Simulation design method and device of bridge box lubrication pipeline, electronic equipment and medium

Abstract

The application provides a simulation design method and device of a bridge box lubrication pipeline, electronic equipment and a medium, and relates to the technical field of bridge box lubrication pipeline design. The method comprises the following steps: based on the basic design parameters of a to-be-designed bridge box, a fluid simulation numerical model of the to-be-designed bridge box is constructed; based on the design variable combination of the to-be-designed bridge box, the fluid simulation numerical model is automatically simulated and calculated to obtain comprehensive simulation data, the comprehensive simulation data comprising a temperature output result meeting a preset temperature constraint condition and a design variable combination corresponding to the temperature output result; a proxy model is obtained based on the comprehensive simulation data; a target design variable combination is obtained by simulating and predicting the to-be-designed bridge box through the proxy model, and the target design variable combination meets the preset temperature constraint condition and an oil port diameter constraint condition; and a target lubrication pipeline design scheme of the to-be-designed bridge box is obtained based on the target design variable combination, so as to improve the design efficiency and quality of the bridge box lubrication system.

Description

Technical Field

[0001] This application relates to the field of bridge gearbox lubrication pipeline design technology, and in particular to a simulation design method, device, electronic equipment and medium for bridge gearbox lubrication pipelines. Background Technology

[0002] Axle housings are forced-lubrication gearboxes designed to support the cab or frame of large agricultural machinery, transmit driving force, and bear the weight of the main body. Examples include axle housings for heavy-duty agricultural machinery, large transport vehicles, industrial transmissions, and railway locomotives and rolling stock. Lubrication is crucial for the proper functioning of axle housings. It effectively reduces friction and wear on internal gears and bearings, lowers operating temperature, prevents corrosion, ensures smooth and efficient power transmission, thereby extending the service life of the axle housing and improving the reliability of agricultural machinery.

[0003] With the increasing demands for energy efficiency and reliability in the agricultural machinery industry, the design precision and efficiency of axle box lubrication systems are receiving more and more attention. Currently, the main design method for axle box lubrication pipelines is to use finite element simulation analysis, starting with the structure and then conducting simulation analysis. Furthermore, based on safety and design cycle considerations, over-lubrication design is usually implemented. However, the above method suffers from low accuracy in design results and significant shortcomings in development efficiency and power loss control. Summary of the Invention

[0004] This application provides a simulation design method, apparatus, electronic equipment, and medium for bridge gearbox lubrication pipelines to improve the design efficiency and quality of bridge gearbox lubrication systems.

[0005] In a first aspect, embodiments of this application provide a simulation design method for bridge gearbox lubrication pipelines, including: Based on the basic design parameters of the bridge box to be designed, a fluid simulation numerical model of the bridge box to be designed is constructed. Based on the design variable combination of the bridge box to be designed, the fluid simulation numerical model is automatically simulated to obtain comprehensive simulation data. The comprehensive simulation data includes the temperature output results that meet the preset temperature constraints, and the design variable combination corresponding to the temperature output results. A proxy model was obtained based on comprehensive simulation data; The target design variable combination is obtained by simulating and predicting the bridge box under design using a proxy model. The target design variable combination satisfies the preset temperature constraint and oil port diameter constraint. The target lubrication pipeline design scheme for the bridge box to be designed is obtained based on the combination of target design variables.

[0006] In one possible implementation, the fluid simulation numerical model is automatically simulated based on the combination of design variables of the bridge box to be designed, to obtain comprehensive simulation data, including: Obtain the design variable combination of the bridge box to be designed. The design variable types corresponding to the design variable combination include pipe diameter, slope angle and side tilt angle range; Under preset operating conditions, an automated simulation script is generated based on the fluid simulation numerical model. The parameter optimization platform calls the automated simulation script to traverse the combination of design variables and obtain the temperature output results that meet the preset temperature constraints. The temperature output results include the maximum temperature of the axle housing, the temperature of each gear and the temperature of each bearing. The preset temperature constraints are that the maximum temperature of the axle housing, the temperature of each gear and the temperature of each bearing are all less than their respective temperature thresholds. Comprehensive simulation data is obtained based on the temperature output results and the corresponding design variable combinations.

[0007] In one possible implementation, an automated simulation script is developed based on a fluid simulation numerical model, including: Obtain the simulation process and results of the fluid simulation numerical model; The simulation process and simulation results are compiled into process script files and result script files; An automated simulation script is obtained based on the process script file and the result script file.

[0008] In one possible implementation, the surrogate model is obtained based on comprehensive simulation data, including: A database was established based on comprehensive simulation data; The surrogate model is obtained by training the model based on the database. The surrogate model represents the fast mapping relationship between the combination of design variables and the temperature output result.

[0009] In one possible implementation, the target design variable combination is obtained by simulating and predicting the bridge box to be designed using a surrogate model, including: Obtain the combination of design variables for predicting the bridge box to be designed; The predicted design variables are input into the proxy model to obtain the target temperature output that meets the preset temperature constraints. Select the target design variable combination from the combination of design variables for prediction corresponding to the target temperature output result; the oil port diameter corresponding to the target design variable combination satisfies the oil port diameter constraint condition, which is the minimum value among all oil port diameters of the combination of design variables for prediction corresponding to the target temperature output result.

[0010] In one possible implementation, based on the fundamental design parameters of the bridge box to be designed, a fluid simulation numerical model of the bridge box is constructed, including: A three-dimensional model is constructed based on the basic design parameters of the bridge box to be designed, resulting in a fully parameterized complete bridge box model. Based on preset driving conditions, the complete model of the fully parameterized axle housing is segmented and extracted to obtain the fully parameterized axle housing fluid domain model of the lubricating oil inside the axle housing. Fluid dynamics simulation was performed on the fully parameterized bridge box fluid domain model to obtain the fluid simulation numerical model.

[0011] In one possible implementation, a fluid dynamics simulation is performed on the fully parameterized bridge box fluid domain model to obtain a fluid simulation numerical model, including: Fluid dynamics simulation was performed on the fully parameterized bridge box fluid domain model to obtain the initial simulation model and fluid simulation results. The fluid dynamics simulation included mesh generation, physical model selection, boundary conditions setting, initialization conditions setting, calculation, and post-processing. Obtain a comparison between the fluid simulation results and the actual test data of the bridge box to be designed; Based on the comparison results, the initial simulation model was modified to obtain the fluid simulation numerical model of the bridge box to be designed.

[0012] Secondly, embodiments of this application provide a simulation design device for bridge gearbox lubrication pipelines, comprising: The bridge box model building unit is used to build a fluid simulation numerical model of the bridge box to be designed based on the basic design parameters of the bridge box to be designed. The automated simulation unit is used to perform automated simulation calculations on the fluid simulation numerical model based on the design variable combination of the bridge box to be designed, and obtain comprehensive simulation data. The comprehensive simulation data includes the temperature output results that meet the preset temperature constraints, and the design variable combination corresponding to the temperature output results. The surrogate model construction unit is used to obtain a surrogate model based on comprehensive simulation data. The simulation prediction unit is used to perform simulation prediction on the bridge box to be designed through a surrogate model to obtain the target design variable combination, which satisfies the preset temperature constraint and oil port diameter constraint. The result unit is used to obtain the target lubrication pipeline design scheme of the bridge box to be designed based on the combination of target design variables.

[0013] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor; The memory stores the instructions that the computer executes; The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0014] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0015] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0016] The simulation design method, apparatus, electronic equipment, and medium for bridge gearbox lubrication pipelines provided in this application construct a fluid simulation numerical model of the bridge gearbox based on its basic design parameters. Automated simulation calculations are then performed on the fluid simulation numerical model based on the design variable combinations of the bridge gearbox to obtain comprehensive simulation data. This comprehensive simulation data includes temperature output results that meet preset temperature constraints, and the corresponding design variable combinations. A surrogate model is obtained based on the comprehensive simulation data. The target design variable combination is then obtained through simulation prediction of the bridge gearbox using the surrogate model. This target design variable combination meets preset temperature and port diameter constraints. Finally, a target lubrication pipeline design scheme for the bridge gearbox is obtained based on the target design variable combination. Therefore, this application achieves accurate results, rapid optimization, and minimum power consumption design for bridge gearbox lubrication pipelines while ensuring simulation accuracy. It effectively overcomes the shortcomings of existing designs, such as long design cycles, incomplete operational condition verification, high power loss, and reliance on designer experience, significantly improving the design efficiency and quality of bridge gearbox lubrication systems. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] Figure 1 A flowchart illustrating the simulation design method for the bridge gear lubrication pipeline provided in this application; Figure 2 This is a schematic diagram of a vehicle with an axle box girder climbing uphill. Figure 3 This is a schematic diagram of the side-tilt working condition of a vehicle with an axle box; Figure 4 This is a schematic diagram of the forced lubrication pipeline flow path inside the bridge box; Figure 5 A schematic diagram of the structure of the simulation design device for the bridge box lubrication pipeline provided in this application; Figure 6 A schematic diagram of the structure of the electronic device provided in this application.

[0019] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0020] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0021] The technical solutions of this application will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0022] Figure 1 A flowchart illustrating the simulation design method for the bridge gearbox lubrication pipeline provided in this application is shown below. Figure 1 As shown, the method includes: S101. Based on the basic design parameters of the bridge box to be designed, construct a fluid simulation numerical model of the bridge box to be designed.

[0023] This embodiment constructs a fluid simulation numerical model based on fundamental design parameters, enabling virtual evaluation of lubrication performance before the manufacture of the bridge box physical prototype. This significantly reduces the cost of physical testing and ensures that all subsequent analyses are based on accurate physical field simulations. Compared to the traditional "design first, simulation later" approach, this embodiment prioritizes simulation analysis, providing performance prediction capabilities in the early design stages and avoiding large-scale design rework later on.

[0024] S102. Based on the design variable combination of the bridge box to be designed, perform automated simulation calculation on the fluid simulation numerical model to obtain comprehensive simulation data. The comprehensive simulation data includes the temperature output results that meet the preset temperature constraints, and the design variable combination corresponding to the temperature output results.

[0025] This embodiment uses a combination of design variables as input to drive automated simulation calculations and establishes a mapping relationship between design variables and temperature output results. By traversing multiple sets of design variable combinations, it is possible to achieve system verification of the axle box lubrication performance across the entire operating range, which is far more comprehensive than the traditional method of selecting only a few typical operating conditions based on experience.

[0026] Furthermore, by using automated simulation calculations to output all compliant combinations that meet the temperature constraints, a complete and balanced sample database is provided for subsequent training of the surrogate model. This fundamentally ensures the prediction accuracy and generalization ability of the surrogate model and avoids prediction distortion caused by uneven sample distribution.

[0027] S103. Obtain the surrogate model based on the comprehensive simulation data.

[0028] This embodiment uses all the comprehensive simulation data generated by automated simulation calculations to train a surrogate model, establishing an implicit mapping function relationship between design variables and temperature output. The high response speed of the surrogate model also provides algorithmic support for rapid prediction under all operating conditions.

[0029] S104. The target design variable combination is obtained by simulation prediction of the bridge box to be designed through the surrogate model. The target design variable combination satisfies the preset temperature constraint and oil port diameter constraint.

[0030] After obtaining the surrogate model, this embodiment utilizes the millisecond-level response capability of the surrogate model to achieve rapid full-condition prediction of massive candidate combinations within the entire candidate space. Combined with the preset optimization criterion (oil port diameter constraint), the target design variable combination is selected, so that the final target design variable combination has a full-condition verification basis and a clear basis for optimality.

[0031] S105. Based on the combination of target design variables, the target lubrication pipeline design scheme of the bridge box to be designed is obtained.

[0032] This embodiment uses the specific data of various design variables corresponding to the target design variable combination as the design basis for the target lubrication pipeline design scheme of the bridge box to be designed, which can significantly shorten the overall development cycle from design to implementation.

[0033] The simulation design method for bridge box lubrication pipelines provided in this application constructs a fluid simulation numerical model based on basic design parameters. This allows for accurate virtual evaluation of lubrication performance in the early design stages without a physical prototype, providing a high-fidelity calculation starting point for subsequent optimization. Furthermore, the method uses combinations of design variables to drive automated batch simulation calculations of the fluid simulation numerical model, replacing the repetitive labor of manual modeling, calculation, and result extraction in traditional methods. This significantly shortens the design cycle and achieves comprehensive verification across the entire operating range through a systematic traversal of numerous combinations of design variable values, overcoming the incomplete verification defects caused by relying solely on experience to select a few typical operating conditions in existing technologies. Based on this... The surrogate model is trained using comprehensive simulation data generated by automated simulation calculations, which improves the performance prediction speed of new design variable combinations from hours / times to milliseconds / times, providing algorithmic support with both accuracy and efficiency for rapid optimization across the entire space. Finally, the surrogate model performs near-instantaneous predictions across the entire candidate space, and uses preset temperature constraints and minimum oil port diameter constraints as dual screening criteria to automatically lock in the globally optimal target lubrication pipeline design scheme that meets all lubrication and heat dissipation temperature requirements and the oil stirring power loss requirements. This optimal solution has a full-condition verification basis and objective quantitative evidence, and its engineering rationality and persuasiveness far exceed the results of traditional manual comparison and selection based on a few empirical schemes.

[0034] It is evident that this application effectively overcomes the shortcomings of existing technologies, such as long design cycles, incomplete operational verification, large power losses, and reliance on the experience of designers, and achieves high efficiency, comprehensiveness, and optimization in the design of bridge gearbox lubrication pipelines.

[0035] In an exemplary embodiment of this application, the step of automatically performing simulation calculations on a fluid simulation numerical model based on the combination of design variables of the bridge box to be designed, and obtaining comprehensive simulation data, may specifically include: Obtain the design variable combination of the bridge box to be designed. The design variable types corresponding to the design variable combination include pipe diameter, slope angle and side tilt angle range; Under preset operating conditions, an automated simulation script is generated based on the fluid simulation numerical model. The parameter optimization platform calls the automated simulation script to traverse the combination of design variables and obtain the temperature output results that meet the preset temperature constraints. The temperature output results include the maximum temperature of the axle housing, the temperature of each gear and the temperature of each bearing. The preset temperature constraints are that the maximum temperature of the axle housing, the temperature of each gear and the temperature of each bearing are all less than their respective temperature thresholds. Comprehensive simulation data is obtained based on the temperature output results and the corresponding design variable combinations.

[0036] The parameter optimization platform includes multidisciplinary simulation software and self-developed optimization programs, such as the Isight multidisciplinary simulation platform. The preset temperature constraint condition is that the maximum temperature of the axle housing, the temperature of each gear, and the temperature of each bearing are all lower than their respective temperature thresholds, which can be expressed as the maximum temperature of the axle housing. Temperature of each gear Temperature of each bearing (in , , It is determined by industry standards.

[0037] One method for obtaining the design variable combination is equal sampling, which divides the value range of each variable in the design variable combination into N intervals with equal probability, and then randomly combines them to ensure that each interval of each variable is taken exactly once. In this way, the entire space can be well covered with fewer points.

[0038] In this embodiment, after obtaining multiple sets of design variable combinations, the fluid simulation numerical model is automatically simulated by traversing each design variable combination. In each optimization iteration, Isight's file parser also generates a new set of pressure and speed values, and modifies the automated simulation script accordingly, assigning these new values ​​to the simulation model to simulate the performance under different preset operating conditions.

[0039] The automated simulation calculation of the fluid simulation numerical model can be achieved by using a variable designer. The variable designer updates and optimizes the fluid simulation numerical model based on the combination of design variables on the parameter optimization platform, so that the parameter optimization platform can extract the temperature of each bridge box component and select the temperature output result that meets the preset temperature constraint conditions.

[0040] For example, in one embodiment, the diameter of each pipe is used as an example. Slope angle and the range of roll angles As design variables, update the fluid simulation numerical model; inlet pressure P, speed of each gear... Isight updates to create new operating conditions, enabling changes to the automated simulation scripts.

[0041] Thus, the above embodiments of this application, through automated simulation script writing, multi-dimensional design variable setting, parameter optimization platform traversal and calling, and multi-target temperature constraint screening, significantly reduce labor costs and shorten the simulation cycle, while realizing systematic exploration of the design space and accurate screening of compliant data. This provides comprehensive, balanced, and highly reliable integrated simulation data support for the subsequent surrogate model training, thereby ensuring the engineering effectiveness of subsequent rapid prediction and optimal solution output from the data source.

[0042] In another exemplary embodiment, the step of developing an automated simulation script based on a fluid simulation numerical model may specifically include: Obtain the simulation process and results of the fluid simulation numerical model; The simulation process and simulation results are compiled into process script files and result script files; An automated simulation script is obtained based on the process script file and the result script file.

[0043] In this embodiment, the simulation process and results are compiled into a process script file and a result script file, respectively. Specifically, the mesh generation, physical model selection, boundary condition setting, and initialization condition setting of the fluid simulation numerical model are compiled into a process script file; the maximum temperature of the bridge box body is output by the post-processing of the fluid simulation numerical model. Temperature of each gear inside the bridge housing and the temperature of each bearing The results are compiled into a results script file.

[0044] Thus, the above embodiments of this application, through dual scripting of simulation process and results, realize the automated execution and standardized output of pre-processing and post-processing of Computational Fluid Dynamics (CFD) simulation, providing efficient, stable and reliable technical support for large-scale traversal simulation of parameter optimization platform.

[0045] In an exemplary embodiment of this application, the step of obtaining a surrogate model based on comprehensive simulation data may specifically include: A database was established based on comprehensive simulation data; The surrogate model is obtained by training the model based on the database. The surrogate model represents the fast mapping relationship between the combination of design variables and the temperature output result.

[0046] The comprehensive simulation data includes temperature output results that meet the preset temperature constraints, as well as the design variable combinations corresponding to the temperature output results; the types of design variables corresponding to the design variable combinations include pipe diameter, slope angle, and side tilt angle range.

[0047] This embodiment first establishes a database based on comprehensive simulation data. The database stores the temperature output results and the corresponding design variable combinations, such as the diameter of each pipe. Slope angle and the range of roll angles Design variables, maximum temperature of the bridge enclosure Temperature of each gear inside the bridge housing and the temperature of each bearing The temperature output results are obtained. Then, a surrogate model is trained based on the database to represent the fast mapping relationship between the combination of design variables and the temperature output results.

[0048] Thus, in the above embodiments of this application, the database established ensures the integrity and traceability of the training data, and the surrogate model obtained by training through comprehensive simulation data encapsulates the mapping relationship from design variables to performance response. The collaboration of the two provides core algorithm support with both accuracy and efficiency for rapid prediction under all working conditions and output of the global optimal solution, ultimately enabling the bridge box lubrication pipeline design to achieve an order-of-magnitude improvement in design efficiency while ensuring simulation accuracy.

[0049] In another exemplary embodiment, the step of obtaining the target design variable combination by performing simulation prediction on the bridge box to be designed through the surrogate model after constructing the surrogate model may specifically include: Obtain the combination of design variables for predicting the bridge box to be designed; The predicted design variables are input into the proxy model to obtain the target temperature output that meets the preset temperature constraints. Select the target design variable combination from the combination of design variables for prediction corresponding to the target temperature output result; the oil port diameter corresponding to the target design variable combination satisfies the oil port diameter constraint condition, which is the minimum value among all oil port diameters of the combination of design variables for prediction corresponding to the target temperature output result.

[0050] Among them, the range of design variable combinations for predicting the bridge box to be designed is expanded from the limited sampling points during automated simulation to the theoretical full candidate space. For example, the range of values ​​corresponding to the pipe diameter included in the design variable combination is the entire pipeline diameter range.

[0051] In this embodiment, after obtaining the combination of predictive design variables for the bridge box to be designed, the combination of predictive design variables is input into the surrogate model to obtain the target temperature output result that meets the preset temperature constraint condition. Finally, the target design variable combination is selected from the combination of predictive design variables corresponding to the target temperature output result. The port diameter corresponding to the target design variable combination meets the port diameter constraint condition, which is the minimum value among all port diameters of the combination of predictive design variables corresponding to the target temperature output result (with a constant inlet pressure P, the smaller the pipe diameter, the smaller the flow rate, and the more fuel-efficient).

[0052] Thus, the above embodiments of this application combine the rapid prediction capability of the proxy model with the dual screening criteria of temperature compliance and minimum pipe diameter, enabling the bridge gearbox lubrication pipeline design to achieve full-space automatic optimization. Under the premise of ensuring that the lubrication performance fully meets the standards, the design scheme with the minimum oil stirring power loss is automatically locked, ultimately achieving a simultaneous improvement in design quality and design efficiency.

[0053] In an exemplary embodiment of this application, the step of constructing a fluid simulation numerical model of the bridge box to be designed based on its basic design parameters may specifically include: A three-dimensional model is constructed based on the basic design parameters of the bridge box to be designed, resulting in a fully parameterized complete bridge box model. Based on preset driving conditions, the complete model of the fully parameterized axle housing is segmented and extracted to obtain the fully parameterized axle housing fluid domain model of the lubricating oil inside the axle housing. Fluid dynamics simulation was performed on the fully parameterized bridge box fluid domain model to obtain the fluid simulation numerical model.

[0054] This embodiment first constructs a 3D model based on the basic design parameters of the axle housing to be designed, resulting in a fully parametric axle housing model, including the forced lubrication pipeline flow channel, the internal air flow channel of the axle housing, and the solid models of the internal gears and bearings. The construction of the fully parametric axle housing model can be achieved using 3D software such as UG, SolidWorks, Catia, and Pro-e.

[0055] Subsequently, based on preset driving conditions, the complete, fully parameterized axle housing model was segmented and extracted to obtain a fully parameterized axle housing fluid domain model of the lubricating oil inside the axle housing. This fully parameterized axle housing fluid domain model can account for the influence of gravity on the lubricating oil inside the axle housing under slope and roll conditions. Finally, fluid dynamics simulation was performed on the fully parameterized axle housing fluid domain model to obtain a fluid simulation numerical model, which was then converted into an exportable file, qiaoxiangliutiyu.x_t.

[0056] The establishment of the fully parameterized bridge box complete model and the fully parameterized bridge box fluid domain model constitutes fully parameterized modeling. Fully parameterized modeling uses variables instead of fixed values ​​to define geometric dimensions, achieving an efficient design method where "changing one number automatically updates the entire model." The embodiments provided in this application establish an "expression table" or "parameter table" in 3D software such as UG, associating each key dimension with a variable name. When the parameter optimization platform (Isight) requires evaluation of a new set of parameters, only the values ​​in this parameter table need to be modified, and the entire model and its derived fluid domain model will be automatically regenerated.

[0057] Please see Figure 2 , Figure 3 and Figure 4 , Figure 2 This is a schematic diagram of a vehicle with an axle box girder climbing uphill. Figure 3 This is a schematic diagram of the rollover condition of a vehicle with an axle box. Figure 4 This is a schematic diagram of the forced lubrication pipeline flow path inside the bridge housing. (See diagram below.) Figure 2 and Figure 3 As shown (the solid rectangular component represents the axle), only the slope angle needs to be modified. and roll angle It enables model updates for fully parameterized bridge-box fluid domain models. For example... Figure 4 As shown, only the diameter of each pipe needs to be modified. The parameters allow for the adjustment of the pipeline flow path model in the fully parameterized bridge box complete model.

[0058] Thus, through the above embodiments, this application supports the rapid iterative update of the geometric model in the subsequent optimization process with fully parametric modeling, and ensures the physical authenticity of the simulation scenario under different driving postures with working condition adaptive fluid domain extraction. This ensures that the automated simulation calculation is always based on a geometrically accurate, boundary-realistic, and parameter-adjustable simulation numerical model, providing a reliable simulation data source for the training of the subsequent proxy model, and fundamentally ensuring the engineering credibility of the whole-chain optimization design results.

[0059] In another exemplary embodiment, after constructing a fully parameterized bridge box fluid domain model through fully parameterized modeling, the step of performing fluid dynamics simulation on the fully parameterized bridge box fluid domain model to obtain a fluid simulation numerical model may specifically include: Fluid dynamics simulation was performed on the fully parameterized bridge box fluid domain model to obtain the initial simulation model and fluid simulation results. The fluid dynamics simulation included mesh generation, physical model selection, boundary conditions setting, initialization conditions setting, calculation, and post-processing. Obtain a comparison between the fluid simulation results and the actual test data of the bridge box to be designed; Based on the comparison results, the initial simulation model was modified to obtain the fluid simulation numerical model of the bridge box to be designed.

[0060] In this embodiment, the fully parameterized bridge housing fluid domain model is imported into CFD fluid simulation software (including StarCCM+, Fluent, StarCD, FIDAP, STREAM, Shipflow, XFLow, etc., with Fluent as an example here), where mesh generation, physical model selection, and boundary conditions (inlet pressure P, speed of each gear) are performed. The initialization conditions, calculations, and post-processing are set to form the initial simulation model. The maximum temperature of the bridge housing is then extracted from the fluid simulation results of the initial simulation model. Temperature of each gear inside the bridge housing and the temperature of each bearing .

[0061] The extracted temperatures of each component are then compared and verified with actual experimental data. The initial simulation model is then corrected using the actual experimental data to obtain the fluid simulation numerical model of the bridge box to be designed. The corrections can be made to the physical and numerical parameters in the simulation model that affect the accuracy of heat transfer and flow.

[0062] Thus, through the above embodiments, this application improves the fidelity of the fluid simulation numerical model by comparing and correcting the fluid simulation results with actual experimental data, providing a reliable and engineering-trustworthy simulation data source for subsequent automated simulation calculations and surrogate model training.

[0063] Figure 5 This is a schematic diagram of the simulation design device for the bridge gear lubrication pipeline provided in this application, as shown below. Figure 5 As shown, the simulation design device 50 for the bridge box lubrication pipeline includes: Bridge box model building unit 501 is used to build a fluid simulation numerical model of the bridge box to be designed based on the basic design parameters of the bridge box to be designed. The automated simulation unit 502 is used to perform automated simulation calculations on the fluid simulation numerical model based on the design variable combination of the bridge box to be designed, and obtain comprehensive simulation data. The comprehensive simulation data includes the temperature output results that meet the preset temperature constraints, and the design variable combination corresponding to the temperature output results. The surrogate model construction unit 503 is used to obtain a surrogate model based on comprehensive simulation data; Simulation prediction unit 504 is used to perform simulation prediction on the bridge box to be designed through a surrogate model to obtain the target design variable combination, which satisfies the preset temperature constraint and oil port diameter constraint. Result unit 505 is used to obtain the target lubrication pipeline design scheme of the bridge box to be designed based on the combination of target design variables.

[0064] In one possible implementation, the automated simulation unit 502 is further used to obtain the design variable combination of the bridge box to be designed. The design variable types corresponding to the design variable combination include pipe diameter, slope angle, and side tilt angle range. Under preset operating conditions, an automated simulation script is written based on the fluid simulation numerical model. The automated simulation script is called through the parameter optimization platform to traverse the design variable combination and obtain the temperature output results that meet the preset temperature constraints. The temperature output results include the maximum temperature of the bridge box, the temperature of each gear, and the temperature of each bearing. The preset temperature constraints are that the maximum temperature of the bridge box, the temperature of each gear, and the temperature of each bearing are all less than their respective temperature thresholds. Comprehensive simulation data is obtained based on the temperature output results and the design variable combination corresponding to the temperature output results.

[0065] In one possible implementation, the automated simulation unit 502 is also used to acquire the simulation process and simulation results of the fluid simulation numerical model; to compile the simulation process and simulation results into a process script file and a result script file; and to obtain an automated simulation script based on the process script file and the result script file.

[0066] In one possible implementation, the surrogate model building unit 503 is also used to establish a database based on comprehensive simulation data; and to train a model based on the database to obtain a surrogate model, which represents the fast mapping relationship between the combination of design variables and the temperature output result.

[0067] In one possible implementation, the simulation prediction unit 504 is further configured to obtain the combination of prediction design variables for the bridge box to be designed; input the combination of prediction design variables into the surrogate model to obtain the target temperature output result that satisfies the preset temperature constraint; select the target design variable combination from the combination of prediction design variables corresponding to the target temperature output result; the oil port diameter corresponding to the target design variable combination satisfies the oil port diameter constraint condition, which is the minimum value among all oil port diameters of the combination of prediction design variables corresponding to the target temperature output result.

[0068] In one possible implementation, the axle housing model building unit 501 is also used to build a three-dimensional model based on the basic design parameters of the axle housing to be designed, so as to obtain a fully parameterized axle housing complete model; to segment and extract the fully parameterized axle housing complete model based on preset driving conditions, so as to obtain a fully parameterized axle housing fluid domain model of the lubricating oil inside the axle housing; and to perform fluid dynamics simulation on the fully parameterized axle housing fluid domain model to obtain a fluid simulation numerical model.

[0069] In one possible implementation, the bridge box model building unit 501 is also used to perform fluid dynamics simulation on the fully parameterized bridge box fluid domain model to obtain an initial simulation model and fluid simulation results. The fluid dynamics simulation includes mesh generation, physical model selection, boundary condition setting, initialization condition setting, calculation, and post-processing. The comparison results between the fluid simulation results and the actual experimental data of the bridge box to be designed are obtained. Based on the comparison results, the initial simulation model is corrected to obtain the fluid simulation numerical model of the bridge box to be designed.

[0070] The simulation design device for the bridge box lubrication pipeline provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0071] Figure 6 A schematic diagram of the structure of the electronic device provided in this application. Figure 6 As shown, the electronic device 60 provided in this embodiment includes at least one processor 601 and a memory 602. Optionally, the device 60 further includes a communication component 603. The processor 601, memory 602, and communication component 603 are connected via a bus 604.

[0072] In a specific implementation, at least one processor 601 executes computer execution instructions stored in memory 602, causing at least one processor 601 to perform the above-described method.

[0073] The specific implementation process of processor 601 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0074] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0075] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0076] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0077] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0078] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0079] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0080] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0081] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0082] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0083] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0084] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0085] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0086] Finally, it should be noted that other embodiments of this application will readily conceive of by those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and alterations may be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A simulation design method for bridge box lubrication pipelines, characterized in that, include: Based on the basic design parameters of the bridge box to be designed, a fluid simulation numerical model of the bridge box to be designed is constructed. Based on the design variable combination of the bridge box to be designed, the fluid simulation numerical model is automatically simulated to obtain comprehensive simulation data. The comprehensive simulation data includes temperature output results that meet the preset temperature constraints, and the design variable combination corresponding to the temperature output results. A proxy model is obtained based on the comprehensive simulation data; The target design variable combination is obtained by simulating and predicting the bridge box to be designed using the surrogate model. The target design variable combination satisfies the preset temperature constraint and the oil port diameter constraint. The target lubrication pipeline design scheme for the bridge box to be designed is obtained based on the combination of the target design variables.

2. The method according to claim 1, characterized in that, The automated simulation calculation of the fluid simulation numerical model based on the design variable combination of the bridge box to be designed yields comprehensive simulation data, including: Obtain the design variable combination of the bridge box to be designed. The design variable types corresponding to the design variable combination include pipe diameter, slope angle and side tilt angle range. Under preset operating conditions, an automated simulation script is generated based on the fluid simulation numerical model. The automated simulation script is called through the parameter optimization platform to traverse the combination of design variables and obtain the temperature output results that meet the preset temperature constraints. The temperature output results include the maximum temperature of the axle housing, the temperature of each gear and the temperature of each bearing. The preset temperature constraints are that the maximum temperature of the axle housing, the temperature of each gear and the temperature of each bearing are all less than their respective temperature thresholds. Comprehensive simulation data is obtained based on the temperature output results and the corresponding design variable combinations.

3. The method according to claim 2, characterized in that, The automated simulation script, developed based on the fluid simulation numerical model, includes: Obtain the simulation process and simulation results of the fluid simulation numerical model; The simulation process and the simulation results are compiled into a process script file and a result script file; An automated simulation script is obtained based on the process script file and the result script file.

4. The method according to any one of claims 1 to 3, characterized in that, The process of obtaining the surrogate model based on the comprehensive simulation data includes: A database was established based on the comprehensive simulation data. A surrogate model is obtained by training the model based on the database. The surrogate model represents the fast mapping relationship between the combination of design variables and the temperature output result.

5. The method according to claim 4, characterized in that, The step of obtaining the target design variable combination through simulation prediction of the bridge box to be designed using the surrogate model includes: Obtain the combination of design variables for predicting the bridge box to be designed; The predicted design variables are combined and input into the surrogate model to obtain the target temperature output result that satisfies the preset temperature constraint condition. A target design variable combination is selected from the combination of predictive design variables corresponding to the target temperature output result; the oil port diameter corresponding to the target design variable combination satisfies the oil port diameter constraint condition, which is the minimum value among all oil port diameters of the combination of predictive design variables corresponding to the target temperature output result.

6. The method according to any one of claims 1 to 3, characterized in that, The process of constructing a fluid simulation numerical model of the bridge box to be designed based on its basic design parameters includes: Based on the basic design parameters of the bridge box to be designed, a three-dimensional model is constructed to obtain a fully parameterized complete bridge box model. Based on preset driving conditions, the complete model of the fully parameterized axle housing is segmented and extracted to obtain a fully parameterized axle housing fluid domain model of the lubricating oil inside the axle housing. Fluid dynamics simulation was performed on the fully parameterized bridge box fluid domain model to obtain a fluid simulation numerical model.

7. The method according to claim 6, characterized in that, The process of performing fluid dynamics simulation on the fully parameterized bridge box fluid domain model to obtain a fluid simulation numerical model includes: Fluid dynamics simulation is performed on the fully parameterized bridge box fluid domain model to obtain the initial simulation model and fluid simulation results; the fluid dynamics simulation includes mesh generation, physical model selection, boundary conditions setting, initialization conditions setting, calculation, and post-processing; Obtain a comparison between the fluid simulation results and the actual test data of the bridge box to be designed; Based on the comparison results, the initial simulation model is corrected to obtain the fluid simulation numerical model of the bridge box to be designed.

8. A simulation design device for bridge box lubrication pipelines, characterized in that, include: The bridge box model building unit is used to build a fluid simulation numerical model of the bridge box to be designed based on the basic design parameters of the bridge box to be designed. An automated simulation unit is used to perform automated simulation calculations on the fluid simulation numerical model based on the design variable combination of the bridge box to be designed, and obtain comprehensive simulation data. The comprehensive simulation data includes temperature output results that meet preset temperature constraints, and the design variable combination corresponding to the temperature output results. A proxy model construction unit is used to obtain a proxy model based on the comprehensive simulation data; The simulation prediction unit is used to perform simulation prediction on the bridge box to be designed through the surrogate model to obtain the target design variable combination, which satisfies the preset temperature constraint and oil port diameter constraint. The result unit is used to obtain the target lubrication pipeline design scheme of the bridge box to be designed based on the combination of the target design variables.

9. An electronic device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to implement the simulation design method for the bridge gear lubrication pipeline as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the simulation design method for the bridge lubrication pipeline as described in any one of claims 1 to 7.

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