Post-processing method, device and equipment of vehicle body modal discipline and storage medium

Abstract

The application belongs to the technical field of automobile manufacturing, and discloses a post-processing method, device and equipment for a vehicle body modal discipline and a storage medium. The method comprises the following steps: obtaining a vehicle body finite element model, and constructing a vehicle body modal calculation model; performing finite element analysis according to the vehicle body modal calculation model to obtain a benchmark analysis result; determining a variable factor according to the benchmark analysis result; building a sampling model corresponding to the vehicle body modal discipline according to the variable factor; setting a sampling process; sampling the variable factor according to the sampling process based on the sampling model, and outputting an analysis result corresponding to the vehicle body modal discipline. In the foregoing manner, the sampling model is set to automatically post-process the benchmark analysis result of the vehicle body modal discipline, thereby avoiding high labor cost and high error rate caused by manual data post-processing, providing data support for multi-disciplinary lightweight optimization, and improving the optimization efficiency of multi-disciplinary lightweight optimization.

Description

Technical Field

[0001] This invention relates to the field of automotive manufacturing technology, and in particular to a post-processing method, apparatus, equipment, and storage medium for vehicle body modal analysis. Background Technology

[0002] With the development trend of the automotive industry towards "five modernizations"—lightweighting, electrification, intelligence, connectivity, and sharing—the requirements for vehicle lightweighting are constantly increasing during vehicle development. Lightweighting is being incorporated into CAE simulation analysis during the design phase. However, multi-disciplinary, multi-objective lightweighting methods involve a large number of DOE samples, and currently, manual data post-processing is generally used, which is labor-intensive, tedious, and prone to errors.

[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this invention is to provide a post-processing method, apparatus, equipment, and storage medium for vehicle body modal analysis, aiming to solve the technical problems of current manual data post-processing, which is labor-intensive, tedious, and prone to errors.

[0005] To achieve the above objectives, the present invention provides a post-processing method for vehicle body modal analysis, the method comprising the following steps:

[0006] Obtain the finite element model of the vehicle body, and construct the vehicle body modal calculation model based on the finite element model of the vehicle body;

[0007] Finite element analysis was performed based on the vehicle body modal calculation model to obtain the baseline analysis results;

[0008] The variable factors are determined based on the benchmark analysis results;

[0009] Based on the aforementioned variable factors, a sampling model corresponding to the vehicle body modality discipline is constructed.

[0010] The sampling process is set up according to the performance values ​​corresponding to the vehicle body modal discipline and the preset sampling requirements.

[0011] According to the sampling model and the sampling process, the variable factors are sampled, and the analysis results corresponding to the vehicle body modality are output.

[0012] Optionally, the step of constructing a sampling model corresponding to the vehicle body modality discipline based on the variable factors includes:

[0013] The file corresponding to the vehicle body modal calculation model is parsed to determine the thickness information paragraphs corresponding to the variable factors;

[0014] The thickness information paragraph in the file corresponding to the vehicle body modal calculation model is deleted, and the thickness information paragraph input interface is set to obtain the sampling model corresponding to the vehicle body modal discipline.

[0015] Optionally, the step of sampling the variable factors according to the sampling model and the sampling process, and outputting the analysis results corresponding to the vehicle body modality discipline, includes:

[0016] The script embedded in the sampling process is edited using simulation software to obtain a sampling thickness information file;

[0017] A target sampling model is generated based on the sampling thickness information file and the sampling model.

[0018] According to the target sampling model and the sampling process, the variable factors are sampled, and the analysis results corresponding to the vehicle body modality are output.

[0019] Optionally, after determining the variable factors based on the benchmark analysis results, the method further includes:

[0020] Record variables based on the variable factors and generate corresponding files to be processed;

[0021] The process of editing the script embedded in the sampling process using simulation software to obtain a sampling thickness information file includes:

[0022] Define variable identifiers and modify variable names in the file to be processed to generate an adjustment file;

[0023] Based on the adjustment file, the thickness of the variable factor is changed, and a sampling thickness information file is generated.

[0024] Optionally, after parsing the file corresponding to the vehicle body modal calculation model and determining the thickness information paragraphs corresponding to the variable factors, the method further includes:

[0025] Store the thickness information segment as an initial file;

[0026] The step of defining variable identifiers and modifying variable names in the file to be processed to generate an adjustment file includes:

[0027] Based on the first simulation software, variable identifiers are defined and variable names are modified to generate an adjustment file, and the adjustment file is output to the second simulation software. The first simulation software is associated with the file to be processed.

[0028] The process of changing the variable factor thickness based on the adjustment file and generating a sampling thickness information file includes:

[0029] The initial file is input into the second simulation software in a preset format;

[0030] The second simulation software performs variable factor thickness changes based on the adjustment file and the initial file to generate a sampling thickness information file.

[0031] Optionally, determining the variable factors based on the benchmark analysis results includes:

[0032] The sensitivity of multiple body parts to torsional modal parameters, bending modal parameters, and relative mass parameters was obtained.

[0033] The components whose sensitivity is greater than a preset threshold are selected as variable factors.

[0034] Optionally, before obtaining the finite element model of the vehicle body and constructing the modal calculation model of the vehicle body based on the finite element model, the method further includes:

[0035] Select the multidisciplinary performance dimension to be optimized, wherein the multidisciplinary performance dimension includes at least the vehicle body modality discipline;

[0036] The performance conditions and key performance items corresponding to the multidisciplinary performance dimensions are determined respectively. The performance condition corresponding to the vehicle body modal discipline is the vehicle body modal condition, and the key performance items corresponding to the vehicle body modal discipline include the vehicle body torsional mode and the vehicle body bending mode.

[0037] Furthermore, to achieve the above objectives, the present invention also proposes a post-processing device for vehicle body modal analysis, the post-processing device for vehicle body modal analysis comprising:

[0038] The acquisition module is used to acquire the finite element model of the vehicle body and construct a modal calculation model of the vehicle body based on the finite element model of the vehicle body.

[0039] The finite element analysis module is used to perform finite element analysis based on the vehicle body modal calculation model to obtain benchmark analysis results.

[0040] The determination module is used to determine variable factors based on the benchmark analysis results;

[0041] The sampling module is used to build a sampling model corresponding to the vehicle body modality discipline based on the variable factors.

[0042] The sampling module is also used to set up a sampling process based on the performance values ​​corresponding to the vehicle body modal discipline and preset sampling requirements.

[0043] The sampling module is also used to sample the variable factors according to the sampling model and the sampling process, and output the analysis results corresponding to the vehicle body modality discipline.

[0044] Furthermore, to achieve the above objectives, the present invention also proposes a post-processing device for the vehicle body modality discipline, the post-processing device for the vehicle body modality discipline comprising: a memory, a processor, and a post-processing program for the vehicle body modality discipline stored in the memory and executable on the processor, the post-processing program for the vehicle body modality discipline being configured to implement the post-processing method for the vehicle body modality discipline as described above.

[0045] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing a post-processing program for the vehicle body modality discipline, wherein the post-processing program for the vehicle body modality discipline, when executed by a processor, implements the post-processing method for the vehicle body modality discipline as described above.

[0046] This invention obtains a finite element model of the vehicle body and constructs a modal analysis model based on it. Finite element analysis is then performed on the modal analysis model to obtain baseline analysis results. Variable factors are determined based on the baseline analysis results. A sampling model corresponding to the modal discipline of the vehicle body is built based on the variable factors. A sampling process is set according to the performance values ​​and preset sampling requirements corresponding to the modal discipline. The variable factors are sampled according to the sampling model and the sampling process, and the analysis results corresponding to the modal discipline of the vehicle body are output. Through this method, the sampling model automatically performs post-processing on the baseline analysis results of the modal discipline of the vehicle body, avoiding the high labor costs and high error rates caused by manual data post-processing. This provides data support for multi-disciplinary lightweight optimization and improves the optimization efficiency of multi-disciplinary lightweighting. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the structure of the post-processing device in the vehicle body modality discipline of the hardware operating environment involved in the embodiments of the present invention;

[0048] Figure 2 This is a flowchart illustrating the first embodiment of the post-processing method in the field of vehicle body modality of the present invention;

[0049] Figure 3 This is a flowchart illustrating the second embodiment of the post-processing method in the field of vehicle body modality of the present invention;

[0050] Figure 4 This is a structural block diagram of the first embodiment of the post-processing device of the vehicle body modal discipline of the present invention.

[0051] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0052] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0053] Reference Figure 1, Figure 1 This is a schematic diagram of the structure of the post-processing device in the vehicle body modal discipline of the hardware operating environment involved in the embodiments of the present invention.

[0054] like Figure 1 As shown, the post-processing equipment for this vehicle body modal discipline may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk storage device. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.

[0055] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the after-processing equipment of the vehicle body modal discipline, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0056] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a post-processing program for vehicle body modal disciplines.

[0057] exist Figure 1 In the post-processing device for the vehicle body modal discipline shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the post-processing device for the vehicle body modal discipline of the present invention can be set in the post-processing device for the vehicle body modal discipline. The post-processing device for the vehicle body modal discipline calls the post-processing program for the vehicle body modal discipline stored in the memory 1005 through the processor 1001 and executes the post-processing method for the vehicle body modal discipline provided in the embodiment of the present invention.

[0058] This invention provides a post-processing method for vehicle body modal analysis, referring to... Figure 2 , Figure 2This is a flowchart illustrating the first embodiment of the post-processing method for the vehicle body modal discipline of the present invention.

[0059] In this embodiment, the post-processing method of the vehicle body modal discipline includes the following steps:

[0060] Step S10: Obtain the finite element model of the vehicle body, and construct the vehicle body modal calculation model based on the finite element model of the vehicle body.

[0061] It should be understood that the execution subject of this embodiment is the post-processing device of the vehicle body modal discipline. The post-processing device of the vehicle body modal discipline can be a computer, processor, server, or other devices with the same or similar functions. This embodiment does not limit it.

[0062] It should be noted that the vehicle body finite element model includes the body-in-white model, the windshield model, the rear windshield model, and the D-pillar glass finite element network model. In the vehicle body finite element model, a vehicle body modal condition is established, the solution frequency range is set to 0-100Hz, the solver card is set to SOL103, and in Analysis—Control cards—PARAM—AUTOSPC and POST are checked with default values. The output results are set to displacement and strain energy, resulting in the vehicle body modal calculation model, represented as a *.fem file.

[0063] Furthermore, prior to step S10, the method further includes: selecting a multidisciplinary performance dimension to be optimized, wherein the multidisciplinary performance dimension includes at least the vehicle body modal discipline; determining the performance conditions and key performance items corresponding to the multidisciplinary performance dimensions respectively, wherein the performance condition corresponding to the vehicle body modal discipline is the vehicle body modal condition, and the key performance items corresponding to the vehicle body modal discipline include the vehicle body torsional mode and the vehicle body bending mode.

[0064] It should be understood that, based on the purpose of multidisciplinary optimization and the performance dimensions that are strongly related to the vehicle body structure, the multidisciplinary performance dimensions that need to be optimized are selected, and the performance conditions and key performance items that need to be examined are identified from the selected performance disciplines. Specifically, for the purpose of lightweighting, the multidisciplinary performance dimensions of NVH, body stiffness, body modal analysis, and safety crash testing are selected.

[0065] It should be noted that after selecting the multidisciplinary performance dimension, the whole vehicle CAD model is obtained, and multiple whole vehicle finite element models for the multidisciplinary performance dimension are constructed according to the model building requirements. Optionally, the modeling requirements for the multidisciplinary performance dimensions are different, and differentiated model construction is carried out according to different modeling requirements. Specifically, the whole vehicle finite element model is a loadable calculation model with assigned material thickness and completed necessary connection relationships. Optionally, in this embodiment, calculation models for three major crash conditions—body stiffness, body modality, NVH, and safety FRB / MPD / Side-pole—are built respectively.

[0066] Step S20: Perform finite element analysis based on the vehicle body modal calculation model to obtain the baseline analysis results.

[0067] Understandably, calculations are submitted for the models corresponding to each working condition to obtain baseline analysis results. Furthermore, the baseline analysis results corresponding to the vehicle body modal discipline are represented as *.out result files.

[0068] Step S30: Determine the variable factors based on the benchmark analysis results.

[0069] It should be noted that the variable factors are represented by the markings of each body component.

[0070] Specifically, step S30 includes: acquiring the sensitivity of multiple body parts to torsional modal index, bending modal index and relative mass index respectively; and selecting the parts with sensitivity greater than a preset threshold as variable factors.

[0071] It should be understood that, based on the sensitivity analysis results of various body components to the three indicators of torsional mode, bending mode, and relative mass, appropriate components are selected as variable factors. The preset threshold is a pre-set critical value used to distinguish between high and low sensitivity; 50 components with high sensitivity to torsional mode, bending mode, and relative mass are selected as variable factors from among the various body components.

[0072] Step S40: Construct a sampling model corresponding to the vehicle body modality discipline based on the variable factors.

[0073] It should be noted that in the specific implementation, the *.fem file corresponding to the vehicle body modal calculation model is edited to obtain the variable factors and corresponding thickness information of the original scheme, and a sampling model is obtained. The thickness information is represented in the *Pshell_orig.dat file. The paragraphs in *Pshell_orig.dat are deleted from the *.fem file, and the thickness information input interface is set to obtain the sampling model, represented in the *DOE.fem file.

[0074] Step S50: Set up the sampling process according to the performance values ​​corresponding to the vehicle body modal discipline and the preset sampling requirements.

[0075] It should be understood that, for multidisciplinary performance dimensions, different requirements exist for extracting performance values ​​and designing sampling processes based on different operating conditions. Specifically, in this embodiment, Matlab is used to edit the script of the embedded process to generate a *.m file. Based on the *.m file, thickness updates are performed, outputting the *Pshell_modify.dat file.

[0076] Step S60: Sampling the variable factors according to the sampling model and the sampling process, and outputting the analysis results corresponding to the vehicle body modality discipline.

[0077] It should be noted that this embodiment couples the computational software with multidisciplinary software. The computational software startup statement and computational file name are input, and the computational process is set. The sampling model *DOE.fem file is input as a Project File, forming a complete sample computational model file from the *DOE.fem file and the *Pshell_modify.dat file. The computation is then performed, and the computational results are output as a *.out file for the next step. Specifically, the output module is configured to include torsional modes, bending modes, and vehicle body mass; that is, the analysis results output in this embodiment include torsional modes, bending modes, and the mass of the variable factors under this scheme.

[0078] In the implementation, the calculation results of all samples are summarized. The post-processing module of the multidisciplinary software allows users to view all sample calculation results and supports exporting and saving them in tabular form.

[0079] In this embodiment, based on multidisciplinary optimization of vehicle performance, Matlab is coupled with multidisciplinary optimization software to program and develop automated post-processing scripts. Multidisciplinary optimization sample sampling and calculation processes are embedded to automate the post-processing process, eliminate errors caused by manual operation, and improve optimization efficiency.

[0080] This embodiment obtains a finite element model of the vehicle body and constructs a modal analysis model based on it. Finite element analysis is performed on the modal analysis model to obtain baseline analysis results. Variable factors are determined based on the baseline analysis results. A sampling model corresponding to the modal discipline of the vehicle body is built based on the variable factors. A sampling process is set according to the performance values ​​and preset sampling requirements corresponding to the modal discipline. The variable factors are sampled according to the sampling model and the sampling process, and the analysis results corresponding to the modal discipline of the vehicle body are output. Through this method, the sampling model automatically performs post-processing on the baseline analysis results of the modal discipline of the vehicle body, avoiding the high labor costs and high error rates caused by manual data post-processing. This provides data support for multi-disciplinary lightweight optimization and improves the optimization efficiency of multi-disciplinary lightweighting.

[0081] refer to Figure 3 , Figure 3 This is a flowchart illustrating the second embodiment of the post-processing method for the vehicle body modal discipline of the present invention.

[0082] Based on the first embodiment described above, step S40 of the post-processing method for vehicle body modal analysis in this embodiment includes:

[0083] Step S401: Parse the file corresponding to the vehicle body modal calculation model to determine the thickness information paragraph corresponding to the variable factor.

[0084] Step S402: Delete the thickness information paragraph in the file corresponding to the vehicle body modal calculation model, and set the thickness information paragraph input interface to obtain the sampling model corresponding to the vehicle body modal discipline.

[0085] It should be understood that the *.fem file of the vehicle body modal calculation model is edited to determine the thickness information paragraphs corresponding to the variable factors in the original scheme. The *.fem file is opened, the thickness information paragraphs for the variable factors are deleted, and the thickness information paragraph input interface is set to "include '*Pshell_modify.dat'", meaning the *Pshell_modify.dat file replaces the thickness information paragraphs in the model file, and this is saved as the sampling model *DOE.fem file. The *Pshell_modify.dat file is the output file that implements the changes to the variable factor names and thicknesses based on the original variable factor thickness information paragraph file and script file.

[0086] Further, step S60 includes: editing the script embedded in the sampling process using simulation software to obtain a sampling thickness information file; generating a target sampling model based on the sampling thickness information file and the sampling model; sampling the variable factors according to the target sampling model and the sampling process, and outputting the analysis results corresponding to the vehicle body modality discipline.

[0087] In this implementation, the simulation software is Matlab. Matlab is used to edit the stirring process, generating a sampling thickness information file (*.m file). Based on the *.m file, the thickness is updated, and the resulting sampling thickness information file (*Pshell_modify.dat) is output. The data in the *Pshell_modify.dat file is then input into the sampling model file (*DOE.fem file) via an input interface to form a complete sampling model. Sampling is then performed through calculations, and the analysis results are output.

[0088] Furthermore, after step S30, the method further includes: recording variables according to the variable factors and generating corresponding files to be processed;

[0089] The step of using simulation software to edit the script embedded in the sampling process to obtain a sampling thickness information file includes: defining variable identifiers and modifying variable names in the file to be processed to generate an adjustment file; and changing the variable factor thickness based on the adjustment file to generate a sampling thickness information file.

[0090] It should be understood that after selecting the variable factors, variables are recorded based on these factors. Specifically, the value of each variable factor is set according to the selectable thickness, and the processed files are output as DVfile and *.ansa files. The selectable thickness is, for example, 0.5–3 mm.

[0091] It should be noted that due to file format limitations, the vehicle body modal cannot be directly output as a sampled original model. Therefore, a script file is needed for variable model processing. Specifically, based on the recorded variable names and variable factors, variable identifiers (IDs) are defined and variable names in the sample model are modified, generating the *modify1.m script file. Following the recorded variable names and variable factors, and combining the functionality of the *modify1.m file, variable IDs are defined to achieve thickness changes for variable factors, generating the *modify2.m script file.

[0092] In the specific implementation, the *modify1.m script file defines variable identifiers and modifies variable names for the file to be processed, generating an adjustment file. The *modify2.m script file then changes the variable factor thickness in the adjustment file, generating a sampling thickness information file, represented as the *Pshell_modify.dat file.

[0093] Furthermore, after step S401, the method further includes: storing the thickness information segment as an initial file;

[0094] The step of defining variable identifiers and modifying variable names in the file to be processed to generate an adjustment file includes: defining variable identifiers and modifying variable names based on the first simulation software to generate an adjustment file, and outputting the adjustment file to the second simulation software, wherein the first simulation software is associated with the file to be processed;

[0095] The step of changing the variable factor thickness based on the adjustment file to generate a sampling thickness information file includes: inputting the initial file into the second simulation software in a preset format; and using the second simulation software to change the variable factor thickness based on the adjustment file and the initial file to generate a sampling thickness information file.

[0096] It should be noted that when opening the *.fem file of the vehicle body modal calculation model, only the thickness information section of the variable factors is retained and saved as the initial file *Pshell_orig.dat. In this embodiment, two Matlab software programs are coupled into the workspace to update the variable factor names and thicknesses of the vehicle body modality according to the sampling scheme. Specifically, the first Matlab is associated with the *modify1.m script file to define variable IDs and modify variable names, and the processing results are transferred to the second Matlab along with the local *modify2.m file of the same name. The second Matlab is associated with the *modify2.m script file, and the pre-stored initial file *Pshell_orig.dat is input in support file format to update the thickness of the variable factors, outputting the sampling thickness information file *Pshell_modify.dat. The data in the *Pshell_modify.dat file is input into the sampling model *DOE.fem file through the input interface to form a complete sampling model, and the sampling is completed through calculation, outputting the analysis results.

[0097] This embodiment acquires a finite element model of the vehicle body and constructs a vehicle body modal calculation model based on the finite element model. Finite element analysis is performed on the vehicle body modal calculation model to obtain benchmark analysis results. Variable factors are determined based on the benchmark analysis results. The file corresponding to the vehicle body modal calculation model is parsed to determine the thickness information segments corresponding to the variable factors. The thickness information segments in the file corresponding to the vehicle body modal calculation model are deleted, and an input interface for the thickness information segments is set to obtain the sampling model corresponding to the vehicle body modal discipline. A sampling process is set according to the performance values ​​corresponding to the vehicle body modal discipline and preset sampling requirements. The variable factors are sampled according to the sampling model and the sampling process, and the analysis results corresponding to the vehicle body modal discipline are output. Through the above method, the file of the vehicle body modal calculation model is parsed, the thickness information segments of the variable factors are deleted, and an input interface is set to generate a sampling model. This solves the problem that the current vehicle body modal calculation model cannot directly output a sampling model due to the file format. The sampling model automatically performs post-processing on the benchmark analysis results of the vehicle body modal discipline, avoiding the high labor costs and high error rates caused by manual data post-processing. This provides data support for multi-disciplinary lightweight optimization and improves the optimization efficiency of multi-disciplinary lightweighting.

[0098] Furthermore, this embodiment of the invention also proposes a storage medium storing a post-processing program for the vehicle body modality discipline. When the post-processing program for the vehicle body modality discipline is executed by a processor, it implements the post-processing method for the vehicle body modality discipline as described above.

[0099] Since this storage medium adopts all the technical solutions of all the above embodiments, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be repeated here.

[0100] Reference Figure 4 , Figure 4 This is a structural block diagram of the first embodiment of the post-processing device of the vehicle body modal discipline of the present invention.

[0101] like Figure 4 As shown, the post-processing device for vehicle body modal analysis proposed in this embodiment of the invention includes:

[0102] The acquisition module 10 is used to acquire the vehicle body finite element model and construct the vehicle body modal calculation model based on the vehicle body finite element model.

[0103] The finite element analysis module 20 is used to perform finite element analysis based on the vehicle body modal calculation model to obtain the benchmark analysis results.

[0104] The determination module 30 is used to determine variable factors based on the benchmark analysis results.

[0105] Sampling module 40 is used to build a sampling model corresponding to the vehicle body modality discipline based on the variable factors;

[0106] The sampling module 40 is also used to set up a sampling process based on the performance values ​​corresponding to the vehicle body modal discipline and preset sampling requirements.

[0107] The sampling module 40 is also used to sample the variable factors according to the sampling model and the sampling process, and output the analysis results corresponding to the vehicle body modality discipline.

[0108] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solutions of the present invention. In specific applications, those skilled in the art can make settings as needed, and the present invention does not impose any restrictions on this.

[0109] This embodiment obtains a finite element model of the vehicle body and constructs a modal analysis model based on it. Finite element analysis is performed on the modal analysis model to obtain baseline analysis results. Variable factors are determined based on the baseline analysis results. A sampling model corresponding to the modal discipline of the vehicle body is built based on the variable factors. A sampling process is set according to the performance values ​​and preset sampling requirements corresponding to the modal discipline. The variable factors are sampled according to the sampling model and the sampling process, and the analysis results corresponding to the modal discipline of the vehicle body are output. Through this method, the sampling model automatically performs post-processing on the baseline analysis results of the modal discipline of the vehicle body, avoiding the high labor costs and high error rates caused by manual data post-processing. This provides data support for multi-disciplinary lightweight optimization and improves the optimization efficiency of multi-disciplinary lightweighting.

[0110] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this invention. In practical applications, those skilled in the art can select some or all of the workflow to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0111] In addition, for technical details not described in detail in this embodiment, please refer to the post-processing method of vehicle body modality provided in any embodiment of the present invention, which will not be repeated here.

[0112] In one embodiment, the sampling module 40 is further configured to parse the file corresponding to the vehicle body modal calculation model, determine the thickness information paragraph corresponding to the variable factor, delete the thickness information paragraph in the file corresponding to the vehicle body modal calculation model, and set the thickness information paragraph input interface to obtain the sampling model corresponding to the vehicle body modal discipline.

[0113] In one embodiment, the sampling module 40 is further configured to use simulation software to edit the script embedded in the sampling process to obtain a sampling thickness information file; generate a target sampling model based on the sampling thickness information file and the sampling model; sample the variable factors according to the target sampling model and the sampling process, and output the analysis results corresponding to the vehicle body modality discipline.

[0114] In one embodiment, the determining module 30 is further configured to record variables based on the variable factors and generate corresponding files to be processed;

[0115] The sampling module 40 is also used to define variable identifiers and modify variable names in the file to be processed, and generate an adjustment file; and to change the variable factor thickness based on the adjustment file, and generate a sampling thickness information file.

[0116] In one embodiment, the sampling module 40 is further configured to store the thickness information segment as an initial file;

[0117] The sampling module 40 is also used to define variable identifiers and modify variable names based on the first simulation software, generate an adjustment file, and output the adjustment file to the second simulation software, wherein the first simulation software is associated with the file to be processed;

[0118] The sampling module 40 is further configured to input the initial file into the second simulation software in a preset format; and to generate a sampling thickness information file by changing the variable factor thickness based on the adjustment file and the initial file through the second simulation software.

[0119] In one embodiment, the determining module 30 is further configured to acquire the sensitivity of multiple vehicle body parts to torsional modal indicators, bending modal indicators and relative mass indicators respectively; and select the parts whose sensitivity is greater than a preset threshold as variable factors.

[0120] In one embodiment, the post-processing device for the vehicle body modal discipline further includes a selection module;

[0121] The selection module is used to select the multidisciplinary performance dimension to be optimized, the multidisciplinary performance dimension including at least the vehicle body modal discipline; and to determine the performance conditions and key performance items corresponding to the multidisciplinary performance dimensions respectively, wherein the performance condition corresponding to the vehicle body modal discipline is the vehicle body modal condition, and the key performance items corresponding to the vehicle body modal discipline include the vehicle body torsional mode and the vehicle body bending mode.

[0122] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0123] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0124] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0125] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A post-processing method for vehicle body modal analysis, characterized in that, The post-processing methods of the vehicle body modal discipline include: Obtain the finite element model of the vehicle body, and construct the vehicle body modal calculation model based on the finite element model of the vehicle body; Finite element analysis was performed based on the vehicle body modal calculation model to obtain the baseline analysis results; The variable factors are determined based on the benchmark analysis results; Based on the aforementioned variable factors, a sampling model corresponding to the vehicle body modality discipline is constructed. The sampling process is set up according to the performance values ​​corresponding to the vehicle body modal discipline and the preset sampling requirements. According to the sampling model and the sampling process, the variable factors are sampled, and the analysis results corresponding to the vehicle body modality are output. The step of constructing a sampling model corresponding to the vehicle body modality discipline based on the variable factors includes: The file corresponding to the vehicle body modal calculation model is parsed to determine the thickness information paragraphs corresponding to the variable factors; The thickness information paragraph in the file corresponding to the vehicle body modal calculation model is deleted, and the thickness information paragraph input interface is set to obtain the sampling model corresponding to the vehicle body modal discipline. The step of sampling the variable factors according to the sampling model and the sampling process, and outputting the analysis results corresponding to the vehicle body modality discipline, includes: The script embedded in the sampling process is edited using simulation software to obtain a sampling thickness information file; A target sampling model is generated based on the sampling thickness information file and the sampling model. According to the target sampling model and the sampling process, the variable factors are sampled, and the analysis results corresponding to the vehicle body modality are output. After determining the variable factors based on the benchmark analysis results, the method further includes: Record variables based on the variable factors and generate corresponding files to be processed; The process of editing the script embedded in the sampling process using simulation software to obtain a sampling thickness information file includes: Define variable identifiers and modify variable names in the file to be processed to generate an adjustment file; Based on the adjustment file, the thickness of the variable factor is changed, and a sampling thickness information file is generated. After parsing the file corresponding to the vehicle body modal calculation model and determining the thickness information paragraphs corresponding to the variable factors, the method further includes: Store the thickness information segment as an initial file; The step of defining variable identifiers and modifying variable names in the file to be processed to generate an adjustment file includes: Based on the first simulation software, variable identifiers are defined and variable names are modified to generate an adjustment file, and the adjustment file is output to the second simulation software. The first simulation software is associated with the file to be processed. The process of changing the variable factor thickness based on the adjustment file and generating a sampling thickness information file includes: The initial file is input into the second simulation software in a preset format; The second simulation software performs variable factor thickness changes based on the adjustment file and the initial file to generate a sampling thickness information file.

2. The post-processing method for vehicle body modal analysis as described in claim 1, characterized in that, The determination of variable factors based on the benchmark analysis results includes: The sensitivity of multiple body parts to torsional modal parameters, bending modal parameters, and relative mass parameters was obtained. The components whose sensitivity is greater than a preset threshold are selected as variable factors.

3. The post-processing method for vehicle body modal analysis as described in claim 1, characterized in that, Before obtaining the finite element model of the vehicle body and constructing the modal calculation model of the vehicle body based on the finite element model, the method further includes: Select the multidisciplinary performance dimension to be optimized, wherein the multidisciplinary performance dimension includes at least the vehicle body modality discipline; The performance conditions and key performance items corresponding to the multidisciplinary performance dimensions are determined respectively. The performance condition corresponding to the vehicle body modal discipline is the vehicle body modal condition, and the key performance items corresponding to the vehicle body modal discipline include the vehicle body torsional mode and the vehicle body bending mode.

4. A post-processing apparatus for performing the post-processing method of the vehicle body modal discipline as described in any one of claims 1 to 3, characterized in that, The post-processing device for the vehicle body modal analysis includes: The acquisition module is used to acquire the finite element model of the vehicle body and construct a modal calculation model of the vehicle body based on the finite element model of the vehicle body. The finite element analysis module is used to perform finite element analysis based on the vehicle body modal calculation model to obtain benchmark analysis results. The determination module is used to determine variable factors based on the benchmark analysis results; The sampling module is used to build a sampling model corresponding to the vehicle body modality discipline based on the variable factors. The sampling module is also used to set up a sampling process based on the performance values ​​corresponding to the vehicle body modal discipline and preset sampling requirements. The sampling module is also used to sample the variable factors according to the sampling model and the sampling process, and output the analysis results corresponding to the vehicle body modality discipline.

5. A post-processing device for vehicle body modal analysis, characterized in that, The device includes: a memory, a processor, and a post-processing program for the vehicle body modality stored in the memory and executable on the processor, the post-processing program for the vehicle body modality being configured to implement the post-processing method for the vehicle body modality as described in any one of claims 1 to 3.

6. A storage medium, characterized in that, The storage medium stores a post-processing program for the vehicle body modality discipline, which, when executed by a processor, implements the post-processing method for the vehicle body modality discipline as described in any one of claims 1 to 3.

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