Method, device and server for optimizing computation efficiency of wheel impact simulation

Abstract

The application provides a wheel impact simulation calculation efficiency optimization method and device and a server, relates to the technical field of simulation application, and comprises the following steps: automatically assembling and processing a wheel model to be processed through a standard test bench component, generating a hammer model according to preset impact standard parameters, and constructing a wheel impact simulation model by using the wheel model and the hammer model; performing explicit dynamics solving processing on the wheel impact simulation model, intelligently performing mass scaling processing on a structure to be scaled in the wheel impact simulation model except the hammer model in the solving process, and performing real-time monitoring on a motion state of the hammer model, so as to automatically terminate an impact simulation process of the hammer model according to a monitoring result and obtain a simulation result; automatically identifying and filtering the simulation result, determining a hammer simulation result corresponding to the hammer model, and removing the hammer simulation result, so as to obtain a visualized cloud diagram corresponding to a wheel structure. The application can significantly improve simulation calculation efficiency.

Description

Technical Field

[0001] This invention relates to the technical field of simulation applications, and in particular to a method, apparatus, and server for optimizing the computational efficiency of wheel impact simulation. Background Technology

[0002] Wheel impact testing is a core method for evaluating the safety of wheel structures. However, traditional physical testing is costly and time-consuming, making it difficult to quickly identify structural weaknesses during the design iteration phase. Currently, related technologies suggest that companies are gradually introducing finite element simulation (FEM) technology to numerically simulate wheel impact conditions in order to shorten the R&D cycle. However, existing solutions require manual completion of more than ten steps for each simulation, and these steps are highly coupled. Once parameters change, a large number of operations need to be restarted, resulting in low simulation modeling efficiency. In addition, the time step of explicit dynamic solution is limited by the local fine mesh of the wheel, resulting in extremely slow overall calculation speed. The total analysis time depends on manual experience: if the time is too short, the impact response will not be fully captured; if the time is too long, a large amount of invalid idling will occur, resulting in low simulation solution efficiency. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a method, apparatus and server for optimizing the computational efficiency of wheel impact simulation, which can significantly improve the simulation computational efficiency.

[0004] In a first aspect, embodiments of the present invention provide a method for optimizing the computational efficiency of wheel impact simulation. The method includes: automatically assembling a wheel model to be processed using preset standard test bench components, and generating an impact hammer model according to preset impact standard parameters, so as to construct a wheel impact simulation model using the assembled wheel model and the impact hammer model; performing explicit dynamic solution processing on the wheel impact simulation model, and during the solution process, performing intelligent mass scaling processing on the structures to be scaled in the wheel impact simulation model other than the impact hammer model, and monitoring the motion state of the impact hammer model in real time, so as to automatically terminate the impact simulation process of the impact hammer model according to the monitoring results and obtain simulation results; automatically identifying and filtering the simulation results to determine the impact hammer simulation results corresponding to the impact hammer model, and removing the impact hammer simulation results to obtain a visual cloud map corresponding to the wheel structure.

[0005] In one embodiment, the step of automatically assembling the wheel model to be processed using a preset standard test bench component includes: calling a standardized test bench model and a preset test bench mounting reference corresponding to the test bench support of the standardized test bench model, wherein the standardized test bench model is a test bench model that has been pre-meshed and encapsulated; determining the target assembly feature based on the user's selection operation of various assembly features of the wheel model, and extracting the geometric center point and axial direction of the target assembly feature to determine the geometric center point and axial direction as the wheel assembly reference; and using the wheel assembly reference and the preset test bench mounting reference, performing coaxial alignment processing on the wheel model and the standardized test bench model to obtain the assembled wheel model.

[0006] In one embodiment, the standardized test bench model includes an equivalent damping element. The method includes: determining the equivalent stiffness coefficient and the equivalent damping coefficient by performing parameter back-calibration processing on a preset standard calibration condition, and configuring the equivalent damping element according to the equivalent stiffness coefficient and the equivalent damping coefficient, so as to use the configured equivalent damping element to simulate the test bench buffering performance of the standardized test bench model in the impact simulation process.

[0007] In one embodiment, the step of generating a hammer model based on preset impact standard parameters includes: determining the target mass and geometric dimensions of the hammer based on the preset impact standard parameters, and determining the volume of the hammer based on the geometric dimensions; obtaining the equivalent density of the hammer by performing density back-calculation on the target mass and the volume of the hammer, and setting the hammer material based on the equivalent density to obtain the hammer model.

[0008] In one embodiment, the step of performing intelligent mass scaling on the structure to be scaled in the wheel impact simulation model, excluding the impact hammer model, includes: when intelligent mass scaling is enabled, identifying the wheel impact simulation model to forcibly exclude the impact hammer model from the mass scaling candidate region, thereby obtaining the structure to be scaled in the wheel impact simulation model excluding the impact hammer model; obtaining the original stable time step of the structure to be scaled at the current density, comparing the original stable time step with the preset target stable time step, and performing intelligent mass scaling on the structure to be scaled based on the comparison result.

[0009] In one embodiment, the step of performing intelligent quality scaling on the structure to be scaled based on the comparison result includes: when the original stable time step is less than the target stable time step, amplifying the equivalent density of the structure to be scaled based on the ratio of the target stable time step to the original stable time step, so that the corrected stable time step of the structure to be scaled is not less than the target stable time step.

[0010] In one embodiment, the step of real-time monitoring of the motion state of the impact hammer model to automatically terminate the impact simulation process of the impact hammer model based on the monitoring results includes: real-time monitoring of the velocity component of the impact hammer model in the impact direction during the explicit dynamic solution process; when the absolute value of the velocity component is less than a preset threshold and the direction of the velocity component is reversed, it is determined that the impact hammer model has completed the impact and rebound, and the impact simulation process of the impact hammer model is automatically terminated.

[0011] Secondly, embodiments of the present invention also provide a computational efficiency optimization device for wheel impact simulation. The device includes: a model building module, which automatically assembles the wheel model to be processed using preset standard test bench components and generates an impact hammer model according to preset impact standard parameters, so as to construct a wheel impact simulation model using the assembled wheel model and the impact hammer model; an intelligent solving module, which performs explicit dynamic solving on the wheel impact simulation model, and performs intelligent mass scaling on the structures to be scaled in the wheel impact simulation model other than the impact hammer model during the solving process, and monitors the motion state of the impact hammer model in real time, so as to automatically terminate the impact simulation process of the impact hammer model according to the monitoring results and obtain the simulation results; and an intelligent post-processing module, which automatically identifies and filters the simulation results, determines the impact hammer simulation results corresponding to the impact hammer model, and removes the impact hammer simulation results to obtain a visual cloud map corresponding to the wheel structure.

[0012] Thirdly, embodiments of the present invention also provide a server, including a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement any of the methods provided in the first aspect.

[0013] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement any of the methods provided in the first aspect.

[0014] The embodiments of the present invention bring the following beneficial effects:

[0015] This invention provides a method, apparatus, and server for optimizing the computational efficiency of wheel impact simulation. The method automatically assembles a wheel model to be processed using preset standard test bench components and generates an impact hammer model based on preset impact standard parameters. Using the assembled wheel model and impact hammer model, a wheel impact simulation model is constructed. Then, explicit dynamics are solved on the wheel impact simulation model. During the solution process, intelligent mass scaling is performed on the structures to be scaled in the wheel impact simulation model, excluding the impact hammer model. The motion state of the impact hammer model is monitored in real time, and the impact simulation process of the impact hammer model is automatically terminated based on the monitoring results to obtain the simulation results. Finally, the simulation results are automatically identified and filtered to determine the impact hammer simulation results corresponding to the impact hammer model, and these results are discarded to obtain a visual cloud map corresponding to the wheel structure. This invention can significantly improve simulation computational efficiency.

[0016] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a method for optimizing computational efficiency in wheel impact simulation provided by an embodiment of the present invention;

[0020] Figure 2 A schematic diagram of a wheel impact simulation provided for an embodiment of the present invention;

[0021] Figure 3 This is a schematic diagram of a computational efficiency optimization device for wheel impact simulation provided in an embodiment of the present invention;

[0022] Figure 4 This is a schematic diagram of the structure of a server provided in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Currently, traditional wheel impact tests struggle to provide timely feedback on structural weaknesses during the design iteration phase, thus hindering the improvement of R&D efficiency. Existing simulation methods generally suffer from low computational efficiency. To meet the computational requirements of nonlinearity, large deformation, and high-frequency dynamic response during the impact process, the model size is usually large, the computation time is long, and the dependence on computational resources is significant, which is not conducive to forming efficient and reusable simulation applications in industrial software.

[0025] Furthermore, existing general-purpose finite element software is mostly designed for general solutions and lacks dedicated process encapsulation and computational optimization mechanisms for the typical industrial scenario of wheel impact. This results in the need for repeated model cleaning, mesh generation, boundary reconstruction, and parameter calibration during repeated simulations, which seriously affects the efficiency of engineering applications. At the same time, the differences in impact height, impact mass, and fixing methods under different test standards also make it difficult to guarantee the consistency and comparability of simulation results.

[0026] To reduce testing costs and shorten R&D cycles, some companies have begun to introduce finite element analysis software to simulate wheel impact conditions. However, existing numerical simulation solutions generally suffer from high dependence on engineer experience, complex modeling processes, and long simulation preparation cycles. Specifically, in the traditional finite element simulation process, engineers need to manually complete a series of operations, including cleaning the wheel model, adjusting its attitude, modeling and positioning the impact hammer, calculating the initial impact velocity, modeling the test bench and buffer structure, defining contact relationships, applying boundary conditions, and meshing. This process is not only cumbersome but also highly coupled between steps. Once the front-end parameters change, a large-scale model adjustment is often required, severely limiting simulation efficiency.

[0027] From a computational efficiency perspective, traditional finite element analysis workflows often repeatedly generate bench models, buffer structure meshes, and general boundary condition settings in each simulation task, leading to a large amount of redundant calculations and invalid data processing. Furthermore, due to the lack of workflow-level optimization mechanisms for impact-related transient dynamics problems, simulation tasks typically require lengthy preprocessing and solution times, making it difficult to support parallel analysis under multiple operating conditions or parameter sensitivity studies. This, to some extent, limits the value of simulation methods in advanced application scenarios such as scheme comparison, rapid iteration, and intelligent optimization.

[0028] Based on this, the computational efficiency optimization method, device and server for wheel impact simulation provided by this invention can significantly reduce modeling complexity and computational cost while ensuring simulation accuracy through model structure predefinition, mesh embedding, parameter mapping and computational process optimization, thereby improving the practicality and promotion value of wheel impact simulation in industrial R&D and improving simulation computational efficiency.

[0029] See Figure 1 The diagram shows a flowchart of a computational efficiency optimization method for wheel impact simulation. The method mainly includes the following steps S102 to S106:

[0030] Step S102: The wheel model to be processed is automatically assembled using a preset standard test bench component, and a hammer model is generated according to preset impact standard parameters. The assembled wheel model and hammer model are then used to construct a wheel impact simulation model.

[0031] In one implementation, firstly, the user-imported wheel model is acquired. This wheel model is a three-dimensional geometric model of the wheel structure to be simulated, typically imported in a common 3D exchange format. Subsequently, the system calls the built-in standard test bench component, which is a simulation resource pre-meshed and encapsulated. This component includes a standardized test bench model, which refers to a test bench simulation model pre-meshed with hexahedral meshes, element type set to C3D8R, and material property assignments, according to industry-standard test bench geometry, and stored as an input file in the system. The standardized test bench model has a pre-set pre-defined test bench installation reference at its support base. This pre-defined installation reference is a reference coordinate system used for assembly and positioning with the wheel model, including the coordinates of the installation center point and the axial direction.

[0032] While calling the standardized test bench model, the system receives user selections of assembly features on the wheel model. Assembly features refer to the geometric structures on the wheel model that form an installation fit with the test bench support, specifically bolt hole features. The user selects a bolt hole surface or hole axis on the wheel model through the front-end interface. The system automatically recognizes this selection, determines the target assembly feature, and extracts its geometric center point and axial direction. These extracted geometric center point and axial direction are then used as the wheel assembly reference. The wheel assembly reference is the wheel-side reference coordinate system used for assembly alignment with the test bench side.

[0033] Based on this, the wheel model and the standardized bench model are aligned coaxially using the wheel assembly datum and the preset bench mounting datum. Coaxial alignment refers to an automated assembly operation that aligns the axial direction of the wheel assembly datum with the axial direction of the preset bench mounting datum, and also aligns the center point of the wheel assembly datum with the center point of the preset bench mounting datum. Through this operation, the wheel model is precisely installed on the support seat of the standardized bench model, completing the automated assembly.

[0034] Simultaneously, a hammer model is generated based on preset impact standard parameters. These preset impact standard parameters refer to the impact test condition parameters specified in industry standards, specifically including the target mass and geometric dimensions of the hammer. The system first determines the target mass and geometric dimensions of the hammer based on the preset impact standard parameters, and then calculates the volume of the hammer entity based on the determined geometric dimensions. The hammer entity refers to a hammer model instance that has been geometrically modeled in simulation software and has defined geometric dimensions and volume. Subsequently, the system performs density back-calculation processing on the target mass and hammer volume. Density back-calculation processing refers to calculating the equivalent density value that the hammer material should have, using the formula density equals mass divided by volume, based on the known target mass and the calculated hammer volume. The system assigns the calculated equivalent density to the material properties of the hammer entity, making the total mass of the hammer entity equal to the target mass specified by the preset impact standard parameters, thus obtaining a hammer model that meets the industry standard quality requirements.

[0035] At this point, the system utilizes the automatically assembled wheel model, the standardized test bench model, and the generated impact hammer model to jointly construct a complete wheel impact simulation model. This wheel impact simulation model includes the geometry of the wheel, test bench, and impact hammer, along with their corresponding meshes, materials, contact definitions, and boundary conditions, for subsequent explicit dynamic solutions.

[0036] Because the test bench structure remains strictly fixed during simulation, its geometry and mesh generation are highly consistent. Therefore, the system pre-generates a high-quality mesh for the bench structure in the background and models it using solid element types suitable for explicit dynamic analysis. The mesh data is built into the wheel impact simulation application as an input file, automatically loaded and assembled with the wheel model before simulation calculation, thus significantly reducing the user's modeling complexity and improving overall simulation efficiency and stability.

[0037] Step S104: Explicit dynamic solution processing is performed on the wheel impact simulation model. During the solution process, intelligent mass scaling processing is performed on the structures to be scaled in the wheel impact simulation model other than the impact hammer model, and the motion state of the impact hammer model is monitored in real time. The impact simulation process of the impact hammer model is automatically terminated according to the monitoring results to obtain the simulation results.

[0038] In one implementation, the system first submits the wheel impact simulation model constructed in step S102 to an explicit dynamics solver for calculation. Explicit dynamics solving refers to the process of using the central difference method to perform stepwise integration in the time domain to solve for the stress, strain, and displacement fields of the structure under conditions of large deformation, nonlinearity, and high-frequency dynamic response.

[0039] During the solution process, the system executes intelligent mass scaling in parallel. Intelligent mass scaling refers to a technique that selectively increases the equivalent density of some structural elements, artificially amplifying their stable time step, thereby improving overall solution efficiency. Specifically, when intelligent mass scaling is enabled, the system first performs identification processing on the wheel impact simulation model. Identification processing involves the system traversing the entire model element set based on the unique entity identifier assigned to the impact hammer model in the preprocessing stage, locating and marking all elements belonging to the impact hammer model. Based on this, the system forcibly excludes the element set corresponding to the impact hammer model from the mass scaling candidate region. The mass scaling candidate region refers to the element set that allows equivalent density adjustment. Through this forced exclusion operation, the system obtains the structures to be scaled in the wheel impact simulation model, excluding the impact hammer model. The structures to be scaled specifically include the wheel model, tire model, and related support structures.

[0040] Subsequently, the system obtains the original stable time step of each element in the structure to be scaled at the current density. The original stable time step refers to the maximum time increment allowed for an element to satisfy the explicit dynamic stability convergence condition under the current material density and stiffness conditions. Its value is directly proportional to the element's characteristic length and inversely proportional to the material wave velocity. The system compares the original stable time step of each element with the preset target stable time step. The target stable time step refers to the overall solution time step threshold that the system expects to achieve, which is preset by the user according to the balance requirements of computational accuracy and efficiency. When the original stable time step of an element is less than the target stable time step, the system amplifies the equivalent density of the element based on the ratio of the target stable time step to the original stable time step of that element. The equivalent density amplification process involves multiplying the current density of the element by the square of this ratio to obtain a new equivalent density value, which is then reassigned to the element's material properties. Through this amplification process, the corrected stable time step of the element is increased to be no less than the target stable time step, thereby effectively amplifying the overall solution time step and significantly improving computational efficiency.

[0041] During the solution process, the system simultaneously performs real-time monitoring and adaptive termination processing of the impact hammer model's motion state. Before the solution starts, the system registers the impact hammer model as a monitoring object and performs real-time monitoring of the velocity components of the impact hammer model in the impact direction during explicit time integration. The impact direction refers to the main motion axis of the impact hammer striking the wheel, specifically the vertical direction of the global coordinate system. The velocity component refers to the instantaneous velocity scalar value of the reference node or element set of the impact hammer model in this axis. The system continuously samples and determines the velocity components output at each explicit time step.

[0042] The system has a preset velocity threshold, which serves as a critical value to distinguish between effective motion and numerical noise in the impact hammer. When the absolute value of the velocity component of the impact hammer model is less than the preset threshold, and the direction of the velocity component reverses, the system determines that the impact hammer model has completed its main energy transfer and has begun to rebound, thus ending the impact loading phase. A reversal of velocity direction refers to a change in the sign of the velocity component, either from negative to positive or from positive to negative. This judgment logic satisfies both amplitude and sign conditions, effectively avoiding misjudgments caused by numerical oscillations.

[0043] Once the judgment condition is met, the system immediately performs a termination operation, taking the current time step as the actual termination time of the impact analysis step, automatically terminating the impact simulation process of the impact hammer model, and no longer continuing to the preset maximum analysis time. Through this adaptive termination process, the total time of explicit dynamic solution is precisely limited to the time interval that completely covers the impact process, completely eliminating invalid calculations after the impact is completed.

[0044] After the above intelligent mass scaling and adaptive termination processing, the system outputs complete simulation results. These results include time-series data of physical quantities such as stress, strain, displacement, and velocity fields of the wheel, test bench, and impact hammer throughout the entire impact process.

[0045] Step S106: Automatically identify and filter the simulation results to determine the hammer simulation results corresponding to the hammer model, and remove the hammer simulation results to obtain the visualization cloud map corresponding to the wheel structure.

[0046] In one implementation, the system first reads the simulation result file output in step S104 and parses all the unit datasets in the simulation result file. During the parsing process, the system traverses and matches the unit datasets based on the unique entity identifier assigned to the hammer model in the preprocessing stage, automatically locates and marks all unit sets belonging to the hammer model, and determines the unit set as the hammer simulation result.

[0047] Subsequently, the system performs result filtering. Result filtering involves actively shielding marked impact hammer simulation result units during the generation of visual cloud maps, extreme value statistics, and output result envelopes, preventing them from participating in cloud map rendering, stress-strain extreme value search, and result data statistical calculations. This filtering operation does not physically delete the original data in the result file; rather, it selectively shields data at the post-processing display and calculation levels. This ensures the integrity of the result file while preventing the high stress values ​​generated by the impact hammer during contact calculations from causing visual interference and statistical contamination of the wheel's true response field.

[0048] Finally, based on the filtered results, the system generates a visual cloud map corresponding to the wheel structure. This visual cloud map only contains stress, strain, and displacement distribution information of the wheel model and its related supporting structures, and does not include any impact hammer model results data. Engineers can directly and accurately identify the structural weak points, plastic deformation areas, and stress concentration levels of the wheel body under impact loads using this visual cloud map, without the need for manual data cleaning or secondary processing.

[0049] The computational efficiency optimization method for wheel impact simulation provided in this embodiment of the invention can significantly reduce modeling complexity and computational cost while ensuring simulation accuracy through model structure predefinition, mesh embedding, parameter mapping and computational process optimization, thereby improving the practicality and promotion value of wheel impact simulation in industrial R&D and improving simulation computational efficiency.

[0050] This invention also provides an implementation method for simulating wheel impact using a novel computationally efficient simulation method, as detailed in (1) to (5) below:

[0051] (1) The standardized test bench model is called through the standard test bench component, and the preset test bench installation reference is corresponding to the test bench support of the standardized test bench model. Then, based on the user's selection operation of various assembly features of the wheel model, the target assembly feature is determined, and the geometric center point and axial direction of the target assembly feature are extracted to determine the geometric center point and axial direction as the wheel assembly reference. Finally, the wheel model and the standardized test bench model are aligned coaxially using the wheel assembly reference and the preset test bench installation reference to obtain the assembled wheel model. The standardized test bench model is a test bench model that has been pre-meshed and encapsulated.

[0052] Specifically, in establishing assembly relationships, the system introduces an automatic assembly algorithm based on geometric features. In the front-end interface, users only need to select the bolt hole feature (hole surface or hole axis) on the wheel model, and the system automatically extracts the geometric center point and axial direction of the bolt hole. The back-end assembly module uses this as the assembly datum to align the wheel model with the predefined installation datum of the support frame. This process is entirely automated in the back-end, requiring no user intervention in coordinate alignment or constraint definition, thus significantly reducing modeling complexity.

[0053] At the numerical discretization level, since the test bench structure maintains a fixed geometry across all simulation conditions, it is pre-meshed and encapsulated as a standardized component. The test bench geometry model is uniformly generated with high-quality hexahedral meshes during the preprocessing stage, and all meshes are discretized using the C3D8R element type. This element type exhibits good computational efficiency and numerical stability in explicit dynamics, ensuring the overall stiffness characteristics of the test bench while avoiding unnecessary computational overhead.

[0054] The generated test bench mesh is stored as an .inp file and directly integrated into the wheel impact simulation application as a built-in resource. In the software engineering workflow, when the user enables the standard test bench option in the simulation task, the system automatically executes the following steps: loading the built-in test bench .inp file; completing the automatic mapping of nodes and element numbers; applying full constraints to the base; initializing the equivalent damping parameters of the rubber supports; and completing the interface docking with the wheel assembly module. The entire process is transparent to the user and requires no additional manual operation.

[0055] In terms of assembly and constraint definition, the connection process between the wheel and the test bench is simplified through a front-end interactive mechanism. Users only need to select the bolt hole feature surface or hole axis on the wheel model in the visual interface, and the system can automatically identify the geometric feature and extract its spatial position, axial direction, and hole diameter information. The background assembly algorithm uses the selected bolt hole as the assembly datum to automatically establish coaxial alignment, contact relationship, or equivalent constraint relationship between the wheel and the test bench support, without requiring users to manually define complex assembly coordinate systems or connection conditions. This process ensures that the wheel installation posture is consistent with the clamping method in actual testing.

[0056] Considering that the test bench structure has a fixed geometric shape and topological relationship under different simulation conditions, the test bench is preprocessed and encapsulated as a standardized component. In the preprocessing stage, a high-quality hexahedral mesh is generated for the test bench geometric model, and C3D8R type elements are uniformly used for discretization. This element type has good numerical stability and computational efficiency in explicit dynamic analysis, ensuring the overall stiffness and inertial characteristics of the test bench while avoiding unnecessary computational overhead. The generated test bench mesh, along with its material, element set, and constraint definitions, are written into a separate .inp file and integrated as a built-in resource into the wheel impact simulation application.

[0057] In the software engineering implementation process, when a user creates a new wheel impact simulation task and enables the standard test bench option, the system automatically completes the following steps: First, it loads the built-in bench inp mesh file; then, it rearranges the node and element numbers to avoid conflicts with the user's imported model; next, it automatically assembles the wheel and the bench support according to the bolt hole information selected at the front end; finally, it loads the equivalent damping and stiffness parameters and generates a complete explicit dynamic solution model.

[0058] By introducing a test bench model that conforms to industry standards, and combining it with a rubber buffer modeling method based on equivalent damping control, an automatic assembly algorithm based on bolt hole features, and pre-packaged C3D8R mesh components, a complete, stable, and highly automated full-scale simulation scheme for wheel impact was constructed.

[0059] In one implementation, the equivalent stiffness coefficient and equivalent damping coefficient are determined by performing parameter back-calibration on a preset standard calibration condition. Based on the equivalent stiffness coefficient and equivalent damping coefficient, the equivalent damping unit is configured so as to simulate the bench buffer performance of the standardized bench model in the impact simulation process using the configured equivalent damping unit.

[0060] Specifically, for the natural rubber support that plays a crucial buffering role in the test bench, an engineering modeling strategy combining equivalent damping and equivalent stiffness is adopted, rather than directly introducing a complex hyperelastic material model. The goal is to control the vertical buffer displacement of the test bench within the allowable range of 7.5 ± 0.75 mm under industry standard calibration load conditions. The equivalent model can be simplified as a one-dimensional viscoelastic system, with the following vertical force-displacement relationship:

[0061]

[0062] in, The vertical reaction force borne by the support of the platform; For equivalent stiffness, the Shore hardness assignment strategy is also adopted; This is the equivalent damping coefficient; and These represent the vertical displacement and its velocity, respectively.

[0063] The system adjusts parameters by performing back-calibration on standard calibration conditions, ensuring that the initial impact response is not weakened by excessive damping. and The combination of these factors allows the simulation calculations to obtain... The test results are stable and fall within the above-mentioned allowable range, thereby achieving consistency between the bench buffer performance and the physical test.

[0064] (2) Determine the target mass and geometric dimensions of the impact hammer according to the preset impact standard parameters, and determine the volume of the impact hammer based on the geometric dimensions. By performing density back calculation on the target mass and impact hammer volume, the equivalent density of the impact hammer is obtained. The impact hammer material is set according to the equivalent density to obtain the impact hammer model. Then, a wheel impact simulation model is constructed based on the impact hammer model and the standardized bench model.

[0065] In one implementation, in wheel impact simulation, the steady-state time step of the explicit dynamic solution is determined by both the smallest element size and the material wave velocity in the model. When extremely small elements exist in the wheel, tire, or locally refined regions, the overall time step decreases significantly, making it difficult to meet the computational efficiency requirements of industrial applications. To improve solution efficiency without significantly affecting the physical realism of the simulation, a mass scaling component is introduced, and its impact is strictly controlled through algorithmic constraints and a quality evaluation mechanism.

[0066] In impact models, the mass of the impact hammer is usually explicitly specified by standards. Since the geometry of the impact hammer can be determined during the modeling stage, the mass is set by converting mass to density. That is, the total mass of the impact hammer entity is made to meet the standard requirements by back-calculating the density. The basic relationship is as follows:

[0067]

[0068] in, The specified hammer mass; The geometric volume of the punch (the geometric volume of the punch can be determined according to industry standard requirements); The system automatically calculates and assigns the equivalent density to the impact hammer material.

[0069] (3) When intelligent mass scaling is enabled, the wheel impact simulation model is identified and the hammer model is forcibly excluded from the mass scaling candidate area to obtain the structure to be scaled in the wheel impact simulation model other than the hammer model. The original stable time step of the structure to be scaled at the current density is obtained, and the original stable time step is compared with the preset target stable time step. Based on the comparison result, intelligent mass scaling is performed on the structure to be scaled. In one embodiment, when the original stable time step is less than the target stable time step, the equivalent density of the structure to be scaled is amplified according to the ratio of the target stable time step to the original stable time step so that the corrected stable time step of the structure to be scaled is not less than the target stable time step.

[0070] Specifically, the mass of the impact hammer is already embedded in the material density. Introducing additional mass into the impact hammer unit during mass scaling directly alters the conservation relationship between impact momentum and energy, significantly interfering with the impact response. Therefore, an impact hammer entity isolation mechanism is introduced during the mass scaling region selection stage. When automatically generating the impact hammer, the system assigns it a unique entity ID. During the initialization of the mass scaling component, the set of units corresponding to this ID is forcibly excluded from the mass scaling candidate region, ensuring that mass scaling only applies to the wheel, tire, and related support structures, and never to the impact hammer entity itself.

[0071] At the quality scaling algorithm level, a quality scaling strategy based on the target stable time step is adopted. For any explicit unit, its stable time step can be approximately expressed as:

[0072]

[0073] in, , The feature length of the unit; For material wave velocity; and These are the elastic modulus and density of the unit, respectively.

[0074] When some units Less than the target time set by the system At that time, the system performs mass scaling by increasing the equivalent density, so that the corrected stable time step satisfies:

[0075]

[0076] From this, the scaled equivalent density can be derived:

[0077]

[0078] This algorithm ensures that the material stiffness parameters remain unchanged, and relaxes the time step constraint only by changing the inertia term, thereby improving the overall solution efficiency.

[0079] To evaluate the impact of mass scaling on simulation results, a mass scaling evaluation index and judgment logic are introduced. After mass scaling is completed, the system automatically calculates the total mass change of the model (excluding the impact hammer) and the mass introduction ratio.

[0080]

[0081] in, This represents the original total mass of the model without mass scaling (excluding the impact hammer). η represents the total mass of the model after mass scaling; η is the mass increase ratio.

[0082] The system compares η with preset engineering allowable thresholds and generates a graded evaluation result. For example, when η is less than the recommended threshold (e.g., 5% by default here), the impact of mass scaling on dynamic response is determined to be negligible; when η exceeds the recommended value but is still within an acceptable range, the system informs the user in the software interface that the current mass scaling level may have some impact on the results; when η exceeds the maximum allowable threshold, the system automatically issues a warning and suggests that the user reduce the mass scaling level. To reduce the amount of quality introduced.

[0083] (4) During the explicit dynamic solution process, the velocity component of the hammer model in the impact direction is monitored in real time. When the absolute value of the velocity component is less than the preset threshold and the direction of the velocity component is reversed, it is determined that the hammer model has completed the impact and rebound, and the impact simulation process of the hammer model is automatically terminated.

[0084] In one implementation, during explicit dynamics-based wheel impact simulation, the impact hammer mass typically varies within a certain range according to industry standards. Since the impact hammer mass directly affects the system's inertial response, the dynamic time required for the descent, contact, and rebound phases differs significantly for impact hammers of different masses. When the impact hammer mass is large, its descent and rebound processes are significantly prolonged. If a fixed total analysis time is still used for the solution, two types of problems are likely to occur: first, insufficient analysis time, meaning the simulation results do not cover the impact hammer velocity reversal or rebound phases, resulting in the maximum impact response frame not being captured; second, blindly extending the analysis time to ensure rebound occurs, leading to a large amount of invalid calculations without impact physical meaning, significantly reducing solution efficiency. Therefore, the traditional method of relying on manual experience to set the explicit total analysis time is no longer suitable for intelligent simulation of wheel impacts with multiple masses and working conditions.

[0085] To address the aforementioned issues, a real-time monitoring and adaptive termination mechanism based on the hammer's motion state is introduced into the explicit dynamics solution process. Before the solution begins, the system registers the hammer entity as a monitoring object and, during the explicit time integration process, progressively outputs and analyzes the velocity state of the hammer element or reference node. Specifically, at each explicit time step... The system extracts the hammer along the impact axis (e.g., global coordinates). velocity component in the Z direction ( This speed is used as the core monitoring quantity for determining the phase of the hammer's motion.

[0086] At the algorithm level, the system determines whether the impact process is complete by analyzing the sign and trend of the hammer's velocity. When the hammer is in the falling phase, its velocity components... ( The impact axis remains aligned with the direction of impact (e.g., negative); after contact with the wheel and completion of the main energy transfer, the hammer velocity gradually decreases. When the velocity approaches zero and the sign reverses, it indicates that the hammer has completed a full impact and entered the rebound phase. The logic for determining the completion of this impact can be formally described as follows:

[0087]

[0088] in, Let be the axial velocity of the hammer at the nth time step; A preset speed threshold is used to avoid misjudgments caused by numerical noise; the sign change condition is used to confirm that the speed direction has been reversed.

[0089] Once the above conditions are met, the system determines that the impact hammer has begun to rebound, and the impact loading phase ends. At this point, the solution control module immediately executes an explicit analysis termination operation, setting the current time step... This serves as the actual termination point of the impact analysis step, without needing to continue to the preset maximum analysis time. In this way, the total duration of the explicit dynamic analysis is adaptively limited to the time interval that just covers the entire impact process, avoiding the waste of computation during invalid time periods.

[0090] In terms of software engineering implementation, this real-time monitoring mechanism is embedded in the solution process as an independent hammer state monitoring component. This component is connected to the solver's data output module and performs lightweight monitoring of the velocity of only key elements or reference nodes of the hammer body without significantly increasing I / O overhead. The monitoring logic runs continuously in the background, completely transparent to the user, and sends a termination signal to the solution control module when a termination condition is triggered.

[0091] This adaptive termination mechanism further integrates with the aforementioned impact hammer unloading and unloading analysis step components to form a collaborative workflow. When the impact analysis step is terminated due to impact hammer velocity reversal, the system automatically enters the unloading phase: the impact hammer's constraints are released or the impact hammer entity is removed, and then the unloading analysis step is initiated to simulate the wheel's rebound and stress redistribution process under the influence of no external impact hammer. The initial state of the unloading analysis step is inherited from the termination time of the impact analysis step, thus ensuring that the maximum impact response is fully preserved, while avoiding discontinuities in results due to improper analysis step switching.

[0092] By introducing a real-time monitoring and adaptive termination mechanism based on the impact hammer velocity in the explicit dynamic solution process, the key impact stages can be stably captured under different impact hammer masses and different impact conditions. This not only avoids the problem of missing impact result frames but also significantly reduces invalid calculation time. Furthermore, it forms a logically closed simulation process with the strain evaluation method of the unloading analysis step, providing efficient, reliable, and scalable technical support for intelligent simulation applications of wheel impact.

[0093] To achieve full simulation of wheel impact conditions and ensure consistency between numerical analysis results and industry-standard physical impact tests in terms of boundary conditions and energy dissipation characteristics, a test bench model was introduced into the intelligent simulation application of wheel impact. This test bench structure simulates the installation and support environment in actual impact tests. Its overall configuration, force path, and buffering characteristics strictly adhere to the industry standards for wheel impact testing equipment, thereby avoiding deviations caused by relying solely on idealized constraints.

[0094] See Figure 2 The diagram illustrates a wheel impact simulation. At the physical modeling level, the test bench consists of a fixed base, a support linkage mechanism, and a natural rubber buffer support at the bottom. In the simulation, the test bench base is fixed to the global coordinate system using a fully constrained method, ensuring that the test bench does not undergo overall rigid body displacement during the impact. The wheel is mounted on the test bench via the support seat, and its impact reaction force is transmitted to the rubber buffer unit through the support linkage, thus forming a force transmission path consistent with physical experiments. This structure allows for a reasonable distribution of impact energy among the wheel's plastic deformation, contact dissipation, and the test bench's buffer deformation, preventing the impact response from being overly idealized.

[0095] For the natural rubber support that plays a crucial buffering role in the test bench, an engineering modeling strategy combining equivalent damping and equivalent stiffness is adopted, rather than directly introducing a complex hyperelastic material model. The goal is to control the vertical buffer displacement of the test bench within the allowable range of 7.5 ± 0.75 mm under industry standard calibration load conditions. The equivalent model can be simplified as a one-dimensional viscoelastic system, with the following vertical force-displacement relationship:

[0096]

[0097] in, The vertical reaction force borne by the support of the platform; For equivalent stiffness, the Shore hardness assignment strategy is also adopted; This is the equivalent damping coefficient; and These represent the vertical displacement and its velocity, respectively.

[0098] The system adjusts parameters by performing back-calibration on standard calibration conditions, ensuring that the initial impact response is not weakened by excessive damping. and The combination of these factors allows the simulation calculations to obtain... The test results are stable and fall within the above-mentioned allowable range, thereby achieving consistency between the bench buffer performance and the physical test.

[0099] (5) In the post-processing stage of explicit dynamic simulation of wheel impact, especially after the introduction of the plastic constitutive model of metallic materials, it is often found that the local stress results significantly exceed the reasonable range of the maximum ultimate stress or the post-yield strengthening stage of the material. After analysis, such abnormally high stress results do not originate from the actual structural response of the wheel body, but mainly from the highly localized stress transfer formed when the impact hammer and the wheel body collide directly at the moment of contact. Since the impact hammer is usually modeled as a high-stiffness, high-quality rigid or near-rigid body structure, the numerical stress concentration in its contact area has obvious numerical characteristics. When mixed with the stress field of the wheel body in the post-processing cloud map, it will significantly interfere with engineers' interpretation of the actual stress state of the wheel, especially the maximum equivalent plastic strain and yield area distribution. To solve this problem, a component-level cloud map result filtering function is introduced into the post-processing component of the intelligent simulation application of wheel impact. By identifying, locking and removing the automatically generated impact hammer components, the stress and strain results are displayed only for the wheel and its related structures, thereby ensuring the physical readability and engineering effectiveness of the post-processing results.

[0100] At the model level, the system assigns a unique volumetric feature identifier to each hammer component during the pre-processing automatic hammer addition stage. This identifier can be defined based on the component ID, instance name, assembly level path, or internal attribute label, and remains consistent throughout the solution and post-processing workflow. In the post-processing stage, the system first parses all element sets in the analysis result file, and automatically locates the element sets belonging to the hammer components by matching the aforementioned volumetric feature identifiers. And add it to the predefined result filtering target set.

[0101] In the process of generating contour plots, the stress or strain field can essentially be represented as defined in the entire model element domain. scalar or tensor field By constructing a component-level filtering operator. In the result output stage, the element domain corresponding to the hammer body is excluded. Its function can be formally expressed as:

[0102]

[0103] in, The cell domain representing the complete model; This represents the set of units corresponding to the hammer components; This is the filtered result field. For the whole model unit domain scalar on, For the whole model unit domain Tensor field on.

[0104] In its implementation, this filtering operation does not simply delete data. Instead, it masks the impact hammer unit during the post-processing display and result statistics stages, preventing it from participating in contour rendering, extreme value search, and result envelope calculation. This ensures the integrity of the result file while avoiding contamination of the wheel body results by the high stress values ​​of the impact hammer.

[0105] At the software engineering level, this functionality is encapsulated as a standalone post-processing result filtering component, forming a logical closed loop with the automatic hammer-adding module. During initialization, the component automatically reads the identification information of the hammer components and uses it as the default filtering object, requiring no additional user configuration. Simultaneously, the system retains advanced options in the user interface, allowing users to manually enable or disable the display of hammer body results to meet specific needs during debugging or model verification phases.

[0106] By introducing a cloud map result filtering mechanism based on component features in the post-processing stage, the interference of impact hammer contact stress on wheel impact results can be effectively eliminated, so that key indicators such as plastic strain and equivalent stress only reflect the real structural response of the wheel body, thereby significantly improving the reliability and consistency of wheel impact simulation results in engineering interpretation and standard compliance assessment.

[0107] In summary, the present invention can:

[0108] 1. Improve modeling efficiency. The test bench structure is pre-packaged as a standardized test bench model and the installation benchmark is preset. At the same time, the density is automatically calculated based on the impact standard to generate the impact hammer model. Users only need to import the wheels and select the bolt holes, and the system will automatically complete the assembly. As a result, the modeling time is reduced from several hours to minutes, completely eliminating the dependence on the engineer's experience, and parameter changes do not require repeated modeling.

[0109] 2. Improve solution efficiency. A dual optimization is introduced in parallel during the solution process: First, intelligent mass scaling automatically excludes impact hammers using a unique identifier, amplifying the equivalent density only for wheels and support structures, ensuring the stabilization time step reaches the preset target and overcoming the limitation of small mesh on solution speed; second, adaptive termination monitors the impact hammer speed in real time, automatically terminating the calculation when the speed reverses, precisely limiting the analysis time to the instant the impact is completed. These two mechanisms work together to improve solution speed and completely eliminate unnecessary computational runs.

[0110] 3. Post-processing interpretation efficiency is significantly improved. The impact hammer marker is automatically identified in the post-processing stage, and the impact hammer result data is actively masked in the cloud map rendering and extreme value statistics, retaining only the true response field of the wheel body. This eliminates the need for engineers to manually clean the data, improving post-processing efficiency and completely avoiding the risk of misjudgment caused by interference with the cloud map.

[0111] 4. The simulation results are highly consistent with the physical test. An equivalent damping unit is integrated into the standardized test bench model. By performing parameter back-calibration on the standard calibration conditions, the equivalent stiffness and equivalent damping coefficient are accurately determined, ensuring that the test bench's buffer displacement response strictly falls within the allowable range. Thus, a buffering characteristic consistent with the physical test is achieved with a simplified equivalent model. The simulation results under different working conditions and different operators are highly consistent and comparable.

[0112] Regarding the computational efficiency optimization method for wheel impact simulation provided in the foregoing embodiments, this invention provides a computational efficiency optimization device for wheel impact simulation, see [link to relevant documentation]. Figure 3 The diagram shows a structural schematic of a computational efficiency optimization device for wheel impact simulation. The device includes the following parts:

[0113] The model building module 302 automatically assembles the wheel model to be processed using preset standard test bench components and generates a hammer model according to preset impact standard parameters. The assembled wheel model and hammer model are then used to build a wheel impact simulation model.

[0114] The intelligent solver module 304 performs explicit dynamic solution processing on the wheel impact simulation model. During the solution process, it performs intelligent mass scaling processing on the structures to be scaled in the wheel impact simulation model other than the impact hammer model, and monitors the motion state of the impact hammer model in real time. Based on the monitoring results, it automatically terminates the impact simulation process of the impact hammer model and obtains the simulation results.

[0115] The intelligent post-processing module 306 automatically identifies and filters the simulation results, determines the hammer simulation results corresponding to the hammer model, and removes the hammer simulation results to obtain the visualization cloud map corresponding to the wheel structure.

[0116] The computational efficiency optimization device for wheel impact simulation provided in this application embodiment can significantly improve the simulation computational efficiency.

[0117] In one embodiment, when performing the step of automatically assembling the wheel model to be processed using a preset standard test bench component, the model construction module 302 is further configured to: call a standardized test bench model and a preset test bench mounting reference corresponding to the test bench support of the standardized test bench model, wherein the standardized test bench model is a test bench model that has been pre-meshed and encapsulated; determine the target assembly features based on the user's selection operation of various assembly features of the wheel model, and extract the geometric center point and axial direction of the target assembly features to determine the geometric center point and axial direction as the wheel assembly reference; and perform coaxial alignment processing on the wheel model and the standardized test bench model using the wheel assembly reference and the preset test bench mounting reference to obtain the assembled wheel model.

[0118] In one embodiment, the standardized test bench model includes an equivalent damping element. The model construction module 302 is further configured to: determine the equivalent stiffness coefficient and the equivalent damping coefficient by performing parameter back-calibration processing on a preset standard calibration condition, and configure the equivalent damping element according to the equivalent stiffness coefficient and the equivalent damping coefficient, so as to use the configured equivalent damping element to simulate the test bench buffer performance of the standardized test bench model in the impact simulation process.

[0119] In one embodiment, when performing the step of generating a hammer model based on preset impact standard parameters, the model building module 302 is further configured to: determine the target mass and geometric dimensions of the hammer based on the preset impact standard parameters, and determine the volume of the hammer based on the geometric dimensions; obtain the equivalent density of the hammer by performing density back-calculation on the target mass and the volume of the hammer, and set the hammer material based on the equivalent density to obtain the hammer model.

[0120] In one embodiment, when performing intelligent mass scaling processing on the structure to be scaled in the wheel impact simulation model other than the impact hammer model, the intelligent solver module 304 is further configured to: when intelligent mass scaling processing is enabled, forcibly exclude the impact hammer model from the mass scaling candidate region by performing identification processing on the wheel impact simulation model, thereby obtaining the structure to be scaled in the wheel impact simulation model other than the impact hammer model; obtain the original stable time step of the structure to be scaled at the current density, compare the original stable time step with the preset target stable time step, and perform intelligent mass scaling processing on the structure to be scaled according to the comparison result.

[0121] In one embodiment, when performing the step of intelligent quality scaling processing on the structure to be scaled based on the comparison result, the intelligent solving module 304 is further configured to: when the original stable time step is less than the target stable time step, amplify the equivalent density of the structure to be scaled according to the ratio of the target stable time step to the original stable time step, so that the corrected stable time step of the structure to be scaled is not less than the target stable time step.

[0122] In one embodiment, when performing the step of real-time monitoring of the motion state of the impact hammer model to automatically terminate the impact simulation process of the impact hammer model based on the monitoring results, the intelligent solution module 304 is further configured to: perform real-time monitoring and processing of the velocity component of the impact hammer model in the impact direction during the explicit dynamic solution process; when the absolute value of the velocity component is detected to be less than a preset threshold and the direction of the velocity component is reversed, determine that the impact hammer model has completed the impact and rebound, and automatically terminate the impact simulation process of the impact hammer model.

[0123] The device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0124] This invention provides a server, specifically, the server includes a processor and a storage device; the storage device stores a computer program, which, when run by the processor, executes the method described in any of the above embodiments.

[0125] Figure 4 This is a schematic diagram of the structure of a server provided in an embodiment of the present invention. The server 100 includes: a processor 40, a memory 41, a bus 42 and a communication interface 43. The processor 40, the communication interface 43 and the memory 41 are connected through the bus 42. The processor 40 is used to execute executable modules, such as computer programs, stored in the memory 41.

[0126] The memory 41 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 43 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0127] Bus 42 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0128] The memory 41 is used to store programs. After receiving an execution instruction, the processor 40 executes the program. The method executed by the device for defining the flow process disclosed in any of the foregoing embodiments of the present invention can be applied to the processor 40 or implemented by the processor 40.

[0129] Processor 40 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 40 or by instructions in software form. Processor 40 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 41. The processor 40 reads the information in memory 41 and, in conjunction with its hardware, completes the steps of the above method.

[0130] The computer program product of the readable storage medium provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the foregoing method embodiments. For specific implementation, please refer to the foregoing method embodiments, which will not be repeated here.

[0131] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion 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 described in the various embodiments of this invention. 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.

[0132] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing the computational efficiency of wheel impact simulation, characterized in that, The method includes: The wheel model to be processed is automatically assembled using a preset standard test bench component, and an impact hammer model is generated according to preset impact standard parameters. The assembled wheel model and the impact hammer model are then used to construct a wheel impact simulation model. The wheel impact simulation model is subjected to explicit dynamic solution processing. During the solution process, intelligent mass scaling processing is performed on the structures to be scaled in the wheel impact simulation model other than the impact hammer model. The motion state of the impact hammer model is monitored in real time so as to automatically terminate the impact simulation process of the impact hammer model according to the monitoring results and obtain the simulation results. The simulation results are automatically identified and filtered to determine the hammer simulation results corresponding to the hammer model, and the hammer simulation results are removed to obtain the visualization cloud map corresponding to the wheel structure. The step of performing intelligent mass scaling on the structures to be scaled in the wheel impact simulation model other than the impact hammer model includes: when intelligent mass scaling is enabled, identifying the wheel impact simulation model to forcibly exclude the impact hammer model from the mass scaling candidate region, thereby obtaining the structures to be scaled in the wheel impact simulation model other than the impact hammer model; obtaining the original stable time step of the structure to be scaled at the current density, comparing the original stable time step with a preset target stable time step, and performing intelligent mass scaling on the structure to be scaled based on the comparison result; The step of performing intelligent quality scaling on the structure to be scaled based on the comparison result includes: when the original stable time step is less than the target stable time step, amplifying the equivalent density of the structure to be scaled according to the ratio of the target stable time step to the original stable time step, so that the corrected stable time step of the structure to be scaled is not less than the target stable time step.

2. The method for optimizing the computational efficiency of wheel impact simulation according to claim 1, characterized in that, The step of automatically assembling the wheel model to be processed using a preset standard test bench assembly includes: The standardized test bench component calls the standardized test bench model and the preset test bench installation reference corresponding to the test bench support of the standardized test bench model, wherein the standardized test bench model is a test bench model that has been pre-meshed and encapsulated. In response to the user's selection of various assembly features of the wheel model, the target assembly feature is determined, and the geometric center point and axial direction of the target assembly feature are extracted to determine the geometric center point and the axial direction as the wheel assembly reference. Using the wheel assembly reference and the preset bench mounting reference, the wheel model and the standardized bench model are aligned coaxially to obtain the assembled wheel model.

3. The method for optimizing the computational efficiency of wheel impact simulation according to claim 2, characterized in that, The standardized bench model includes: an equivalent damping element, and the method includes: By performing parameter back-calibration on the preset standard calibration conditions, the equivalent stiffness coefficient and equivalent damping coefficient are determined. Based on the equivalent stiffness coefficient and the equivalent damping coefficient, the equivalent damping unit is configured so that the configured equivalent damping unit can be used to simulate the bench buffer performance of the standardized bench model during the impact simulation process.

4. The method for optimizing the computational efficiency of wheel impact simulation according to claim 1, characterized in that, The step of generating the impact hammer model based on preset impact standard parameters includes: The target mass and geometric dimensions of the impact hammer are determined according to the preset impact standard parameters, and the volume of the impact hammer is determined based on the geometric dimensions. The equivalent density of the hammer is obtained by performing density back-calculation on the target mass and the volume of the hammer, and the hammer material is set according to the equivalent density to obtain the hammer model.

5. The method for optimizing the computational efficiency of wheel impact simulation according to claim 1, characterized in that, The step of real-time monitoring of the motion state of the impact hammer model, and automatically terminating the impact simulation process of the impact hammer model based on the monitoring results, includes: During the explicit dynamic solution process, the velocity components of the impact hammer model in the impact direction are monitored and processed in real time. When the absolute value of the velocity component is detected to be less than a preset threshold and the direction of the velocity component is reversed, it is determined that the impact hammer model has completed the impact and rebound, and the impact simulation process of the impact hammer model is automatically terminated.

6. A computational efficiency optimization device for wheel impact simulation, characterized in that, The device includes: The model building module automatically assembles the wheel model to be processed using preset standard test bench components and generates an impact hammer model according to preset impact standard parameters. The assembled wheel model and the impact hammer model are then used to construct a wheel impact simulation model. The intelligent solution module performs explicit dynamic solution processing on the wheel impact simulation model. During the solution process, it performs intelligent mass scaling processing on the structures to be scaled in the wheel impact simulation model other than the impact hammer model, and monitors the motion state of the impact hammer model in real time. Based on the monitoring results, it automatically terminates the impact simulation process of the impact hammer model and obtains the simulation results. The intelligent post-processing module automatically identifies and filters the simulation results, determines the hammer simulation results corresponding to the hammer model, and removes the hammer simulation results to obtain the visualization cloud map corresponding to the wheel structure. The step of performing intelligent mass scaling on the structures to be scaled in the wheel impact simulation model other than the impact hammer model includes: when intelligent mass scaling is enabled, identifying the wheel impact simulation model to forcibly exclude the impact hammer model from the mass scaling candidate region, thereby obtaining the structures to be scaled in the wheel impact simulation model other than the impact hammer model; obtaining the original stable time step of the structure to be scaled at the current density, comparing the original stable time step with a preset target stable time step, and performing intelligent mass scaling on the structure to be scaled based on the comparison result; The step of performing intelligent quality scaling on the structure to be scaled based on the comparison result includes: when the original stable time step is less than the target stable time step, amplifying the equivalent density of the structure to be scaled according to the ratio of the target stable time step to the original stable time step, so that the corrected stable time step of the structure to be scaled is not less than the target stable time step.

7. A server, characterized in that, The method includes a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the method of any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method described in any one of claims 1 to 5.

Images