CN111125946A - Method for optimizing structure of boarding body based on MDO technology - Google Patents
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Abstract
The invention relates to a method for optimizing a boarding body structure based on an MDO technology, which comprises the following steps: step one, establishing a finite element model of the vehicle body to realize multidisciplinary performance simulation analysis of the vehicle body; establishing a parameterized model based on the upper vehicle body joint cavity and the material thickness of the key sheet metal part of the upper vehicle body as design variables; step three, according to the step two, a simulation optimization flow based on the upper vehicle body joint cavity and the material thickness of the key sheet metal part as design variables is established; step four, according to the step three, DOE sampling calculation of each design variable is carried out; step five, constructing a response surface approximate model meeting the precision requirement according to the DOE sampling calculation result in the step four; and step six, according to the response surface approximate model in the step five, taking the limit value of the multidisciplinary performance index of the vehicle body as a constraint condition, taking the lightest weight of the vehicle body as an optimization target to carry out optimization design and obtain an optimization scheme. The invention can quickly find out the lightweight optimization scheme meeting the multidisciplinary performance requirement of the vehicle body.
Description
Technical Field
The invention relates to the technical field of automobile CAE simulation, in particular to a method for optimizing a structure of a loading body based on an MDO technology.
Background
The CAE simulation technology of various current large automobile production enterprises is quite mature, the simulation precision of a single subject is increasingly improved, and the level of simulation replaceable tests is gradually realized. Although the traditional single-subject simulation analysis can enable the performance of each subject to be optimal through an advanced optimization method and means, the coupling and restriction relation among the performances is not fully considered, and the optimization scheme lacks of global property. Therefore, in order to overcome the defect of single-subject optimization design, the vehicle body structure optimization method based on the MDO technology is continuously developed and advanced.
The upper vehicle body is an important component of the vehicle body, and the vehicle body structure form and the material thickness distribution of sheet metal parts directly influence the performance and the light weight level of the vehicle body. Take a certain SUV motorcycle type to go up the automobile body as an example, its structure includes: the upper joint of the column A, the upper joint of the column B, the upper joint of the column C, the upper joint of the column D, the top cover beam, the side wall and the like. The body performance includes body rigidity, body mode, body torsional fatigue, body mounting point rigidity, etc., and these performance indexes may be interrelated or mutually exclusive, that is, when one performance reaches the optimum, other performances are weakened. Therefore, the traditional single-subject simulation analysis optimization method is used, the performance requirements of various subjects are met, the situations of excessive vehicle body performance and heavy vehicle body are brought, and the development period and the development cost of the vehicle body are greatly challenged.
Disclosure of Invention
The invention aims to provide an optimization method of a vehicle loading body structure based on an MDO technology, so as to quickly find out a lightweight optimization scheme meeting the multidisciplinary performance requirements of a vehicle body.
The invention relates to a method for optimizing a boarding body structure based on an MDO technology, which comprises the following steps:
step one, establishing a finite element model of a vehicle body, and realizing the simulation analysis of the multidisciplinary performance of the vehicle body, wherein the multidisciplinary performance of the vehicle body comprises the rigidity of the vehicle body, the mode of the vehicle body, the torsional fatigue of the vehicle body and the rigidity of a mounting point of the vehicle body;
establishing a parameterized model based on the upper vehicle body joint cavity and the material thickness of the key sheet metal part of the upper vehicle body as design variables;
step three, according to the parameterized model in the step two, a simulation optimization flow based on the upper vehicle body joint cavity and the material thickness of the key sheet metal part as design variables is established, and the output items are the bending rigidity value of the vehicle body, the bending modal frequency of the vehicle body, the fatigue damage value of the vehicle body and the rigidity value of each mounting point of the vehicle body;
step four, according to the simulation optimization process in the step three, DOE sampling calculation of each design variable is carried out;
step five, constructing a response surface approximate model meeting the precision requirement according to the DOE sampling calculation result in the step four;
and step six, according to the response surface approximate model in the step five, taking the limit value of the multidisciplinary performance index of the vehicle body as a constraint condition, taking the lightest weight of the vehicle body as an optimization target to carry out optimization design and obtain an optimization scheme.
Further, the step one is specifically as follows: establishing a finite element model of the vehicle body by adopting Hypermesh pretreatment software; respectively establishing corresponding SPC (statistical process control) and MPC (process control computer) constraints based on the vehicle body rigidity analysis working condition, applying corresponding load excitation, setting the vehicle body structure displacement response output, and realizing the CAE (vehicle body rigidity) simulation; establishing a free modal boundary condition based on the modal analysis working condition of the vehicle body, extracting modal frequency of the vehicle body, automatically extracting modal order and modal frequency through Matlab development, and outputting a correlation value (MAC) to realize modal self-identification; based on the torsional fatigue analysis working condition of the vehicle body, adopting an inertial release definition model to restrain, calculating the stress of each node of the vehicle body through an NASTRAN solving sequence, and calculating the fatigue damage value of each node of the vehicle body through Femfat software; based on the rigidity analysis working condition of the vehicle body mounting point, corresponding SPC constraints are established, load excitation under a local coordinate system is applied to the center of each mounting hole, displacement response output of each loading point is set, and CAE simulation of the rigidity of the vehicle body mounting point is achieved.
Further, the second step is specifically as follows: and establishing a parameterized model based on the material thicknesses of the upper vehicle body joint cavity and the upper vehicle body key sheet metal part as design variables by adopting preprocessing ANSA software, updating the material thicknesses of the upper vehicle body joint cavity and the upper vehicle body key sheet metal part driven by parameters, and outputting a corresponding vehicle body finite element analysis model.
Further, the third step is specifically: according to the second parameterized model, respectively completing the simulation analysis integration of the rigidity of the vehicle body, the mode of the vehicle body, the torsional fatigue of the vehicle body and the rigidity of the mounting points of the vehicle body and the post-processing integration of simulation results based on commercial integrated simulation optimization software Optimus, building a simulation optimization flow based on the upper vehicle body joint cavity and the material thickness of key sheet metal parts as design variables, and outputting items including the bending and twisting rigidity value of the vehicle body, the bending and twisting modal frequency of the vehicle body, the fatigue damage value of the vehicle body and the rigidity value of each mounting point of the vehicle body.
Further, the fourth step is specifically: parameter change test combinations are randomly generated in the value ranges of various design variables through Matlab development, and DOE sampling calculation of the multidisciplinary performance of the vehicle body is carried out on Optimus software in a form of Table test combinations.
Further, the step five specifically comprises: and according to the DOE sampling calculation result in the fourth step, constructing a response surface approximate model by adopting a least square method and a Taylor linear equation expansion, then verifying whether the precision of the response surface approximate model meets the requirement, if not, increasing the sample points of DOE sampling calculation, and updating the response surface approximate model until the precision meets the requirement.
The invention discloses a method for optimizing a boarding body structure based on an MDO (minimization of drive tests) technology, which comprises the steps of firstly, realizing the accurate simulation of the multidisciplinary performances of the rigidity of a vehicle body, the mode of the vehicle body, the torsional fatigue of the vehicle body and the rigidity of a vehicle body mounting point by a CAE (computer aided engineering) finite element simulation technology; then, screening parameter factors influencing various performances in the multidisciplinary performances as design variables, and carrying out parametric modeling; then building a multidisciplinary performance integration MDO workflow, performing multidisciplinary performance DOE simulation analysis, and building a response surface approximate model to replace a high-time-consumption CAE finite element analysis model; and then, performing optimization design by taking the multidisciplinary performance indexes as constraint conditions and taking the lightest weight of the vehicle body as an optimization target to obtain an optimal solution which meets the performance requirements of various disciplines and has the highest light weight level. And finally substituting the optimal solution into a real CAE simulation model for verification to obtain a final optimization scheme.
The invention has the advantages that: by establishing the upper vehicle body structure optimization parameterization integrated model, the interaction relation among different disciplines can be fully considered, the repeated optimization and verification processes of the performance of different disciplines in the design process are effectively reduced, and the problem of high time consumption caused by the performance simulation of the traditional vehicle body is solved, so that the aims of shortening the design development period and improving the product design quality are fulfilled; the automobile body can be lightened under the condition of meeting the requirement of the multidisciplinary performance of the automobile body, and the aims of avoiding the excessive performance design of the automobile body and reducing the development cost of products are fulfilled.
Drawings
FIG. 1 is a flow chart of a method for optimizing a boarding body structure based on MDO technology;
FIG. 2 is a schematic diagram of design variables of upper vehicle body cavity and sheet metal part material thickness
FIG. 3 is a working flow chart of the upper vehicle body structure optimization MDO;
FIG. 4 is a graph of response surface model accuracy analysis;
fig. 5 is a table of the optimum design solutions satisfying the subject performance constraints and having the lightest vehicle body weight in the present example.
Detailed Description
The present invention will be further described below by taking a certain SUV model as an example.
Fig. 1 shows a method for optimizing a boarding body structure based on an MDO technology, which includes the following steps:
step one, establishing a finite element model of a vehicle body, and realizing the simulation analysis of the multidisciplinary performance of the vehicle body, wherein the multidisciplinary performance of the vehicle body comprises the rigidity of the vehicle body, the mode of the vehicle body, the torsional fatigue of the vehicle body and the rigidity of a mounting point of the vehicle body;
specifically, based on a certain SUV model, a finite element model of the vehicle body is established by adopting Hypermesh preprocessing software;
respectively establishing corresponding SPC (statistical process control) and MPC (dynamic control) constraints based on the rigidity analysis working condition of the vehicle body, applying corresponding load excitation, setting a structural displacement response measuring point of the vehicle body and enabling the structural displacement response measuring point to pass through, outputting a response value by a pch (personal computer) file, and realizing the CAE (computer aided engineering) simulation of the rigidity of the vehicle body, wherein the torsional rigidity of the vehicle body is 912 KN x m/rad and the bending rigidity is 11466N/m in an initial design state;
establishing a free modal analysis constraint condition based on the modal analysis working condition of the vehicle body, extracting the modal frequency of the vehicle body from 0Hz to 70Hz, and under the initial design state, the first-order torsional frequency of the vehicle body is 33.8Hz, and the first-order bending modal frequency is 45.06 Hz; outputting a pch file, and automatically extracting the vehicle body modal order, modal frequency and correlation value (MAC) under Matlab development to realize modal autonomous identification;
based on the torsional fatigue analysis working condition of the vehicle body, defining model constraint by adopting inertia release; firstly, in Hyperview software, reading an op2 file generated by solving NASTRAN, identifying and recording the number of a critical node; then, calculating 10 ten thousand accumulated fatigue damage values by automatically calling Femfat software, and outputting a dma file; finally, automatically outputting the damage value of the critical node and the number of all nodes with the damage value larger than 0.2, the node number and the damage value through a secondary development program DmaFile _ read.exe; in an initial design state, 3 critical and excessive points of fatigue damage are provided, and the damage values are 0.38/0.37/0.24 respectively;
based on the rigidity analysis working condition of the vehicle body mounting point, respectively establishing SPC constraints, applying load excitation under a local coordinate system at each mounting point, setting each loading point to form a displacement response measuring point, and outputting a response value through a pch file to realize the CAE simulation of the rigidity of the vehicle body in white at the mounting point. In the initial design state, the rigidity values of the respective mounting points of the vehicle body are shown in table 1 in fig. 4.
Secondly, optimizing and parameterizing the structure of the upper vehicle body, and establishing a parameterization model based on the upper vehicle body joint cavity and the material thickness of the key sheet metal part of the upper vehicle body as design variables;
specifically, a pre-processing ANSA software is adopted to establish a parameterized model based on the material thickness of the upper vehicle body joint cavity and the upper vehicle body key sheet metal part as design variables, so that the material thickness of the upper vehicle body joint cavity and the upper vehicle body key sheet metal part driven by parameters is updated, and a corresponding vehicle body finite element analysis model is output. In order to identify the influence degree of the upper vehicle body on the white vehicle body rigidity, the mode, the torsional fatigue and the mounting point rigidity performance, the material thickness of the upper vehicle body joint cavity and the upper vehicle body key sheet metal part is selected as a design variable for optimizing the upper vehicle body structure. Wherein, the screening process of getting on the automobile body and connecting the cavity is: selecting side wall upper cross beam width and height direction cross sections with larger influence on various performances of the vehicle body, column A and column B inner plate Y direction cross sections, top cover rear cross beam X and Z direction cross section areas and the like (by stretching or compressing the cavity cross sections along respective directions, the cavity volume is increased or reduced); the screening process of the key sheet metal parts of the upper vehicle body comprises the following steps: selecting the sheet metal part thicknesses of the areas such as the A column, the B column, the side wall, the back door frame and the like which have larger influence on the rigidity performance of the vehicle body, the mode, the torsional fatigue and the mounting point rigidity performance, wherein the specific design variables in the embodiment are shown in figure 2.
Step three, according to the parameterized model in the step two, a simulation optimization flow based on the upper vehicle body joint cavity and the material thickness of the key sheet metal part as design variables is established, and the output items are the bending rigidity value of the vehicle body, the bending modal frequency of the vehicle body, the fatigue damage value of the vehicle body and the rigidity value of each mounting point of the vehicle body;
specifically, as shown in fig. 3, according to the second step, a txt file and a parameterized model, which take the thicknesses of the above vehicle body joint cavity and the key sheet metal part as design variables, are output, the two output files are used as the input of the integrated simulation optimization software Optimus, the vehicle body rigidity, the vehicle body mode and the vehicle body mounting point rigidity are used as analysis working conditions, displacement and mode response measuring points are set and pass through the analysis working conditions, and then corresponding vehicle body rigidity, vehicle body mode and vehicle body mounting point rigidity simulation values are automatically output through an editing and calculating formula; and (3) taking the torsional fatigue of the vehicle body as an analysis working condition, converting the result file dma into the txt file for output through a secondary development program, realizing automatic output of the critical point damage value, and completing the construction of the multidisciplinary performance simulation analysis flow of the upper vehicle body.
Step four, according to the simulation optimization process in the step three, DOE sampling calculation of each design variable is carried out;
specifically, txt file parameter change test combinations are randomly generated in the value ranges of design variables of an upper vehicle body joint cavity and the thickness of a key sheet metal part through Matlab development, the number of samples in the case is 50, multi-disciplinary performance DOE sampling calculation is carried out on Optimus software in a Table test combination mode, and simulation integration analysis of the multi-disciplinary performance of the upper vehicle body is completed based on sampling results.
Step five, constructing a response surface approximate model meeting the precision requirement according to the DOE sampling calculation result in the step four;
specifically, according to the data of the calculation results of 50 groups of DOE sampling in the fourth step, a least square method is adopted, a Taylor linear equation is expanded to construct a response surface approximate model, then whether the precision of the response surface approximate model meets the requirement is verified, if not, the sample points calculated by the DOE sampling are increased, and the response surface approximate model is updated until the precision meets the requirement.
In this case, the response surface approximate model precision verification process is as follows: adding 10 groups of DOE sample points, verifying the precision of the response surface approximate model constructed by the original 50 groups of D0E sample points, and performing error analysis on the result of the added sample points and the result of the response surface approximate model to verify that the maximum error is 2.4%, so that the requirement that the precision is more than or equal to 95% is met, the sample points calculated by DOE sampling do not need to be added, and the error analysis result is shown in FIG. 4.
And sixthly, carrying out optimization iteration according to the response surface approximate model in the fifth step, taking the limit value of each vehicle body multidisciplinary performance index as a constraint condition, wherein the vehicle body performance comprises vehicle body rigidity, vehicle body mode, vehicle body torsional fatigue and vehicle body mounting point rigidity, and carrying out optimization design and obtaining an optimization scheme by taking the lightest vehicle body weight as an optimization target. And finally substituting the optimization scheme into the finite element model of the vehicle body for verification, checking whether the difference between the simulation result and the response surface approximate model optimization result reaches the standard, returning to the fourth step if the difference does not reach the standard, properly increasing the design space of the design variables, and carrying out optimization design again. The optimal design scheme for the vehicle body that satisfies the subject performance constraints and is the lightest in weight in this example is shown in table 1 in fig. 5, and it can be seen from table 1 that: the initial design scheme of all the performances of the car body meets the requirements except that the fatigue damage value does not meet the requirements, all the performances meet the requirements after the MDO technical structure is optimally designed, the weight of the car body is reduced from 389.3kg to 387.6kg, and the weight of the car body is reduced by 1.7 kg.
Claims (6)
1. A method for optimizing a structure of a boarding body based on an MDO technology is characterized by comprising the following steps:
step one, establishing a finite element model of a vehicle body, and realizing the simulation analysis of the multidisciplinary performance of the vehicle body, wherein the multidisciplinary performance of the vehicle body comprises the rigidity of the vehicle body, the mode of the vehicle body, the torsional fatigue of the vehicle body and the rigidity of a mounting point of the vehicle body;
establishing a parameterized model based on the upper vehicle body joint cavity and the material thickness of the key sheet metal part of the upper vehicle body as design variables;
step three, according to the parameterized model in the step two, a simulation optimization flow based on the upper vehicle body joint cavity and the material thickness of the key sheet metal part as design variables is established, and the output items are the bending rigidity value of the vehicle body, the bending modal frequency of the vehicle body, the fatigue damage value of the vehicle body and the rigidity value of each mounting point of the vehicle body;
step four, according to the simulation optimization process in the step three, DOE sampling calculation of each design variable is carried out;
step five, constructing a response surface approximate model meeting the precision requirement according to the DOE sampling calculation result in the step four;
and step six, according to the response surface approximate model in the step five, taking the limit value of the multidisciplinary performance index of the vehicle body as a constraint condition, taking the lightest weight of the vehicle body as an optimization target to carry out optimization design and obtain an optimization scheme.
2. The method for optimizing the structure of the boarding body based on the MDO technology as claimed in claim 1, wherein the first step is specifically as follows: establishing a finite element model of the vehicle body by adopting Hypermesh pretreatment software; respectively establishing corresponding SPC (statistical process control) and MPC (process control computer) constraints based on the vehicle body rigidity analysis working condition, applying corresponding load excitation, setting the vehicle body structure displacement response output, and realizing the CAE (vehicle body rigidity) simulation; establishing a free modal boundary condition based on the modal analysis working condition of the vehicle body, extracting modal frequency of the vehicle body, automatically extracting modal order and modal frequency through Matlab development, and outputting a correlation value (MAC) to realize modal self-identification; based on the torsional fatigue analysis working condition of the vehicle body, adopting an inertial release definition model to restrain, calculating the stress of each node of the vehicle body through an NASTRAN solving sequence, and calculating the fatigue damage value of each node of the vehicle body through Femfat software; based on the rigidity analysis working condition of the vehicle body mounting point, corresponding SPC constraints are established, load excitation under a local coordinate system is applied to the center of each mounting hole, displacement response output of each loading point is set, and CAE simulation of the rigidity of the vehicle body mounting point is achieved.
3. The method for optimizing the structure of the boarding body based on the MDO technology as claimed in claim 1, wherein the second step is specifically as follows: and establishing a parameterized model based on the material thicknesses of the upper vehicle body joint cavity and the upper vehicle body key sheet metal part as design variables by adopting preprocessing ANSA software, updating the material thicknesses of the upper vehicle body joint cavity and the upper vehicle body key sheet metal part driven by parameters, and outputting a corresponding vehicle body finite element analysis model.
4. The MDO technology-based boarding body structure optimization method according to claim 1, characterized in that the third step specifically comprises: according to the second parameterized model, respectively completing the simulation analysis integration of the rigidity of the vehicle body, the mode of the vehicle body, the torsional fatigue of the vehicle body and the rigidity of the mounting points of the vehicle body and the post-processing integration of simulation results based on commercial integrated simulation optimization software Optimus, building a simulation optimization flow based on the upper vehicle body joint cavity and the material thickness of key sheet metal parts as design variables, and outputting items including the bending and twisting rigidity value of the vehicle body, the bending and twisting modal frequency of the vehicle body, the fatigue damage value of the vehicle body and the rigidity value of each mounting point of the vehicle body.
5. The MDO technology-based boarding body structure optimization method according to claim 1, wherein the fourth step is specifically: parameter change test combinations are randomly generated in the value ranges of various design variables through Matlab development, and DOE sampling calculation of the multidisciplinary performance of the vehicle body is carried out on Optimus software in a form of Table test combinations.
6. The method for optimizing the structure of the boarding body based on the MDO technology according to claim 1, wherein the step five is specifically as follows: and according to the DOE sampling calculation result in the fourth step, constructing a response surface approximate model by adopting a least square method and a Taylor linear equation expansion, then verifying whether the precision of the response surface approximate model meets the requirement, if not, increasing the sample points of DOE sampling calculation, and updating the response surface approximate model until the precision meets the requirement.
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