomotive based on the rigid-flexible coupling dynamics theory includes:

[0019] Based on the multi-rigid body dynamics theory and the actual structural parameters of the heavy-haul locomotive, a rigid body dynamics model of the heavy-haul locomotive is established;

[0020] Establishing a finite element model of the steel spring according to the structural parameters of the steel spring; the structural parameters include the structural parameter variables and corresponding parameter values;

[0021] Based on the rigid-flexible coupling dynamics theory, the rigid body dynamics model and the steel spring finite element model are coupled to obtain the rigid-flexible coupling dynamics model of the heavy-duty locomotive.

[0022] In one embodiment, the rigid-flexible coupling dynamics model of the heavy-load locomotive is obtained by coupling the rigid body dynamics model and the steel spring finite element model based on the rigid-flexible coupling dynamics theory, including:

[0023] Performing modal analysis on the steel spring finite element model to obtain modal analysis results;

[0024] Based on the rigid-flexible coupling dynamics theory and in combination with the modal analysis results, the rigid body dynamics model and the steel spring finite element model are coupled to obtain the rigid-flexible coupling dynamics model of the heavy-duty locomotive.

[0025] In a second aspect, the present application further provides a device for optimizing the structural parameters of a steel spring for a heavy-duty locomotive, comprising:

[0026] Establishing a module for establishing a rigid-flexible coupling dynamics model of a heavy-load locomotive based on the rigid-flexible coupling dynamics theory; the rigid-flexible coupling dynamics model of the heavy-load locomotive is obtained by coupling a rigid body dynamics model of the heavy-load locomotive and a steel spring finite element model of a steel spring of the heavy-load locomotive;

[0027] an acquisition module, configured to input the measured wheel-rail excitation data of the heavy-load locomotive into the rigid-flexible coupling dynamic model of the heavy-load locomotive to obtain a plurality of first steel spring dynamic stresses corresponding to each structural parameter variable of the steel spring; the plurality of first steel spring dynamic stresses corresponding to each structural parameter variable are calculated when the structural parameter variable adopts multiple parameter values;

[0028] A determination module is used to determine a target structural parameter value of the steel spring based on changes in the dynamic stresses of multiple first steel springs corresponding to each of the structural parameter variables; wherein, when the steel spring adopts the target structural parameter value, the dynamic stress of the steel spring corresponding to the steel spring satisfies a preset dynamic stress minimization condition.

[0029] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0030] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when executed by a processor.

[0031] In a fifth aspect, the present application also provides a computer program product, comprising a computer program, which implements the steps of the above method when executed by a processor.

[0032] The above-mentioned steel spring structural parameter optimization method, device, computer equipment, computer-readable storage medium and computer program product for heavy-load locomotives establish a rigid-flexible coupling dynamics model of a heavy-load locomotive based on the rigid-flexible coupling dynamics theory; the rigid-flexible coupling dynamics model of the heavy-load locomotive is obtained by coupling the rigid body dynamics model of the heavy-load locomotive and the steel spring finite element model of the steel spring of the heavy-load locomotive; the measured wheel-rail excitation data of the heavy-load locomotive is input into the rigid-flexible coupling dynamics model of the heavy-load locomotive to obtain a plurality of first steel spring dynamic stresses corresponding to each structural parameter variable of the steel spring; the plurality of first steel spring dynamic stresses corresponding to each structural parameter variable are calculated when the structural parameter variable adopts multiple parameter values; the target structural parameter value of the steel spring is determined according to the changes in the plurality of first steel spring dynamic stresses corresponding to each structural parameter variable; wherein, when the steel spring adopts the target structural parameter value, the steel spring dynamic stress corresponding to the steel spring meets the preset dynamic stress minimization condition.

[0033] In this way, based on the rigid-flexible coupling dynamics theory, the present application couples the rigid body dynamics model of a heavy-load locomotive and the steel spring finite element model of the steel spring of the heavy-load locomotive, fully considering the flexible deformation characteristics and structural parameters of the steel spring, and can more accurately establish a rigid-flexible coupling dynamics model of the heavy-load locomotive. Through the rigid-flexible coupling dynamics model of the heavy-load locomotive, based on the measured wheel-rail excitation data of the heavy-load locomotive, multiple first steel spring dynamic stresses corresponding to each structural parameter variable of the steel spring are obtained. Therefore, the target structural parameter value of the steel spring can be determined according to the change of the multiple first steel spring dynamic stresses corresponding to each structural parameter variable. The target structural parameter value is the parameter value when the steel spring dynamic stress corresponding to the steel spring meets the preset dynamic stress minimization condition. Therefore, the use of the target structural parameter value can effectively reduce the vibration acceleration and dynamic stress of the steel spring under wheel-rail excitation, reduce the occurrence of steel spring failure and fracture, and effectively improve the reliability of the steel spring. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 1 is a flow chart of a method for optimizing the structural parameters of a steel spring for a heavy-load locomotive in one embodiment;

[0036] Figure 2 is a schematic diagram of main structural parameters of a steel spring in one embodiment;

[0037] Figure 3 Comparison results of dynamic stress for a spring wire diameter under different parameter values ​​in one embodiment;

[0038] Figure 4 The comparison results of dynamic stress of a spring with different mean diameters and different parameter values ​​in one embodiment are shown;

[0039] Figure 5 Comparison results of dynamic stress at different free heights and different parameter values ​​in one embodiment;

[0040] Figure 6 Comparison results of dynamic stress under different parameter values ​​for different numbers of end turns in one embodiment;

[0041] Figure 7 The dynamic stress comparison results of a spring wire diameter and a spring mid-diameter under the combined action of different parameter value combinations in one embodiment are shown;

[0042] Figure 8 A schematic flow chart of a method for optimizing structural parameters of a steel spring for a heavy-load locomotive according to another embodiment;

[0043] Figure 9 This is a structural block diagram of a device for optimizing the structural parameters of a steel spring for a heavy-load locomotive in one embodiment;

[0044] Figure 10 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0046] It should be noted that the terms "first," "second," and the like in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the numbers used in this manner are interchangeable where appropriate so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure as detailed in the appended claims.

[0047] In one embodiment, Figure 1 As shown, a method for optimizing the structural parameters of a steel spring for a heavy-duty locomotive is provided. This embodiment uses the method applied to a computer device as an example. It is understood that the computer device can be a terminal, a server, or a system including a terminal and a server, and implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0048] Step S110 : establishing a rigid-flexible coupling dynamics model of a heavy-load locomotive based on the rigid-flexible coupling dynamics theory.

[0049] The heavy-haul locomotive rigid-flexible coupling dynamics model is obtained by coupling the rigid-body dynamics model of the heavy-haul locomotive with the steel spring finite element model of the heavy-haul locomotive's steel springs. Specifically, the computer device can obtain the rigid-body dynamics model and the steel spring finite element model of the heavy-haul locomotive, couple the rigid-body dynamics model and the steel spring finite element model, and obtain the heavy-haul locomotive rigid-flexible coupling dynamics model.

[0050] Among them, the rigid body dynamics model of the heavy-load locomotive is a rigid body dynamics model established based on the multi-rigid body dynamics theory and the actual structural parameters of the heavy-load locomotive.

[0051] In practice, in the rigid-body dynamics model, all components, except the axlebox and motor, are considered for nodal freedom, with six degrees of freedom considered. All suspension components, except the primary steel spring, are simulated using nonlinear spring-damper force elements. The wheel-rail normal force is solved using Hertz contact theory, while the tangential force is calculated using Kalker's simplified theory, both using the FASTSIM algorithm.

[0052] The steel spring finite element model is a finite element model established based on the structural parameters of the steel spring. The structural parameters include structural parameter variables and corresponding parameter values. The structural parameter variables of the steel spring may include at least one of the spring wire diameter d, the spring pitch diameter D, the free height H, and the number of end turns N.

[0053] To facilitate understanding by those skilled in the art, Figure 2 A schematic diagram of the main structural parameters of the steel spring is provided, including the spring wire diameter d, the spring center diameter D, the free height H, and the number of end coils N.

[0054] Specifically, a three-dimensional model of the steel spring can be constructed based on the structural parameters of the steel spring, and then the three-dimensional model of the steel spring can be imported into the finite element analysis software. The finite element model of the steel spring can be constructed by meshing the three-dimensional model of the steel spring, assigning material properties, defining boundary conditions, and loading loads.

[0055] Furthermore, based on the rigid-flexible coupling dynamics theory, the rigid body dynamics model and the steel spring finite element model can be coupled to obtain a rigid-flexible coupling dynamics model of a heavy-duty locomotive.

[0056] By introducing a steel spring finite element model, the elastic deformation of the spring can be accurately simulated, thereby more accurately reflecting the locomotive's dynamic response during operation, such as vibration and impact. This allows the rigid-flexible coupling dynamic model of a heavy-duty locomotive to more realistically simulate the internal mechanical transmission processes of the locomotive. As a key component connecting different locomotive components, the elastic properties of the steel spring affect the path and magnitude of force transmission. The rigid-flexible coupling model accounts for this variability in force transmission, resulting in simulation results that are closer to reality.

[0057] Among them, in the process of coupling the rigid body dynamics model and the steel spring finite element model based on the rigid-flexible coupling dynamics theory to obtain the rigid-flexible coupling dynamics model of the heavy-duty locomotive, the steel spring finite element model can be subjected to modal analysis to obtain the modal analysis results; and based on the rigid-flexible coupling dynamics theory, the rigid body dynamics model and the steel spring finite element model are coupled in combination with the modal analysis results to obtain the rigid-flexible coupling dynamics model of the heavy-duty locomotive.

[0058] In this way, modal analysis can determine the steel spring's dynamic characteristics, such as its natural frequency and mode shape. These characteristics are key factors in the spring's response in a dynamic environment. Incorporating this modal information into the coupling process allows for a more accurate description of the spring's elastic deformation and vibration response during locomotive operation, enabling the rigid-flexible coupling dynamic model to more accurately reflect actual physical phenomena. Furthermore, modal analysis results can be used to verify and optimize the parameters of the steel spring's finite element model, providing guidance for improved structural design of the spring.

[0059] Step S120 , inputting the measured wheel-rail excitation data of the heavy-haul locomotive into the rigid-flexible coupling dynamic model of the heavy-haul locomotive to obtain a plurality of first steel spring dynamic stresses corresponding to the structural parameter variables of the steel spring.

[0060] In the process of calculating the dynamic stress of the steel spring, the parameter values ​​of the structural parameter variables can be continuously adjusted to calculate a plurality of first dynamic stresses of the steel spring.

[0061] Among them, the first steel spring dynamic stress refers to the root mean square stress (RMS).

[0062] The multiple first steel spring dynamic stresses corresponding to each structural parameter variable are calculated when the structural parameter variable adopts multiple parameter values.

[0063] Among them, the parameter value corresponding to each structural parameter variable is within the corresponding geometric position limit.

[0064] Specifically, the dynamic stress of the steel spring can be calculated by inputting measured wheel-rail excitations into the rigid-flexible coupling dynamics model of a heavy-duty locomotive. During the dynamic stress calculation, each structural parameter variable can correspond to multiple parameter values. This allows the dynamic stress of each structural parameter variable to be calculated under different parameter values, thereby calculating multiple first steel spring dynamic stresses corresponding to each structural parameter variable.

[0065] It is understandable that when switching the structural parameter variables to different parameter values, the steel spring finite element model also needs to be updated due to the change in parameter values ​​to obtain a new steel spring finite element model. At the same time, based on the rigid-flexible coupling dynamics theory, it is necessary to couple the rigid body dynamics model and the new steel spring finite element model to obtain a new heavy-duty locomotive rigid-flexible coupling dynamics model. The measured wheel-rail excitation data of the heavy-duty locomotive is then input into the new heavy-duty locomotive rigid-flexible coupling dynamics model to obtain the dynamic stress calculated for the structural parameter variables under the current parameter values. This is done until the current parameter value of the structural parameter variable approaches the geometric position limit of the steel spring (i.e., the difference between the current parameter value of the structural parameter variable and the corresponding geometric position limit of the structural parameter variable is less than or equal to the preset parameter threshold) or the steel spring strength approaches the limit (i.e., the difference between the current strength of the steel spring and the strength threshold is less than or equal to the preset strength threshold). In this way, the dynamic stress of each structural parameter variable under different parameter values ​​can be obtained.

[0066] like Figure 2 As shown in FIG, the variable structural parameters of the steel spring finite element model may include the spring wire diameter d, the spring mid-diameter D, the free height H, and the number of end coils N. Where D1, D2, and D represent the spring mid-diameter under different parameter values.

[0067] For each structural parameter variable, a corresponding upper and lower limit value can be obtained as a geometric limit value. Multiple parameter values ​​corresponding to each structural parameter variable can be determined based on the geometric limit value and the original design parameter values. The original design parameters of the structural parameter variable can be used to construct an initial steel spring finite element model.

[0068] For example, each structural parameter variable can be assigned three values: an upper limit, a lower limit, and the original design parameter. For example, the spring wire diameter d can be set to 36 mm (lower limit), 38.6 mm (original design parameter), and 42 mm (upper limit); the spring center diameter D can be set to 180 mm (lower limit), 191 mm (original design parameter), and 200 mm (upper limit); the free height H can be set to 280 mm (lower limit), 325 mm (original design parameter), and 360 mm (upper limit); and the number of end turns N can be set to 1 turn (lower limit), 1.5 turns (original design parameter), and 1.8 turns (upper limit).

[0069] By sequentially optimizing and analyzing these single structural parameter variables, multiple first steel spring dynamic stresses corresponding to each structural parameter variable are obtained. These multiple first steel spring dynamic stresses corresponding to each structural parameter variable are calculated when the structural parameter variable adopts multiple corresponding parameter values. For example, in the above example, each structural parameter variable corresponds to three parameter values. Therefore, three first steel spring dynamic stresses can be calculated for each structural parameter variable under the corresponding three parameter values.

[0070] To facilitate understanding by those skilled in the art, taking a vehicle running at a speed of 80 km / h in a straight line, and the measured wheel-rail excitation data as the measured irregularity and the American five-level spectrum as an example, the simulation model can output the dynamic stress of the steel spring in real time, and calculate the root mean square stress (RMS) based on the output dynamic stress as the first steel spring dynamic stress. Figures 3 to 6 Schematic diagram of the comparison of the dynamic stress of the first steel spring corresponding to different parameter values ​​of the spring wire diameter d, the spring middle diameter D, the free height H and the number of end coils N.

[0071] Step S130 , determining target structural parameter values ​​of the steel spring according to changes in dynamic stresses of the plurality of first steel springs corresponding to the structural parameter variables.

[0072] Among them, when the steel spring adopts the target structural parameter value, the steel spring dynamic stress corresponding to the steel spring meets the preset dynamic stress minimization condition.

[0073] In a specific implementation, the computer device can determine the target structural parameter value of the steel spring according to the change of the dynamic stress of the plurality of first steel springs corresponding to each structural parameter variable.

[0074] In the process of determining the target structural parameter value of the steel spring, the computer device can first screen out the target structural parameter variable from the various structural parameter variables, and the degree of influence of the target structural parameter variable on the change of the dynamic stress of the corresponding first steel spring is greater than the degree of influence of the unscreened structural parameter variables on the change of the dynamic stress of the corresponding first steel spring.

[0075] Specifically, based on the multiple first steel spring dynamic stresses corresponding to each of the structural parameter variables, the change in the first steel spring dynamic stress corresponding to each of the structural parameter variables is obtained as the dynamic stress change. Based on the dynamic stress change corresponding to each of the structural parameter variables, at least one structural parameter whose corresponding dynamic stress change meets a preset change condition is selected as the target structural parameter variable. In this way, the target structural parameter variable with the greatest impact on the change in dynamic stress can be accurately selected for further analysis.

[0076] like Figures 3 to 6 As shown, the dynamic stress of the steel spring is more sensitive to changes in the spring wire diameter d and the spring mid-diameter D, while the change in dynamic stress is less obvious when the free height H and the number of end coils N are changed. That is, the change in the dynamic stress of the first steel spring corresponding to the spring wire diameter d and the spring mid-diameter D is greater than the change in the dynamic stress of the first steel spring corresponding to the free height H and the number of end coils N. The spring wire diameter d and the spring mid-diameter D serve as target structural parameter variables for the corresponding dynamic stress changes that meet the preset change condition.

[0077] In this way, the target structural parameter variables can be continuously optimized and analyzed. By continuously adjusting the parameter values ​​of the target structural parameter variables, multiple dynamic stresses can be calculated, so that the parameter values ​​when the dynamic stress corresponding to the steel spring meets the dynamic stress minimization condition can be determined as the target parameter values. The target parameter values ​​can be used as the target structural parameter values ​​of the steel spring.

[0078] In this embodiment, at least one target structural parameter variable is screened out from each structural parameter variable; the degree of influence of the target structural parameter variable on the change of the dynamic stress of the corresponding first steel spring is greater than the degree of influence of the unscreened structural parameter variables on the change of the dynamic stress of the corresponding first steel spring; the target parameter value corresponding to each target structural parameter variable is obtained as the target structural parameter value of the steel spring; wherein, when each target structural parameter variable adopts the corresponding target parameter value, the dynamic stress of the steel spring corresponding to the steel spring satisfies the dynamic stress minimization condition.

[0079] In this way, by screening out the target structural parameter variables that have a greater impact on the dynamic stress change for further analysis, while reducing the amount of calculation, we can focus on the impact of the parameter value changes of the target parameter variables on the dynamic stress of the steel spring. By adjusting the parameter values ​​of the target parameter variables, the dynamic stress level of the steel spring can be more effectively reduced to determine the target parameter value when the dynamic stress of the steel spring corresponding to the steel spring meets the dynamic stress minimization condition, thereby achieving the optimization of the steel spring performance.

[0080] In some embodiments, in the process of obtaining the target parameter values ​​corresponding to each target structural parameter variable, after the target structural parameter variable is determined, multiple groups of parameter value combinations set for the target structural parameter variable can be obtained, each group of parameter value combinations including parameter values ​​corresponding to each target structural parameter variable, thereby calculating the root mean square stress (RMS) under the joint action of the target structural parameter variables as the dynamic stress of the second steel spring.

[0081] Specifically, in the process of switching the target structural parameter variables to different parameter value combinations, the steel spring finite element model also needs to be updated due to changes in the parameter values. The steel spring finite element model is updated by combining the target structural parameter variables with the current parameter values ​​to obtain a new steel spring finite element model. At the same time, based on the rigid-flexible coupling dynamics theory, the rigid body dynamics model and the new steel spring finite element model need to be coupled to obtain a new heavy-duty locomotive rigid-flexible coupling dynamics model. The measured wheel-rail excitation data of the heavy-duty locomotive is then input into the new heavy-duty locomotive rigid-flexible coupling dynamics model to obtain the dynamic stress calculated for the target structural parameter variables under the current parameter value combination, until the parameter values ​​in the current parameter value combination of the target structural parameter variables approach the geometric position limit of the steel spring (i.e., the difference between the parameter values ​​in the current parameter value combination and the geometric position limit corresponding to the target structural parameter variables is less than or equal to the preset parameter threshold) or the steel spring strength approaches the limit (i.e., the difference between the current strength of the steel spring and the strength threshold is less than or equal to the preset strength threshold). Thus, the dynamic stress of the target structural parameter variable under multiple sets of parameter value combinations can be obtained to calculate the dynamic stress of the second steel spring corresponding to the multiple sets of parameter value combinations.

[0082] In this way, the computer device can determine at least one set of parameter value combinations that minimize the dynamic stress of the corresponding second steel spring, and use the parameter values ​​in the at least one set of parameter value combinations as target parameter values.

[0083] In practical applications, a set of parameter value combinations that minimize the dynamic stress of the corresponding second steel spring can be selected, and the parameter values ​​in this set of parameter value combinations can be used as target parameter values.

[0084] To facilitate understanding by those skilled in the art, Figure 7This article provides a comparison of dynamic stresses for different combinations of spring wire diameter d and spring mid-diameter D. These two parameters are the target structural parameters. Furthermore, Table 1 provides the RMS dynamic stress values ​​for these different combinations:

[0085] Table 1

[0086]

[0087] As shown in Table 1, when the spring wire diameter d is 41.3 mm and the spring middle diameter D is 188 mm, the corresponding dynamic stress drops to the minimum value of 13.7 MPa. Therefore, this group of parameter values ​​can be used as the final optimized value, that is, the target parameter value.

[0088] The technical solution of this embodiment obtains the second steel spring dynamic stress corresponding to multiple parameter value combinations of the steel spring; each parameter value combination includes parameter values ​​corresponding to each target structural parameter variable; determines at least one parameter value combination that minimizes the corresponding second steel spring dynamic stress, and uses the parameter values ​​in at least one parameter value combination as the target parameter value. Thus, after screening out the target structural parameter variables that have a significant impact on the dynamic stress change, the parameter values ​​of the target structural parameter variables are continuously adjusted to determine the second steel spring dynamic stress calculated under the combined action of the target structural parameter variables in the same parameter value combination. Based on the second steel spring dynamic stress corresponding to the multiple parameter value combinations, at least one parameter value combination that minimizes the corresponding second steel spring dynamic stress is screened out, and the parameter values ​​in the combination are used as the target parameter value. Thus, by configuring the structural parameter variables of the steel spring using the target parameter values, the vibration acceleration and dynamic stress of the steel spring under wheel-rail excitation can be effectively reduced.

[0089] In the above-mentioned method for optimizing the structural parameters of the steel spring of a heavy-load locomotive, a rigid-flexible coupling dynamics model of the heavy-load locomotive is established based on the rigid-flexible coupling dynamics theory; the rigid-flexible coupling dynamics model of the heavy-load locomotive is obtained by coupling the rigid body dynamics model of the heavy-load locomotive and the steel spring finite element model of the steel spring of the heavy-load locomotive; the measured wheel-rail excitation data of the heavy-load locomotive is input into the rigid-flexible coupling dynamics model of the heavy-load locomotive to obtain a plurality of first steel spring dynamic stresses corresponding to each structural parameter variable of the steel spring; the plurality of first steel spring dynamic stresses corresponding to each structural parameter variable are calculated when the structural parameter variable adopts multiple parameter values; the target structural parameter value of the steel spring is determined according to the changes in the plurality of first steel spring dynamic stresses corresponding to each structural parameter variable; wherein, when the steel spring adopts the target structural parameter value, the steel spring dynamic stress corresponding to the steel spring meets the preset dynamic stress minimization condition.

[0090] In this way, based on the rigid-flexible coupling dynamics theory, the present application couples the rigid body dynamics model of a heavy-load locomotive and the steel spring finite element model of the steel spring of the heavy-load locomotive, fully considering the flexible deformation characteristics and structural parameters of the steel spring, and can more accurately establish a rigid-flexible coupling dynamics model of the heavy-load locomotive. Through the rigid-flexible coupling dynamics model of the heavy-load locomotive, based on the measured wheel-rail excitation data of the heavy-load locomotive, multiple first steel spring dynamic stresses corresponding to each structural parameter variable of the steel spring are obtained. Therefore, the target structural parameter value of the steel spring can be determined according to the change of the multiple first steel spring dynamic stresses corresponding to each structural parameter variable. The target structural parameter value is the parameter value when the steel spring dynamic stress corresponding to the steel spring meets the preset dynamic stress minimization condition. Therefore, the use of the target structural parameter value can effectively reduce the vibration acceleration and dynamic stress of the steel spring under wheel-rail excitation, reduce the occurrence of steel spring failure and fracture, and effectively improve the reliability of the steel spring.

[0091] In another embodiment, Figure 8 As shown, a method for optimizing the structural parameters of a steel spring for a heavy-duty locomotive is provided. The method is described by taking the application of the method to a computer device as an example, and includes the following steps:

[0092] S1, based on the multi-rigid body dynamics theory and the actual structural parameters of the heavy-duty locomotive, a rigid body dynamics model is established;

[0093] S2, drawing a three-dimensional model of the steel spring according to the structural parameters of the steel spring, establishing a finite element model of the steel spring, and performing modal analysis;

[0094] S3, based on the rigid-flexible coupling dynamics theory and combined with the modal analysis results, the rigid body dynamics model is coupled with the steel spring finite element model to obtain the rigid-flexible coupling dynamics model of the heavy-duty locomotive;

[0095] S4, inputting the measured wheel-rail excitation data into the rigid-flexible coupling dynamic model of the heavy-load locomotive to calculate the dynamic stress of the steel spring;

[0096] S5, analyze the optimization effect of a single structural parameter variable of the steel spring. The structural parameter variables include the spring wire diameter, spring center diameter, free height, and number of end coils. The evaluation index is the change in the dynamic stress of the steel spring.

[0097] S6, repeating steps S2-S5 until the parameter values ​​of the various structural parameter variables of the steel spring are close to the geometric position limit or the strength of the steel spring is close to the limit;

[0098] S7, select the structural parameter variables that have the greatest impact on acceleration and dynamic stress as the target structural parameter variables, and perform multi-parameter value combination optimization analysis;

[0099] S8, repeating steps S2-S4 until the parameter value of the target structural parameter variable of the steel spring approaches the geometric position limit or the strength of the steel spring approaches the limit;

[0100] S9, among multiple sets of parameter value combinations of target structural parameter variables, select the parameter value combination with the minimum dynamic stress, use the parameter value as the final optimized target parameter value, and put it into practical application.

[0101] It should be noted that the specific definitions of the above steps can refer to the specific definitions of the steel spring structural parameter optimization method for a heavy-load locomotive above.

[0102] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0103] Based on the same inventive concept, embodiments of the present application also provide a device for optimizing the structural parameters of steel springs for heavy-haul locomotives, which is used to implement the aforementioned method for optimizing the structural parameters of steel springs for heavy-haul locomotives. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the device for optimizing the structural parameters of steel springs for heavy-haul locomotives provided below can be found in the aforementioned definition of the method for optimizing the structural parameters of steel springs for heavy-haul locomotives, and will not be further elaborated here.

[0104] In an exemplary embodiment, Figure 9 As shown, a device for optimizing the structural parameters of a steel spring of a heavy-duty locomotive is provided, comprising: an establishment module 910, an acquisition module 920, and a determination module 930, wherein:

[0105] Establishing module 910, for establishing a rigid-flexible coupling dynamics model of a heavy-load locomotive based on the rigid-flexible coupling dynamics theory; the rigid-flexible coupling dynamics model of the heavy-load locomotive is obtained by coupling the rigid body dynamics model of the heavy-load locomotive and the steel spring finite element model of the steel spring of the heavy-load locomotive.

[0106] An acquisition module 920 is configured to input the measured wheel-rail excitation data of the heavy-load locomotive into the rigid-flexible coupling dynamic model of the heavy-load locomotive to obtain a plurality of first steel spring dynamic stresses corresponding to each structural parameter variable of the steel spring; the plurality of first steel spring dynamic stresses corresponding to each structural parameter variable are calculated when the structural parameter variable adopts multiple parameter values.

[0107] A determination module 930 is configured to determine a target structural parameter value for the steel spring based on changes in the dynamic stresses of the plurality of first steel springs corresponding to the respective structural parameter variables; wherein, when the steel spring adopts the target structural parameter value, the corresponding dynamic stress of the steel spring satisfies a preset dynamic stress minimization condition.

[0108] In one embodiment, the determination module 930 is specifically used to screen out at least one target structural parameter variable from each of the structural parameter variables; the degree of influence of the target structural parameter variable on the change of the dynamic stress of the corresponding first steel spring is greater than the degree of influence of the unscreened structural parameter variables on the change of the dynamic stress of the corresponding first steel spring; obtain the target parameter value corresponding to each of the target structural parameter variables as the target structural parameter value of the steel spring; wherein, when each of the target structural parameter variables adopts the corresponding target parameter value, the dynamic stress of the steel spring corresponding to the steel spring satisfies the dynamic stress minimization condition.

[0109] In one embodiment, the determination module 930 is specifically configured to obtain, based on the multiple first steel spring dynamic stresses corresponding to the structural parameter variables, a change in the first steel spring dynamic stress corresponding to each of the structural parameter variables as a dynamic stress change; and, based on the dynamic stress change corresponding to each of the structural parameter variables, screen out at least one structural parameter whose corresponding dynamic stress change satisfies a preset change condition as the target structural parameter variable.

[0110] In one embodiment, the determination module 930 is specifically used to obtain the dynamic stress of the second steel spring corresponding to each of multiple groups of parameter value combinations of the steel spring; wherein each group of parameter value combinations includes parameter values ​​corresponding to each of the target structural parameter variables; determine at least one group of parameter value combinations with the minimum corresponding second steel spring dynamic stress, and use the parameter values ​​in the at least one group of parameter value combinations as the target parameter values.

[0111] In one embodiment, the establishment module 910 is specifically used to establish a rigid body dynamics model of the heavy-load locomotive based on the multi-rigid body dynamics theory and the actual structural parameters of the heavy-load locomotive; establish the steel spring finite element model based on the structural parameters of the steel spring; the structural parameters include the structural parameter variables and the corresponding parameter values; based on the rigid-flexible coupling dynamics theory, the rigid body dynamics model and the steel spring finite element model are coupled to obtain the rigid-flexible coupling dynamics model of the heavy-load locomotive.

[0112] In one embodiment, the establishment module 910 is specifically used to perform modal analysis on the steel spring finite element model to obtain modal analysis results; based on the rigid-flexible coupling dynamics theory, combined with the modal analysis results, the rigid body dynamics model and the steel spring finite element model are coupled to obtain the rigid-flexible coupling dynamics model of the heavy-duty locomotive.

[0113] Each module in the aforementioned device for optimizing the structural parameters of a steel spring for a heavy-duty locomotive can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor within a computer device as hardware, or stored in a computer device memory as software, allowing the processor to call and execute the corresponding operations of each module.

[0114] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 10As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless means, and the wireless means can be implemented via Wi-Fi, mobile cellular networks, near-field communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for optimizing the structural parameters of steel springs for heavy-duty locomotives. The display unit of the computer device is used to produce visual images and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0115] Those skilled in the art will understand that Figure 10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0116] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0117] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0118] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0119] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0120] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. 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-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0121] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0122] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for optimizing the structural parameters of a steel spring for a heavy-duty locomotive, characterized in that: The method comprises: Based on the rigid-flexible coupling dynamics theory, a rigid-flexible coupling dynamics model of a heavy-load locomotive is established; the rigid-flexible coupling dynamics model of the heavy-load locomotive is obtained by coupling a rigid body dynamics model of the heavy-load locomotive and a steel spring finite element model of a steel spring of the heavy-load locomotive; Inputting the measured wheel-rail excitation data of the heavy-load locomotive into the rigid-flexible coupling dynamic model of the heavy-load locomotive to obtain a plurality of first steel spring dynamic stresses corresponding to each structural parameter variable of the steel spring; the plurality of first steel spring dynamic stresses corresponding to each structural parameter variable are calculated when the structural parameter variable adopts multiple parameter values; The target structural parameter value of the steel spring is determined based on changes in the dynamic stresses of multiple first steel springs corresponding to each of the structural parameter variables; wherein, when the steel spring adopts the target structural parameter value, the dynamic stress of the steel spring corresponding to the steel spring satisfies a preset dynamic stress minimization condition.

2. The method according to claim 1, characterized in that Determining the target structural parameter value of the steel spring according to changes in the dynamic stresses of the plurality of first steel springs corresponding to the structural parameter variables includes: At least one target structural parameter variable is selected from each of the structural parameter variables; the target structural parameter variable has a greater influence on the change of the dynamic stress of the corresponding first steel spring than the unselected structural parameter variables have on the change of the dynamic stress of the corresponding first steel spring; Obtain target parameter values ​​corresponding to the target structural parameter variables as target structural parameter values ​​of the steel spring; wherein, when the target structural parameter variables adopt corresponding target parameter values, the dynamic stress of the steel spring corresponding to the steel spring satisfies the dynamic stress minimization condition.

3. The method according to claim 2, characterized in that The step of selecting at least one target structural parameter variable from each of the structural parameter variables comprises: According to the plurality of first steel spring dynamic stresses corresponding to the respective structural parameter variables, obtaining a change in the first steel spring dynamic stress corresponding to each of the structural parameter variables as a dynamic stress change; According to the dynamic stress variation corresponding to each of the structural parameter variables, at least one structural parameter whose corresponding dynamic stress variation meets a preset variation condition is screened out as the target structural parameter variable.

4. The method according to claim 2, characterized in that The obtaining of target parameter values ​​corresponding to the target structural parameter variables includes: Obtaining the second steel spring dynamic stress corresponding to each of a plurality of parameter value combinations of the steel spring; wherein each parameter value combination includes a parameter value corresponding to each of the target structural parameter variables; At least one set of parameter value combinations that minimize the dynamic stress of the corresponding second steel spring is determined, and the parameter values ​​in the at least one set of parameter value combinations are used as the target parameter values.

5. The method according to claim 1, characterized in that The rigid-flexible coupling dynamics model of a heavy-duty locomotive is established based on the rigid-flexible coupling dynamics theory, including: Based on the multi-rigid body dynamics theory and the actual structural parameters of the heavy-haul locomotive, a rigid body dynamics model of the heavy-haul locomotive is established; Establishing a finite element model of the steel spring according to the structural parameters of the steel spring; the structural parameters include the structural parameter variables and corresponding parameter values; Based on the rigid-flexible coupling dynamics theory, the rigid body dynamics model and the steel spring finite element model are coupled to obtain the rigid-flexible coupling dynamics model of the heavy-duty locomotive.

6. The method according to claim 5, characterized in that The rigid-flexible coupling dynamics model of the heavy-load locomotive is obtained by coupling the rigid body dynamics model and the steel spring finite element model based on the rigid-flexible coupling dynamics theory, including: Performing modal analysis on the steel spring finite element model to obtain modal analysis results; Based on the rigid-flexible coupling dynamics theory and in combination with the modal analysis results, the rigid body dynamics model and the steel spring finite element model are coupled to obtain the rigid-flexible coupling dynamics model of the heavy-duty locomotive.

7. A device for optimizing the structural parameters of a steel spring for a heavy-duty locomotive, characterized in that: The device comprises: Establishing a module for establishing a rigid-flexible coupling dynamics model of a heavy-load locomotive based on the rigid-flexible coupling dynamics theory; the rigid-flexible coupling dynamics model of the heavy-load locomotive is obtained by coupling a rigid body dynamics model of the heavy-load locomotive and a steel spring finite element model of a steel spring of the heavy-load locomotive; an acquisition module, configured to input the measured wheel-rail excitation data of the heavy-load locomotive into the rigid-flexible coupling dynamic model of the heavy-load locomotive to obtain a plurality of first steel spring dynamic stresses corresponding to each structural parameter variable of the steel spring; the plurality of first steel spring dynamic stresses corresponding to each structural parameter variable are calculated when the structural parameter variable adopts multiple parameter values; A determination module is used to determine a target structural parameter value of the steel spring based on changes in the dynamic stresses of multiple first steel springs corresponding to each of the structural parameter variables; wherein, when the steel spring adopts the target structural parameter value, the dynamic stress of the steel spring corresponding to the steel spring satisfies a preset dynamic stress minimization condition.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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