Method and equipment for optimizing transverse stability of heavy-load locomotive
By optimizing the stiffness and damping parameters of the suspension system of heavy-duty locomotives through DOE tests and simulation models, the problem of cumbersome and time-consuming suspension device design was solved, and the efficient optimization and stability improvement of the suspension system were achieved.
Patent Information
- Application Number
- CN202511359496.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-23
- Publication Date
- 2026-01-20
AI Technical Summary
Existing technologies suffer from cumbersome and time-consuming parameter optimization during the suspension design phase, which relies heavily on experience-based judgment and leads to poor matching of suspension system stiffness and damping parameters.
The lateral stability of heavy-haul locomotives was optimized using DOE tests and simulation models. By generating a dynamic model, conducting DOE tests, and simulating the lateral acceleration of the frame, the stiffness and damping parameters of the secondary lateral stops were optimized.
Within the range of values for the lateral stop stiffness and damping of the second-order suspension, the lateral acceleration of the frame is optimized, saving R&D time, improving work efficiency, and enhancing the lateral stability of the suspension system.
Smart Images
Figure CN121365500A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The embodiment of the present application relates to the technical field of rail transit, in particular to the field of designing locomotive test technology in rail transit, and particularly relates to a heavy haul locomotive lateral stability optimization method, device, equipment, medium and product program. BACKGROUND
[0002] At present, in the design stage of the suspension device, the parameter test method is usually used to optimize the parameters of the suspension device, which leads to complicated design work and consumes a large amount of working time. Meanwhile, in the aspect of analyzing the simulation calculation results, the designer usually judges the change trend by experience and adjusts the stiffness and damping parameters of the suspension system. Due to the difference in the experience level of each designer, the subjective judgment is easy to cause deviation, and sometimes it may take more time to find the appropriate matching parameters, and the final effect is not necessarily the best. SUMMARY
[0003] The purpose of the present application is to at least provide a heavy haul locomotive lateral stability optimization method and equipment to overcome the above technical problems in the prior art, and to optimize the calculation of the objective function in the value range of the design variable, thereby saving the research and development time and improving the work efficiency.
[0004] To solve the above technical problems, at least one embodiment of the present application provides a heavy haul locomotive lateral stability optimization method, comprising:
[0005] generating a dynamic model of the heavy haul locomotive according to the dynamic characteristic parameters of the heavy haul locomotive;
[0006] performing a DOE test on the stiffness and damping of the secondary lateral stop of the heavy haul locomotive and the lateral acceleration of the frame of the heavy haul locomotive; wherein the stiffness and damping of the secondary lateral stop are variables of the DOE test, and the lateral acceleration of the frame is an objective function;
[0007] generating a simulation model of the lateral acceleration of the frame according to the results of the DOE test;
[0008] optimizing the lateral stability of the heavy haul locomotive according to the dynamic model and the simulation model.
[0009] In some embodiments, optimizing the lateral stability of the heavy haul locomotive according to the dynamic model and the simulation model comprises:
[0010] optimizing the lateral acceleration of the frame according to the simulation model and the predetermined constraint condition of the lateral acceleration of the frame;
[0011] optimizing the lateral stability of the heavy haul locomotive according to the optimization results of the lateral acceleration of the frame and the dynamic model.
[0012] In some embodiments, the optimization of the lateral stability of the heavy haul locomotive according to the optimization result of the frame lateral acceleration and the dynamic model comprises:
[0013] determining the optimal secondary lateral stop stiffness and the optimal damping according to the optimization result of the frame lateral acceleration;
[0014] optimizing the lateral stability of the heavy haul locomotive according to the optimal secondary lateral stop stiffness, the optimal damping and the dynamic model.
[0015] In some embodiments, the optimization of the lateral stability of the heavy haul locomotive according to the optimal secondary lateral stop stiffness, the optimal damping and the dynamic model comprises:
[0016] inputting the optimal secondary lateral stop stiffness and the optimal damping into the dynamic model, and performing simulation verification on the heavy haul locomotive to optimize the lateral stability of the heavy haul locomotive.
[0017] In some embodiments, the simulation verification of the heavy haul locomotive is performed under the condition that the heavy haul locomotive runs at a speed of 90 KM / h on a straight line.
[0018] In some embodiments, the dynamic model is used to determine the simulation condition and the evaluation index of the lateral stability of the heavy haul locomotive.
[0019] In some embodiments, the evaluation index of the lateral stability is the frame lateral vibration acceleration.
[0020] At least one embodiment of the present application further provides a device for optimizing the lateral stability of a heavy haul locomotive, comprising:
[0021] a dynamic model generation module configured to generate a dynamic model of the heavy haul locomotive according to dynamic characteristic parameters of the heavy haul locomotive;
[0022] a DOE test module configured to perform DOE test on the secondary lateral stop stiffness and damping of the heavy haul locomotive and the frame lateral acceleration of the heavy haul locomotive; wherein the secondary lateral stop stiffness and damping are variables of the DOE test, and the frame lateral acceleration is a target function;
[0023] a simulation model generation module configured to generate a simulation model of the frame lateral acceleration according to the result of the DOE test;
[0024] a lateral stability optimization module configured to optimize the lateral stability of the heavy haul locomotive according to the dynamic model and the simulation model.
[0025] At least one embodiment of the present application also provides an electronic device, comprising: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the above-mentioned heavy haul locomotive lateral stability optimization method.
[0026] At least one embodiment of the present application also provides a computer readable storage medium storing a computer program, and the computer program is executed by a processor to implement the above-mentioned heavy haul locomotive lateral stability optimization method.
[0027] The embodiment of the present application provides a heavy haul locomotive lateral stability optimization method and device, and the corresponding method comprises the following steps: firstly, generating a dynamic model of a heavy haul locomotive according to dynamic characteristic parameters of the heavy haul locomotive; then, performing a DOE test on the stiffness and damping of a secondary lateral stop of the heavy haul locomotive and the lateral acceleration of a frame of the heavy haul locomotive; wherein the stiffness and damping of the secondary lateral stop are variables of the DOE test, and the lateral acceleration of the frame is a target function; generating a simulation model of the lateral acceleration of the frame according to the result of the DOE test; and finally, optimizing the lateral stability of the heavy haul locomotive according to the dynamic model and the simulation model.
[0028] The method provided by the present application optimizes the calculation of the target function of the lateral acceleration of the frame in the value range of the stiffness and damping of the secondary lateral stop, thereby saving the development time of the lateral stability of the heavy haul locomotive and improving the work efficiency. BRIEF DESCRIPTION OF DRAWINGS
[0029] One or more embodiments are exemplarily illustrated by pictures in the corresponding drawings, and the exemplarily illustrations do not constitute a limitation on the embodiments.
[0030] Figure 1 is a flowchart of a heavy haul locomotive lateral stability optimization method provided by the embodiment of the present application;
[0031] Figure 2 is a flowchart of step 400 provided by an embodiment of the present application;
[0032] Figure 3 is a flowchart of step 402 provided by an embodiment of the present application;
[0033] Figure 4 is a flowchart of a heavy haul locomotive lateral stability optimization method provided by the specific embodiment of the present application;
[0034] Figure 5 is a flowchart of step S4 provided by the specific embodiment of the present application;
[0035] Figure 6 is a schematic diagram of a heavy haul locomotive lateral stability optimization device provided by an embodiment of the present application;
[0036] Figure 7 is a structural schematic diagram of an electronic device provided by another embodiment of the present application. DETAILED DESCRIPTION
[0037] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the embodiments of the present application will be described in detail below with reference to the drawings. However, it can be understood by those skilled in the art that, in the embodiments of the present application, many technical details are proposed in order to make the readers better understand the present application. However, the technical solutions claimed by the present application can be implemented even if there are no such technical details and various changes and modifications based on the following embodiments. The division of the following embodiments is for the convenience of description, and should not constitute any limitation on the specific implementation of the present application, and the embodiments can be combined and referred to each other without contradiction.
[0038] Hereinafter, the term "include" or "may include" used in various embodiments of the present application indicates the existence of the claimed function, operation or element, and does not limit the addition of one or more functions, operations or elements. In addition, as used in various embodiments of the present application, the terms "include", "have" and their synonyms are only intended to mean the presence of a specific feature, number, step, operation, element, component or combination of the foregoing, and should not be understood as first excluding the presence or addition of one or more other features, numbers, steps, operations, elements, components or combinations of the foregoing.
[0039] In various embodiments of the present application, the expression "or" or "at least one of B or / and C" includes any combination of the listed terms or all combinations thereof. For example, the expression "B or C" or "at least one of B or / and C" can include B, can include C, or can include both B and C.
[0040] The expressions (such as "first", "second", etc.) used in various embodiments of the present application can modify various constituent elements in various embodiments, but can not limit the corresponding constituent elements. For example, the above expressions do not limit the order and / or importance of the elements. The above expressions are only for the purpose of distinguishing one element from other elements. For example, the first user device and the second user device indicate different user devices, although both are user devices. For example, without departing from the scope of various embodiments of the present application, a first element can be referred to as a second element, and likewise, a second element can be referred to as a first element.
[0041] It should be noted that if a first component is described as being "connected" or "coupled" to a second component, the first component can be directly connected or coupled to the second component, and that a third component can be "connected" or "coupled" between the first component and the second component. Conversely, where a first component is described as being "directly connected" or "directly coupled" to a second component, it is understood that there are no third components between the first component and the second component.
[0042] The terminology used in the various embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the various embodiments of the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of the present application belong. The terms, such as "include", "comprise", "have", "contain", "depict", "show", "exhibit", "display", and the like, used herein are used in the sense of "including but not limited to".
[0043] In order to make the objects, technical solutions and advantages of the present application clearer, further detailed description will be made to the present application in combination with embodiments and drawings, the illustrative embodiments of the present application and the description thereof are only used to explain the present application and do not limit the present application.
[0044] Embodiment one:
[0045] It is not difficult to understand that the suspension device is an important part of the vehicle, and its key role is to realize the elastic connection between the vehicle body, the frame and the wheel pair, and to transmit various forces and moments. The design purpose is to reduce the impact and vibration of the vehicle caused by track irregularities, so that the vehicle runs more stably on the line.
[0046] The process of designing the suspension device is usually based on theoretical calculation, and the designer can better simulate and analyze the dynamic performance of the vehicle system under various working conditions. In this design process, multiple adjustments and simulation calculations of suspension parameters are inevitable steps to ensure that the vehicle can run stably under various conditions, and the adjustment process and simulation process of the suspension parameters have the technical pain points of excessive reliance on the subjective experience of the designer and the need for a large amount of working time. Based on this, the method for optimizing the lateral stability of a heavy haul locomotive according to the embodiment can be applied to an electronic device with communication, calculation and data storage capabilities, and the specific process thereof can be as shown in Figure 1 As shown in the figure, it includes:
[0047] Step 100: generating a power model of the heavy haul locomotive according to power characteristic parameters of the heavy haul locomotive;
[0048] Step 200: performing a DOE test on the stiffness and damping of the secondary lateral stop of the heavy haul locomotive and the lateral acceleration of the frame of the heavy haul locomotive; wherein the stiffness and damping of the secondary lateral stop are variables of the DOE test, and the lateral acceleration of the frame is a target function;
[0049] Step 300: generating a simulation model of the lateral acceleration of the frame according to the results of the DOE test;
[0050] Step 400: optimizing the lateral stability of the heavy haul locomotive according to the power model and the simulation model.
[0051] The embodiment of the present application provides a heavy haul locomotive lateral stability optimization method, which comprises the following steps: firstly, generating a power model of the heavy haul locomotive according to power characteristic parameters of the heavy haul locomotive; then, performing a DOE test on the stiffness and damping of the secondary lateral stop of the heavy haul locomotive and the lateral acceleration of the frame of the heavy haul locomotive; wherein the stiffness and damping of the secondary lateral stop are variables of the DOE test, and the lateral acceleration of the frame is a target function; generating a simulation model of the lateral acceleration of the frame according to the results of the DOE test; and finally, optimizing the lateral stability of the heavy haul locomotive according to the power model and the simulation model.
[0052] The method provided by the present application optimizes the target function of the lateral acceleration of the frame in the value range of the stiffness and damping of the secondary lateral stop, thereby saving the development time of the lateral stability of the heavy haul locomotive and improving the work efficiency.
[0053] For step 100, firstly, each rigid or flexible component of the heavy haul locomotive should be defined, such as the car body, the bogie, the wheelset, etc. Then the connection mode between these components should be defined, such as the spring and damping elements of the suspension system, and possibly shock absorbers, etc. Then the wheel-rail contact model needs to be defined, which may require input of the geometric parameters and contact mechanics parameters of the wheel and rail, such as the friction coefficient, the creep coefficient, etc. In addition, the track irregularity input also needs to be set to better evaluate the lateral stability of the vehicle. The lateral running stability generally refers to the anti-snaking motion capability of the vehicle during high-speed running, and the stability during curve passing. Different speed levels, different track conditions (such as straight lines, curves, curves with different radii), and different load conditions need to be considered. For example, empty and full loads may affect the dynamics response of the vehicle.
[0054] Then the dynamic evaluation index is defined. Commonly used indexes for lateral stability include lateral vibration acceleration of the vehicle body and bogie, lateral displacement of the wheelset, derailment coefficient, wheel load reduction rate, etc. Preferably, for hunting motion stability, the critical speed is used for evaluation, i.e. the speed threshold at which the vehicle starts to have unstable hunting motion. In addition, the smoothness index of the vehicle, such as the Sperling index, etc. also needs to be considered.
[0055] As for the data processing method, after the simulation is completed, the time domain and frequency domain data need to be extracted for analysis. For example, the time domain waveform of the lateral acceleration can be observed to see if the vibration amplitude is within the allowed range, and the frequency domain analysis can identify the main vibration frequency components. For the determination of the critical speed, it may be necessary to gradually increase the speed and observe whether the system response diverges (such as the amplitude continuously increases), or to solve the stability of the system through eigenvalue analysis. The derailment coefficient and the wheel load reduction rate are usually obtained by calculating the wheel-rail force, and it is necessary to judge whether it exceeds the safety limit according to the relevant standards (such as the UIC standard).
[0056] It can be understood that the DOE test in step 200 refers to the Design of Experiments (DOE) method, which is a systematic method for planning, conducting, analyzing and interpreting controlled experiments to evaluate which factors (variables) have a significant impact on the output of a process or product. Systematically analyze the effects of multiple factors on the response and find the optimal parameter combination. After completing the model establishment and preliminary simulation, further optimize the suspension parameters such as spring stiffness and damping coefficient to improve the lateral stability.
[0057] Preferably, the DOE test design method is the optimal Latin hypercube sampling. This method is used to control two potential confounding variables. In the Latin square design, experimental units are arranged in a square matrix, with each row and each column representing a control variable, and each treatment appearing only once in each row and column.
[0058] For step 300, specifically, based on the relationship between the sample points and the response values obtained in step 200, a simulation model (approximate model) of the bogie lateral acceleration is established, and the constraint conditions of the optimization target are determined, and an optimization algorithm is used for optimization calculation;
[0059] Embodiment two:
[0060] In some embodiments, referring to Figure 2 , step 400 includes:
[0061] Step 401: optimizing the bogie lateral acceleration according to the simulation model and the pre-determined constraint conditions of the bogie lateral acceleration;
[0062] Step 402: optimizing the lateral stability of the heavy haul locomotive according to the optimization result of the bogie lateral acceleration and the dynamic model.
[0063] In some embodiments, referring to Figure 3 , step 402 comprises:
[0064] Step 4021: determining the optimal secondary lateral stop stiffness and the optimal damping according to the optimization result of the bogie lateral acceleration.
[0065] The secondary suspension system, that is, the suspension part between the bogie and the car body, is mainly used to limit the lateral displacement of the car body relative to the bogie to prevent excessive movement from affecting the operation safety. The stiffness should refer to the resistance of the stop when subjected to lateral force, that is, the force required to produce unit displacement.
[0066] It should be pointed out that the stiffness and damping of the secondary lateral stop need to be balanced between limiting displacement and suppressing vibration, and work cooperatively with the whole vehicle suspension system
[0067] Step 4022: optimizing the lateral stability of the heavy haul locomotive according to the optimal secondary lateral stop stiffness, the optimal damping and the dynamic model.
[0068] In some embodiments, step 4022 comprises:
[0069] Inputting the optimal secondary lateral stop stiffness and the optimal damping into the dynamic model, and simulating and verifying the heavy haul locomotive to optimize the lateral stability of the heavy haul locomotive.
[0070] Specifically, the optimized secondary lateral stop stiffness and damping are substituted into the original heavy haul locomotive dynamic model for simulation and verification.
[0071] In some embodiments, the simulation and verification working condition of the heavy haul locomotive is that the heavy haul locomotive runs at a speed of 90 KM / h on a straight line.
[0072] In some embodiments, the dynamic model is used to determine the simulation working condition and evaluation index of the lateral stability of the heavy haul locomotive.
[0073] In some embodiments, the evaluation index of the lateral stability is the bogie lateral vibration acceleration.
[0074] The bogie lateral vibration acceleration of the heavy haul locomotive is the acceleration of the locomotive car body (especially the bogie part) in lateral motion, which is an important index for evaluating the stability and running quality of the locomotive.
[0075] Lateral vibration acceleration is influenced by multiple factors, including locomotive speed, track conditions, carbody stiffness, suspension systems, wheel-rail interactions, etc. Specifically:
[0076] Speed: The higher the locomotive speed, the greater the amplitude and acceleration of lateral vibrations typically. At high speeds, lateral disturbances caused by track irregularities, changes in curve radii, etc. have a more significant impact on the carbody.
[0077] Track irregularities: Height differences, width errors, curve radii, ballast stability, and other irregularities in the track can cause lateral vibrations in the locomotive. If the track is irregular, especially at turns or crossings, lateral disturbances can intensify.
[0078] Suspension system characteristics: The suspension system of the locomotive, including secondary and tertiary suspensions, affects the transmission of lateral vibrations. Softer suspension systems can better absorb external disturbances and reduce vibration acceleration; while stiffer suspension systems can transmit vibrations to the carbody.
[0079] Carbody stiffness and weight: Heavy-duty locomotives typically have greater carbody mass and stiffness, which can result in greater vibration acceleration, but the strong stiffness can also improve the stability of the carbody.
[0080] Wheel-rail contact and mechanical properties: The contact force between the wheel and the rail is one of the direct sources of lateral vibrations. Factors such as the geometry of the track, wear conditions, and relative slip between the wheel and the rail can all affect vibrations.
[0081] In some embodiments, a heavy-duty locomotive lateral stability optimization method further includes: building an integrated model for vehicle running stability; specifically, creating a bat pre-processing script file for the dynamic model, creating a bat post-processing script file for the dynamic model; adding a subvar replacement variable file, which can read the specific parameters of the dynamic model as design variables and custom re-write the modeling parameters; adding a bat pre-processing script file to automatically run the background of the dynamic model simulation calculation; adding a bat post-processing script file to automatically run the background of the dynamic model post-processing analysis, obtaining the post-processing calculation results; adding a dat post-processing result file to read the dynamic index data as the target response, facilitating subsequent result analysis.
[0082] The embodiment of the present application provides a heavy haul locomotive lateral stability optimization method, which comprises the following steps: firstly, generating a dynamic model of the heavy haul locomotive according to dynamic characteristic parameters of the heavy haul locomotive; then, performing a DOE test on the stiffness and damping of a secondary lateral stop of the heavy haul locomotive and the lateral acceleration of a frame of the heavy haul locomotive; wherein the stiffness and the damping of the secondary lateral stop are variables of the DOE test, and the lateral acceleration of the frame is a target function; generating a simulation model of the lateral acceleration of the frame according to a result of the DOE test; and finally, optimizing the lateral stability of the heavy haul locomotive according to the dynamic model and the simulation model.
[0083] The method provided by the present application optimizes the target function formed by the lateral acceleration of the frame in the value range of the stiffness and the damping of the secondary lateral stop, thereby saving the development time of the lateral stability of the heavy haul locomotive and improving the work efficiency.
[0084] Embodiment three:
[0085] In order to further illustrate the scheme, the present application further provides a specific embodiment of a heavy haul locomotive lateral stability optimization method, which is described with reference to Figure 4 , and specifically comprises the following contents.
[0086] S1: establishing a dynamic model of the vehicle according to dynamic calculation parameters of the heavy haul locomotive, determining a simulation working condition of the lateral running stability of the vehicle and defining relevant dynamic evaluation indexes and data processing methods.
[0087] S2: constructing an integrated optimization platform of the running stability of the vehicle.
[0088] Firstly, creating script files: creating a bat pre-processing script file of the heavy haul locomotive model, which is used for automatically pre-processing the heavy haul locomotive dynamic model; creating a bat post-processing script file of the heavy haul locomotive model, which is used for automatically post-processing the heavy haul locomotive dynamic model to obtain a post-processing result file; then, establishing an integrated model: adding a subvar replacement variable file and the bat pre-processing script file respectively; adding the bat post-processing script file and the dat post-processing result file respectively. Finally, creating a replacement variable file, which is used for changing the heavy haul locomotive dynamic model.
[0089] The evaluation index of the lateral stability of the vehicle is the lateral vibration acceleration a jy of the bogie frame; the data processing on the time domain curve of the lateral vibration acceleration of the frame can reflect whether the continuous lateral oscillation of the bogie of the vehicle cannot be rapidly attenuated, and when the acceleration peak value reaches or exceeds 8 m / s 2 for more than 6 times continuously, the lateral instability of the bogie of the vehicle is determined; the lateral acceleration measuring point of the frame is located above the axle box.
[0090] Furthermore, the specific method for processing the time-domain curve of the lateral vibration acceleration of the frame is as follows: the time-domain curve of the lateral vibration acceleration of the frame is bandpass filtered from 0.5 to 10 Hz.
[0091] S3 uses the stiffness and damping of the secondary lateral stop as design variables and the lateral acceleration of the frame as the objective function for DOE design.
[0092] The optimal Latin hypercube sampling method was used to generate a sample matrix of stiffness and damping parameters of the two-stage lateral stop. The number of experimental sample points was 10 times that of the design variables. Dynamic simulation calculations were performed on the generated sample matrix to obtain the objective function values under different parameters.
[0093] S4. Based on the relationship between sample points and response values, establish an approximate model of the lateral acceleration of the framework, determine the constraints of the optimization objective, and use optimization algorithms to perform optimization calculations.
[0094] Specifically, see Figure 5 Based on the mapping relationship between sample points and the objective function, an approximate model is built, and five sample points are randomly selected to verify the accuracy of the approximate model of the objective function. If the accuracy of the approximate model is within the acceptable level of the evaluation index, it indicates that the established approximate model is within the allowable error range and can replace the dynamic model for further simulation; otherwise, it indicates that the established approximate model is not within the allowable error range and cannot be used for subsequent simulations. In the second case, it is necessary to continue to increase the number of sample points, reconstruct the approximate model, and perform a reliability analysis of the approximate model until the error is within the acceptable level. In this embodiment, the coefficient of determination R is used. 2 As an evaluation metric for approximate models, R 2 The expression is:
[0095]
[0096] Where yi is the response value of the i-th sample point; is the predicted value for the i-th sample point; ˉyi is the average value of the actual response of the sample points.
[0097] In addition, the established approximate model needs to be solved and optimized. The solver is set to limit the lateral vibration acceleration of the frame to 8 m / s². 2 The optimization objective is set to find its minimum value.
[0098] Preferably, the approximate model for fitting the nonlinear relationship between the design variables and the objective function is one or more of the following: radial basis function (RBF) neural network model, response surface model, and Griggs model. The optimization algorithm is one of NLPQL or MISQP optimization algorithms.
[0099] Step S5: the stiffness and damping of the optimized secondary lateral stop are substituted into the original heavy haul locomotive dynamics model for simulation verification.
[0100] The specific embodiment of the present application provides a heavy haul locomotive lateral stability optimization method, which comprises the following steps: first, generating a dynamic model of a heavy haul locomotive according to dynamic characteristic parameters of the heavy haul locomotive; then, performing DOE test on the stiffness and damping of a secondary lateral stop of the heavy haul locomotive and the lateral acceleration of the frame of the heavy haul locomotive; wherein the stiffness and damping of the secondary lateral stop are variables of the DOE test, and the lateral acceleration of the frame is a target function; generating a simulation model of the lateral acceleration of the frame according to the results of the DOE test; and finally, optimizing the lateral stability of the heavy haul locomotive according to the dynamic model and the simulation model.
[0101] The method provided by the present application optimizes the target function of the lateral acceleration of the frame in the value range of the stiffness and damping of the secondary lateral stop, thereby saving the development time of the lateral stability of the heavy haul locomotive and improving the work efficiency.
[0102] Embodiment four:
[0103] Another embodiment of the present application relates to a heavy haul locomotive lateral stability optimization device, and the implementation details of a heavy haul locomotive lateral stability optimization device in this embodiment will be specifically described as follows: the following content is only provided for the implementation details for the convenience of understanding, and is not necessary for implementing this solution. The schematic diagram of the heavy haul locomotive lateral stability optimization device in this embodiment can be as shown in Figure 6 The power model generation module 801, the DOE test module 802, the simulation model generation module 803 and the lateral stability optimization module 804.
[0104] The power model generation module 801 is configured to generate a dynamic model of a heavy haul locomotive according to dynamic characteristic parameters of the heavy haul locomotive;
[0105] The DOE test module 802 is configured to perform DOE test on the stiffness and damping of a secondary lateral stop of the heavy haul locomotive and the lateral acceleration of the frame of the heavy haul locomotive; wherein the stiffness and damping of the secondary lateral stop are variables of the DOE test, and the lateral acceleration of the frame is a target function;
[0106] The simulation model generation module 803 is configured to generate a simulation model of the lateral acceleration of the frame according to the results of the DOE test;
[0107] The lateral stability optimization module 804 is configured to optimize the lateral stability of the heavy haul locomotive according to the dynamic model and the simulation model.
[0108] In some embodiments, the lateral stability optimization module 804 comprises:
[0109] a lateral acceleration optimization unit configured to optimize the lateral acceleration of the bogie according to the simulation model and a predetermined constraint condition of the lateral acceleration of the bogie;
[0110] a lateral stability optimization unit configured to optimize the lateral stability of the heavy haul locomotive according to the optimization result of the lateral acceleration of the bogie and the dynamic model.
[0111] In some embodiments, the lateral stability optimization unit comprises:
[0112] an optimal stiffness determination unit configured to determine an optimal stiffness of the secondary lateral stop and an optimal damping according to the optimization result of the lateral acceleration of the bogie;
[0113] a lateral stability optimization sub-unit configured to optimize the lateral stability of the heavy haul locomotive according to the optimal stiffness of the secondary lateral stop, the optimal damping and the dynamic model.
[0114] In some embodiments, the lateral stability optimization sub-unit comprises:
[0115] a parameter input unit configured to input the optimal stiffness of the secondary lateral stop and the optimal damping into the dynamic model, and to simulate and verify the heavy haul locomotive to optimize the lateral stability of the heavy haul locomotive.
[0116] In some embodiments, the simulation and verification of the heavy haul locomotive is performed under a condition that the heavy haul locomotive runs at a speed of 90 KM / h on a straight line.
[0117] In some embodiments, the dynamic model is used to determine a simulation condition and an evaluation index of the lateral stability of the heavy haul locomotive.
[0118] In some embodiments, the evaluation index of the lateral stability is a lateral vibration acceleration of the bogie.
[0119] As can be seen from the above description, the embodiment of the present application provides a heavy haul locomotive lateral stability optimization device, which comprises: a dynamic model generation module configured to generate a dynamic model of a heavy haul locomotive according to dynamic characteristic parameters of the heavy haul locomotive; a DOE test module configured to perform a DOE test on a stiffness and a damping of a secondary lateral stop of the heavy haul locomotive and a lateral acceleration of a bogie of the heavy haul locomotive; wherein the stiffness and the damping of the secondary lateral stop are variables of the DOE test, and the lateral acceleration of the bogie is a target function; a simulation model generation module configured to generate a simulation model of the lateral acceleration of the bogie according to a result of the DOE test; and a lateral stability optimization module configured to optimize the lateral stability of the heavy haul locomotive according to the dynamic model and the simulation model.
[0120] In conclusion, the application optimizes the target function of the frame lateral acceleration in the rigidity and damping range of the secondary lateral stop, thereby saving the research and development time of the lateral stability of the heavy haul locomotive and improving the work efficiency.
[0121] It is worth mentioning that each module involved in the embodiment is a logical module, and in actual application, one logical unit can be one physical unit, or a part of one physical unit, or realized by combination of multiple physical units.
[0122] Embodiment Five:
[0123] Another embodiment of the application relates to an electronic device, such as Figure 7 As shown in the figure, the electronic device specifically includes the following contents:
[0124] The processor (processor) 1201, the memory (memory) 1202, the communication interface (communications interface) 1203 and the bus 1204;
[0125] The processor 1201, the memory 1202 and the communication interface 1203 can communicate with each other through the bus 1204; the communication interface 1203 is used for realizing information transmission between the server-side device, the user-side device and other related devices;
[0126] The processor 1201 is used for calling the computer program in the memory 1202, and the processor executes the computer program to realize all steps in the heavy haul locomotive lateral stability optimization method in the above-mentioned embodiments, for example, the processor executes the computer program to realize the following steps:
[0127] Generating a dynamic model of the heavy haul locomotive according to the dynamic characteristic parameters of the heavy haul locomotive;
[0128] Performing a DOE test on the rigidity and damping of the secondary lateral stop of the heavy haul locomotive, the frame lateral acceleration of the heavy haul locomotive; wherein the rigidity and damping of the secondary lateral stop are variables of the DOE test, and the frame lateral acceleration is a target function;
[0129] Generating a simulation model of the frame lateral acceleration according to the results of the DOE test;
[0130] Optimizing the lateral stability of the heavy haul locomotive according to the dynamic model and the simulation model.
[0131] In some embodiments, optimizing the lateral stability of the heavy haul locomotive according to the dynamic model and the simulation model comprises:
[0132] optimizing the bogie lateral acceleration according to the simulation model and a predetermined constraint of the bogie lateral acceleration;
[0133] optimizing the lateral stability of the heavy haul locomotive according to the optimization result of the bogie lateral acceleration and the dynamic model.
[0134] In some embodiments, optimizing the lateral stability of the heavy haul locomotive according to the optimization result of the bogie lateral acceleration and the dynamic model comprises:
[0135] determining an optimal secondary lateral stop stiffness and an optimal damping according to the optimization result of the bogie lateral acceleration;
[0136] optimizing the lateral stability of the heavy haul locomotive according to the optimal secondary lateral stop stiffness, the optimal damping and the dynamic model.
[0137] In some embodiments, optimizing the lateral stability of the heavy haul locomotive according to the optimal secondary lateral stop stiffness, the optimal damping and the dynamic model comprises:
[0138] inputting the optimal secondary lateral stop stiffness and the optimal damping into the dynamic model, and simulating and verifying the heavy haul locomotive to optimize the lateral stability of the heavy haul locomotive.
[0139] In some embodiments, the simulation and verification of the heavy haul locomotive is under the condition that the heavy haul locomotive runs at a speed of 90 KM / h on a straight line.
[0140] In some embodiments, the dynamic model is used to determine a simulation condition and an evaluation index of the lateral stability of the heavy haul locomotive.
[0141] In some embodiments, the evaluation index of the lateral stability is a bogie lateral vibration acceleration.
[0142] The memory and the processor are connected in a bus manner, the bus can include any number of interconnected buses and bridges, the bus connects various circuits of one or more processors and memories together. The bus can also connect various other circuits such as peripheral devices, voltage stabilizers and power management circuits together, which are well known in the art, therefore, further description is not made herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements such as multiple receivers and transmitters, which provide units for communicating with various other devices on the transmission medium. The data processed by the processor is transmitted on the wireless medium through the antenna, further, the antenna also receives data and transmits the data to the processor.
[0143] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interface, voltage regulation, power management and other control functions. And the memory can be used to store the data used by the processor in the execution operation.
[0144] Embodiment six:
[0145] Another embodiment of the application relates to a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the steps in the above-mentioned overload locomotive lateral stability optimization method embodiment, and the steps include:
[0146] Generating a dynamic model of the overload locomotive according to dynamic characteristic parameters of the overload locomotive;
[0147] Performing a DOE test on the stiffness, damping of the secondary lateral stop of the overload locomotive and the frame lateral acceleration of the overload locomotive; wherein the stiffness and damping of the secondary lateral stop are variables of the DOE test, and the frame lateral acceleration is a target function;
[0148] Generating a simulation model of the frame lateral acceleration according to the results of the DOE test;
[0149] Optimizing the lateral stability of the overload locomotive according to the dynamic model and the simulation model.
[0150] In some embodiments, optimizing the lateral stability of the overload locomotive according to the dynamic model and the simulation model includes:
[0151] Optimizing the frame lateral acceleration according to the simulation model and a predetermined constraint condition of the frame lateral acceleration;
[0152] Optimizing the lateral stability of the overload locomotive according to the optimization results of the frame lateral acceleration and the dynamic model.
[0153] In some embodiments, the optimizing the lateral stability of the heavy haul locomotive according to the optimization result of the frame lateral acceleration and the dynamic model comprises:
[0154] determining an optimal secondary lateral stop stiffness and an optimal damping according to the optimization result of the frame lateral;
[0155] optimizing the lateral stability of the heavy haul locomotive according to the optimal secondary lateral stop stiffness, the optimal damping and the dynamic model.
[0156] In some embodiments, the optimizing the lateral stability of the heavy haul locomotive according to the optimal secondary lateral stop stiffness, the optimal damping and the dynamic model comprises:
[0157] inputting the optimal secondary lateral stop stiffness and the optimal damping into the dynamic model, and simulating and verifying the heavy haul locomotive to optimize the lateral stability of the heavy haul locomotive.
[0158] In some embodiments, the simulation and verification of the heavy haul locomotive is under the condition that the heavy haul locomotive runs at a speed of 90 KM / h on a straight line.
[0159] In some embodiments, the dynamic model is used to determine the simulation condition and evaluation index of the lateral stability of the heavy haul locomotive.
[0160] In some embodiments, the evaluation index of the lateral stability is frame lateral vibration acceleration.
[0161] Each of the embodiments in the specification is described in a progressive manner, and the same and similar parts of each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments. In particular, for hardware + program type embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.
[0162] The above describes specific embodiments of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different than the order in the embodiments and still achieve the desired result. In addition, the processes depicted in the figures do not necessarily require the particular order shown or sequential order to achieve the desired results. In certain implementations, multitasking and parallel processing can be utilized or can be advantageous.
[0163] Although the present application provides method operational steps as in the embodiments or flowcharts, more or less operational steps can be included based on routine or non-creative labor. The order of steps listed in the embodiments is only one of the many ways of executing the steps, and does not represent the only way of executing the steps. In actual device or client product execution, the method steps can be executed in sequence or in parallel (such as in parallel processor or multi-threaded processing environment) as shown in the embodiments or drawings.
[0164] The present application is described in reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce the functions specified in the flowcharts and / or block diagrams. Figure 1 one flow or multiple flows and / or blocks Figure 1 an apparatus that performs the functions specified in one block or multiple blocks.
[0165] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one flow or multiple flows and / or blocks Figure 1 an apparatus that performs the functions specified in one block or multiple blocks.
[0166] These computer program instructions can also be loaded into a computer or other programmable data processing device, so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide a function for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one flow or multiple flows and / or blocks Figure 1 an apparatus that performs the functions specified in one block or multiple blocks.
[0167] The principles and implementation manners of the present application are described in the specific embodiments, and the above embodiment descriptions are only used to help understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation manners and application ranges will be changed, and the above description should not be understood as a limitation of the present application.
Claims
1. A method for optimizing lateral stability of a heavy haul locomotive, characterized by, The method comprises: generating a dynamic model of the heavy-haul locomotive according to dynamic characteristic parameters of the heavy-haul locomotive; performing a DOE test on the stiffness and damping of a secondary lateral stop of the heavy-haul locomotive and a lateral acceleration of a frame of the heavy-haul locomotive, wherein the stiffness and damping of the secondary lateral stop are variables of the DOE test, and the lateral acceleration of the frame is a target function; generating a simulation model of the lateral acceleration of the frame according to a result of the DOE test; optimizing lateral stability of the heavy-haul locomotive according to the dynamic model and the simulation model.
2. The heavy haul locomotive lateral stability optimization method of claim 1, wherein, The method of optimizing the lateral stability of the heavy-haul locomotive according to the dynamic model and the simulation model comprises: optimizing the lateral acceleration of the frame according to the simulation model and a predetermined constraint condition of the lateral acceleration of the frame; optimizing the lateral stability of the heavy-haul locomotive according to an optimization result of the lateral acceleration of the frame and the dynamic model.
3. The heavy haul locomotive lateral stability optimization method of claim 2, wherein, The method of optimizing the lateral stability of the heavy-haul locomotive according to the optimization result of the lateral acceleration of the frame and the dynamic model comprises: determining optimal stiffness and damping of the secondary lateral stop according to the optimization result of the lateral acceleration of the frame; optimizing the lateral stability of the heavy-haul locomotive according to the optimal stiffness and damping of the secondary lateral stop and the dynamic model.
4. The heavy haul locomotive lateral stability optimization method of claim 3, wherein, The method of optimizing the lateral stability of the heavy-haul locomotive according to the optimal stiffness and damping of the secondary lateral stop and the dynamic model comprises: inputting the optimal stiffness and damping of the secondary lateral stop into the dynamic model, and performing simulation verification on the heavy-haul locomotive to optimize the lateral stability of the heavy-haul locomotive.
5. The heavy haul locomotive lateral stability optimization method of claim 4, wherein, A working condition of the simulation verification on the heavy-haul locomotive is that the heavy-haul locomotive runs at a speed of 90 KM / h on a straight line.
6. The method of optimizing lateral stability of a heavy haul locomotive of any one of claims 1 to 5, wherein, The dynamic model is used to determine a simulation working condition and an evaluation index of the lateral stability of the heavy-haul locomotive.
7. The heavy haul locomotive lateral stability optimization method of claim 6, wherein, The evaluation index of the lateral stability is a lateral vibration acceleration of the frame.
8. A heavy haul locomotive lateral stability optimization device characterized by, The method comprises: a dynamic model generation module configured to generate a dynamic model of the heavy-haul locomotive according to dynamic characteristic parameters of the heavy-haul locomotive; a DOE test module configured to perform a DOE test on the stiffness and damping of a secondary lateral stop of the heavy-haul locomotive and a lateral acceleration of a frame of the heavy-haul locomotive, wherein the stiffness and damping of the secondary lateral stop are variables of the DOE test, and the lateral acceleration of the frame is a target function; a simulation model generation module configured to generate a simulation model of the lateral acceleration of the frame according to a result of the DOE test; a lateral stability optimization module configured to optimize lateral stability of the heavy-haul locomotive according to the dynamic model and the simulation model.
9. An electronic device, comprising: The method comprises: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of optimizing lateral stability of a heavy-haul locomotive according to any one of claims 1 to 7.
10. A computer readable storage medium storing a computer program, characterized in that, The computer program, when executed by a processor, implements the method for optimizing lateral stability of a heavy haul locomotive according to any one of claims 1 to 7.