Vehicle structure simulation optimization method and device and storage medium

Abstract

The invention provides a vehicle structure simulation optimization method, a vehicle structure simulation optimization device and a computer readable storage medium. According to the vehicle structure simulation optimization method, the simulation model for the target performance of the vehicle body target structure is firstly built, then the end-to-end simulation workflow is built and input to the simulation model for efficient and accurate simulation operation, the simulation operation result with the lower error rate is obtained, the correlation between the input variable and the output variable is obtained through analysis, and the simulation performance of the vehicle body target structure is improved. According to the method, the accuracy of simulation operation is guaranteed, redundant simulation calculation is reduced, a plurality of parts with the relevancy from strong to weak with output variables are screened out to serve as target input variables for simulation optimization, the target input variables are corrected, the more accurate corrected target input variables are obtained, and the simulation optimization efficiency is improved. And performing iterative simulation optimization on the target performance of the vehicle body target structure by adopting a preset optimization algorithm, and automatically adjusting optimization parameters in the iteration process until an algorithm termination condition is met, thereby obtaining a precise simulation optimization result.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, and in particular to a vehicle structure simulation optimization method, a vehicle structure simulation optimization device, and a computer-readable storage medium. Background Technology

[0002] Structural optimization is a crucial aspect of engineering design, aiming to optimize certain structural parameters (such as weight and cost) while meeting specific performance requirements (such as strength and stiffness). With the development of computer technology, structural simulation analysis has become an important tool in structural design. Through structural simulation optimization methods such as finite element analysis, the performance of structures under various conditions can be predicted.

[0003] Among related technologies, known structural simulation optimization methods mainly include: manual identification methods based on finite element analysis, and optimization methods based on commercial CAE software, such as topology optimization, morphology optimization, and shape optimization. Taking vehicles as an example, structural optimization in the vehicle development process mainly relies on two to three rounds of CAE (Computer-Aided Engineering) manual simulation optimization.

[0004] However, the above-mentioned structural simulation optimization has the following problems: over-reliance on engineers' experience, with human factors having a significant impact on optimization efficiency; low efficiency in finding optimization solutions; inability to determine the optimal solution for structural design; low efficiency and limited flexibility when dealing with complex nonlinear problems. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art.

[0006] Therefore, one objective of this invention is to propose a vehicle structure simulation optimization method that reduces reliance on manual labor, has high computational efficiency and flexibility, improves the efficiency of finding optimization solutions and the accuracy of determining the optimal structural design, reduces the computation time and resources required for optimization, and lowers the cost of structural simulation optimization.

[0007] Therefore, the second objective of this invention is to provide a vehicle structure simulation optimization device.

[0008] Therefore, a third objective of the present invention is to provide a computer-readable storage medium.

[0009] To achieve the above objectives, a first aspect of the present invention proposes a vehicle structure simulation optimization method, comprising: constructing a simulation model for the target performance of a vehicle body target structure; establishing an end-to-end simulation workflow for the simulation model, inputting the end-to-end simulation workflow into the simulation model for simulation calculation, and obtaining simulation calculation results, wherein the end-to-end simulation workflow includes input variables and output variables for the simulation model; analyzing the correlation between the input variables and the output variables based on the simulation calculation results; selecting several components with a correlation from strong to weak with the output variables as target input variables for simulation optimization based on the correlation, correcting the target input variables to obtain corrected target input variables; and iteratively simulating and optimizing the target performance of the vehicle body target structure using a preset optimization algorithm based on the corrected target input variables until a termination condition is met, thereby obtaining simulation optimization results.

[0010] According to the vehicle structure simulation optimization method of this invention, a simulation model for the target performance of the vehicle body structure is constructed to provide a model foundation for vehicle structure simulation optimization. An end-to-end simulation workflow is then built to automate the process. The end-to-end simulation workflow is input into the simulation model for efficient and accurate simulation calculations, resulting in simulation results with minimal human intervention and a lower error rate. Based on the simulation results, the correlation between input and output variables is analyzed to quickly identify the impact of key factors on the simulation calculations, ensuring accuracy, reducing redundant calculations, and improving computational efficiency. Based on the correlation, several components with a strong to weak correlation to the output variables are selected as components for use in simulation optimization. The target input variables for simulation optimization are corrected to make them more accurate. Based on the corrected target input variables, a preset optimization algorithm is used to iteratively simulate and optimize the target performance of the vehicle body target structure. During the iteration process, the optimization parameters are automatically adjusted until the algorithm termination condition is met, resulting in accurate simulation optimization results. This approach aims to reduce reliance on manual labor, improve computational efficiency and flexibility, enhance the efficiency of finding optimization solutions, and increase the accuracy of determining the optimal structural design solution through preset optimization algorithms and simulation optimization. It also aims to reduce the computation time and resources required for optimization, lower the cost of structural simulation optimization, and achieve lightweight design effects while maintaining high vehicle performance.

[0011] In some embodiments, the target vehicle structure includes the vehicle roof, and the target performance includes compressive strength. The construction of a simulation model for the target performance of the vehicle structure includes: collecting the compressive stress generated in the vehicle cab under a preset standard scenario, and building an Ls-Dyna (Livermore Software Technology Corporation's Dynamic Analyzer, explicit dynamic finite element analysis software) finite element model of the vehicle roof compressive strength based on the compressive stress to obtain the simulation model; wherein, the preset standard scenario includes: the vehicle is fixed on a horizontal plane, and all windows and doors are closed, and a forced velocity is applied to the pressure plate on the vehicle roof with a first preset angle of forward tilt of the longitudinal axis downward, a longitudinal axis parallel to a vertical plane passing through the longitudinal centerline of the vehicle, and a second preset angle of outward tilt of the transverse axis downward, wherein the first preset angle is smaller than the second preset angle.

[0012] In some embodiments, after constructing a simulation model of the target performance of the vehicle body target structure, the method further includes: simplifying the simulation model based on at least one of model size, time step, and simulation calculation end time.

[0013] In some embodiments, the target performance includes compressive strength. The construction of a simulation model for the target performance of the vehicle body target structure and the establishment of an end-to-end simulation workflow include: based on a preset algorithm, using the thickness of each part of the vehicle body target structure as the input variable, and using the maximum bearing capacity of the vehicle top and the total mass of the simulation model as the output variables to construct the end-to-end simulation workflow, wherein the maximum bearing capacity of the vehicle top is the target to be optimized, and the total mass of the simulation model is a constraint.

[0014] In some embodiments, the step of analyzing the correlation between input variables and output variables in the end-to-end simulation workflow based on the simulation results includes: constructing multiple end-to-end simulation workflows by using the thickness of multiple components as multiple input variables; inputting the multiple end-to-end simulation workflows into the simulation model for multiple iterative calculations to obtain multiple simulation results; and obtaining the correlation between the input variables and output variables based on the multiple simulation results and the multiple input variables.

[0015] In some embodiments, the modification of the target input variable includes setting the range and precision of the initial thickness of the plurality of components.

[0016] In some embodiments, the preset optimization algorithm includes the differential evolution algorithm.

[0017] In some embodiments, the termination condition includes at least one of the following: the computation time of the preset optimization algorithm reaches a preset time, the number of iterations reaches a preset number, and the fitness of the output result reaches a preset fitness. To achieve the above objectives, a second aspect of the present invention provides a vehicle structure simulation optimization device, comprising: a construction module for constructing a simulation model of the target performance of a vehicle body target structure; a setup module for building an end-to-end simulation workflow for the simulation model, inputting the end-to-end simulation workflow into the simulation model for simulation calculation, and obtaining simulation calculation results, wherein the end-to-end simulation workflow includes input variables and output variables for the simulation model; an analysis module for analyzing the correlation between the input variables and the output variables based on the simulation calculation results; a filtering module for filtering out several components with a correlation from strong to weak with the output variables as target input variables for simulation optimization based on the correlation, correcting the target input variables to obtain corrected target input variables; and an iterative simulation optimization module for iteratively simulating and optimizing the target performance of the vehicle body target structure based on the corrected target input variables using a preset optimization algorithm until a termination condition is met, thereby obtaining simulation optimization results.

[0018] According to an embodiment of the present invention, the vehicle structure simulation optimization device constructs a simulation model of the target performance of the vehicle body target structure through a construction module to provide a model foundation for vehicle structure simulation optimization. Then, an end-to-end simulation workflow is built by a setup module to automate the processing flow. The end-to-end simulation workflow is input into the simulation model for efficient and accurate simulation calculations, resulting in simulation results with minimal human intervention and a lower error rate. An analysis module analyzes the correlation between input and output variables based on the simulation results to quickly identify the impact of key factors on the simulation calculations, ensuring accuracy, reducing redundant simulation calculations, and improving computational efficiency. Finally, a screening module selects several components with a correlation ranging from strong to weak with the output variables based on the correlation. The component serves as the target input variable for simulation optimization. The target input variable is corrected to make the corrected target input variable more accurate. Based on the corrected target input variable, the iterative simulation optimization module uses a preset optimization algorithm to iteratively simulate and optimize the target performance of the vehicle body target structure. During the iteration process, the optimization parameters are automatically adjusted until the algorithm termination condition is met, and accurate simulation optimization results are obtained. This achieves the goal of reducing reliance on manual labor, improving computational efficiency and flexibility, improving the efficiency of finding optimization solutions, and improving the accuracy of determining the optimal solution for structural design through preset optimization algorithms and simulation optimization. It also reduces the computation time and resources required for optimization and lowers the cost of structural simulation optimization, achieving lightweight design effects while maintaining high vehicle performance.

[0019] To achieve the above objectives, a third aspect of the present invention provides a computer-readable storage medium storing a vehicle structure simulation optimization program, which, when executed by a processor, implements the vehicle structure simulation optimization method as described in the above embodiments.

[0020] According to an embodiment of the present invention, a computer-readable storage medium stores a vehicle structure simulation optimization program corresponding to the vehicle structure simulation optimization method of the above embodiments. When the processor executes the vehicle structure simulation optimization program, it constructs a simulation model of the target performance of the vehicle body target structure to provide a model basis for vehicle structure simulation optimization. Then, it builds an end-to-end simulation workflow to achieve automated processing. The end-to-end simulation workflow is input into the simulation model for efficient and accurate simulation calculations, resulting in simulation results with no human intervention and a lower error rate. Based on the simulation results, the correlation between input and output variables is analyzed to quickly identify the impact of key factors on the simulation calculations, ensuring the accuracy of the simulation calculations, reducing redundant simulation calculations, and improving computational efficiency. The correlation between the output variables and the target input variables is determined by selecting several components with a strong to weak correlation as target input variables for simulation optimization. The target input variables are then corrected to make them more accurate. Based on the corrected target input variables, a preset optimization algorithm is used to iteratively simulate and optimize the target performance of the vehicle body target structure. During the iteration process, the optimization parameters are automatically adjusted until the algorithm termination condition is met, resulting in accurate simulation optimization results. This approach aims to reduce reliance on manual labor, improve computational efficiency and flexibility, enhance the efficiency of finding optimization solutions, and increase the accuracy of determining the optimal structural design solution through preset optimization algorithms and simulation optimization. It also aims to reduce the computation time and resources required for optimization and lower the cost of structural simulation optimization, achieving lightweight design effects while maintaining high vehicle performance.

[0021] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0022] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a flowchart of a vehicle structure simulation optimization method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a simulation system according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a loading device and positioning according to an embodiment of the present invention; Figure 4This is a schematic diagram of the top strength simulation model and bearing capacity curve according to an embodiment of the present invention; Figure 5 This is a simplified model and a schematic diagram of model benchmarking according to an embodiment of the present invention; Figure 6 A schematic diagram of a simulation workflow according to an embodiment of the present invention; Figure 7 A schematic diagram illustrating the correlation between input and output variables according to an embodiment of the present invention; Figure 8 A schematic diagram of an optimized component according to an embodiment of the present invention; Figure 9 A flowchart of a vehicle structure simulation optimization method according to another embodiment of the present invention; Figure 10 A block diagram of a vehicle structure simulation optimization device according to an embodiment of the present invention.

[0023] Figure label: 100 - Vehicle structure simulation optimization device; 101 - Construction module; 102 - Construction module; 103 - Analysis module; 104 - Screening module; 105 - Iterative simulation optimization module. Detailed Implementation The embodiments described with reference to the accompanying drawings are exemplary, and the embodiments of the present invention are described in detail below.

[0024] The following is combined Figure 1-9 This describes an embodiment of the present invention.

[0025] like Figure 1 The diagram shown is a flowchart of a vehicle structure simulation optimization method according to an embodiment of the present invention. The vehicle structure simulation optimization method of this embodiment includes at least steps S1-S5.

[0026] Step S1: Construct a simulation model of the target performance of the vehicle body target structure.

[0027] In this embodiment, the target vehicle body structure is the structure on the vehicle to be simulated and optimized; the target performance is the performance corresponding to the target vehicle body structure. Ls-Dyna is a general-purpose explicit dynamic finite element analysis software, mainly used to simulate and analyze complex nonlinear dynamic events. Taking the top of the vehicle body as the target structure and compressive strength as the target performance as an example, the compressive strength generated by the vehicle's cab is collected, and a finite element model of the compressive strength of the top of the Ls-Dyna vehicle body is built based on the compressive strength to obtain a simulation model, which serves as the model basis for the simulation optimization of the vehicle structure.

[0028] Step S2: For the simulation model, build an end-to-end simulation workflow, input the end-to-end simulation workflow into the simulation model for simulation calculation, and obtain the simulation calculation results. The end-to-end simulation workflow includes input variables and output variables for the simulation model.

[0029] In this embodiment, the input variables of the simulation model include the thickness of each component, and the output variables include the maximum load-bearing capacity of the vehicle's roof (i.e., the maximum force exerted on the roof) and the total mass of the model. An end-to-end input-output simulation workflow is built using programming methods or commercial software. This end-to-end simulation workflow is input into the simulation model for simulation calculations, yielding the simulation results. This automated process makes the entire simulation process more efficient and accurate, reducing human intervention and lowering the error rate.

[0030] Step S3: Analyze the correlation between input and output variables based on the simulation results.

[0031] In this embodiment, for example, the correlation between the input variable of the thickness of 17 structural components (parts) and the two output variables of maximum force (maximum load on the vehicle roof) and mass is obtained. Taguchi test design is carried out, and the above-mentioned end-to-end simulation workflow is used for automated calculation. The correlation between the input variables and the output variables is obtained through post-processing. This allows for the rapid identification of the impact of key factors on the simulation calculation through correlation analysis, ensuring the accuracy of the simulation calculation, reducing redundant simulation calculations, improving calculation efficiency, and preparing for finding the globally optimal or closer to the globally optimal structural optimization scheme.

[0032] Step S4: Based on correlation, select several components with a strong to weak correlation with the output variable as target input variables for simulation optimization, and correct the target input variables to obtain the corrected target input variables.

[0033] In this embodiment, for example, based on correlation ranking, 10 components strongly correlated with the maximum force (maximum load-bearing capacity of the vehicle roof) in the output variables are selected as target input variables for subsequent optimization. For different components, considering factors such as their manufacturing process, the target input variables can be corrected based on empirical values. For example, empirical values ​​(such as the initial thickness range and accuracy) can be stored in a storage unit and retrieved during correction to set the initial thickness range and accuracy of the corresponding components, resulting in corrected target input variables that are more accurate.

[0034] Step S5: Based on the corrected target input variables, the target performance of the vehicle body target structure is iteratively simulated and optimized using a preset optimization algorithm until the termination condition is met, and the simulation optimization result is obtained.

[0035] In this embodiment, a preset optimization algorithm, such as the Differential Evolution (DE) algorithm, is used. DE is an emerging intelligent algorithm characterized by fewer parameter settings, faster convergence, simpler structure, and stronger robustness. Taking the vehicle body top as the target structure and compressive strength as the target performance, the DE algorithm iteratively simulates and optimizes the compressive strength of the vehicle body top based on the corrected target input variables. The preset optimization algorithm automatically adjusts the optimization parameters until the algorithm's termination condition is met, yielding the simulation optimization result. This simulation optimization result ensures that, under the condition of the vehicle body top bearing the top load, the thickness distribution of each component reaches the optimal state, the deformation of the main load-bearing structural components achieves a coordinated and complementary effect, and the load transfer path of the vehicle body top reaches the most ideal state. This achieves the goal of reducing reliance on manual labor, improving computational efficiency and flexibility, increasing the efficiency of finding optimization solutions, improving the accuracy of determining the optimal structural design solution, reducing the computation time and resources required for optimization, and lowering the cost of structural simulation optimization through preset optimization algorithms and simulation optimization, thereby achieving lightweight design effects while maintaining high vehicle performance.

[0036] According to the vehicle structure simulation optimization method of this invention, a simulation model for the target performance of the vehicle body structure is constructed to provide a model foundation for vehicle structure simulation optimization. An end-to-end simulation workflow is then built to automate the process. The end-to-end simulation workflow is input into the simulation model for efficient and accurate simulation calculations, resulting in simulation results with minimal human intervention and a lower error rate. Based on the simulation results, the correlation between input and output variables is analyzed to quickly identify the impact of key factors on the simulation calculations, ensuring accuracy, reducing redundant calculations, and improving computational efficiency. Based on the correlation, several components with a strong to weak correlation to the output variables are selected as components for use in simulation optimization. The target input variables for simulation optimization are corrected to make them more accurate. Based on the corrected target input variables, a preset optimization algorithm is used to iteratively simulate and optimize the target performance of the vehicle body target structure. During the iteration process, the optimization parameters are automatically adjusted until the algorithm termination condition is met, resulting in accurate simulation optimization results. This approach aims to reduce reliance on manual labor, improve computational efficiency and flexibility, enhance the efficiency of finding optimization solutions, and increase the accuracy of determining the optimal structural design solution through preset optimization algorithms and simulation optimization. It also aims to reduce the computation time and resources required for optimization, lower the cost of structural simulation optimization, and achieve lightweight design effects while maintaining high vehicle performance.

[0037] In some embodiments, the target structure of the vehicle body includes the top of the vehicle body, and the target performance includes compressive strength. Constructing a simulation model for the target performance of the target structure of the vehicle body includes: collecting the compressive stress generated in the vehicle cab under a preset standard scenario; building an Ls-Dyna (Livermore Software Technology Corporation's Dynamic Analyzer, explicit dynamic finite element analysis software) finite element model of the compressive strength of the vehicle body top based on the compressive stress to obtain the simulation model; wherein the preset standard scenario includes: the vehicle is fixed on a horizontal plane, and all windows and doors are closed; a forced velocity is applied to the pressure plate on the top of the vehicle with a first preset angle of forward tilt of the longitudinal axis downwards, a longitudinal axis parallel to a vertical plane passing through the longitudinal centerline of the vehicle, and a second preset angle of outward tilt of the transverse axis downwards, wherein the first preset angle is smaller than the second preset angle.

[0038] In an embodiment, such as Figure 2 The diagram shown is a schematic of a simulation system according to an embodiment of the present invention. The first preset angle is set to 5°, and the second preset angle to 25°. Ls-Dyna is a general-purpose explicit dynamic finite element analysis software, mainly used for simulating and analyzing complex nonlinear dynamic events. Figure 2 The simulation system shown establishes a simulation model of the target performance.

[0039] like Figure 3 The diagram shown illustrates the loading device and positioning according to another embodiment of the present invention. A preset standard scenario is provided, for example, that standard GB 26134 specifies the test method as follows: The loading device is a rigid block, i.e., a pressure plate, whose lower surface is a flat rectangular surface of 1829mm × 762mm. The vehicle is rigidly fixed to a rigid horizontal surface and held in place. All windows are closed. All doors are closed and locked. Figure 3 The positioning and loading device shown has its longitudinal axis tilt angle of 5° downwards from the horizontal plane, and its longitudinal axis is parallel to the vertical plane passing through the longitudinal centerline of the vehicle, and its transverse axis tilt angle of 25° downwards from the horizontal plane.

[0040] like Figure 4The diagram shown illustrates a top strength simulation model and bearing capacity curve according to another embodiment of the present invention. Under the aforementioned preset standard scenario, the compressive strength generated by the vehicle cab is collected. Based on this compressive strength, an Ls-Dyna vehicle body top compressive strength finite element model is built to obtain a simulation model, serving as the model basis for vehicle structure simulation optimization. For example, using the forced speed command of Ls-Dyna, a forced speed is applied to the pressure plate with a longitudinal axis forward tilt angle of 5° downwards from the horizontal plane, the longitudinal axis parallel to the vertical plane passing through the vehicle's longitudinal centerline, and a transverse axis outward tilt angle of 25° downwards from the horizontal plane. The reaction force generated during the cab's pressure deformation process is the cab's compressive strength, which is output through contact force in Ls-Dyna as shown in the diagram. Figure 4 The contact force curve shown is shown.

[0041] In some embodiments, after constructing a simulation model of the target performance of the vehicle body target structure, the method further includes: simplifying the simulation model based on at least one of model size, time step, and simulation calculation end time.

[0042] In this embodiment, the model size, time step, and simulation completion time can be determined based on actual experience values ​​and / or experimental calibration, and the determined model size, time step, and simulation completion time are stored in the storage unit and can be directly called when needed.

[0043] like Figure 5 The diagram shown illustrates a simplified model and model benchmark of an embodiment of the present invention. For example, since hundreds or thousands of calculations are often required during the entire simulation model optimization process, using a complete top compressive strength finite element model would result in very low optimization efficiency. Therefore, while ensuring acceptable model accuracy, the model must be appropriately simplified. Because the model is an explicit dynamic model, the following three methods are used to simplify it: Model size: typically the mesh size and the number of parts included; Time step: affects solution accuracy and must be controlled within a reasonable range; Simulation completion time: the time required for the entire loading process of the simulation model to complete. The simulation model is simplified according to the above process, as shown below. Figure 5 As shown, this aims to improve simulation efficiency without affecting the accuracy of the simulation optimization results.

[0044] In some embodiments, the target performance includes compressive strength. A simulation model for the target performance of the vehicle body target structure is constructed, and an end-to-end simulation workflow is established, including: based on a preset algorithm, using the thickness of each part of the vehicle body target structure as input variables, and the maximum bearing capacity of the vehicle top and the total mass of the simulation model as output variables, to construct an end-to-end simulation workflow, wherein the maximum bearing capacity of the vehicle top is the target to be optimized, and the total mass of the simulation model is a constraint condition.

[0045] In an embodiment, such as Figure 6 The diagram illustrates a simulation workflow according to an embodiment of the present invention. For example, an end-to-end input-output simulation workflow is built using programming methods or commercial software. Input variables are defined, including the thickness of each part. The maximum load-bearing capacity of the vehicle's roof (i.e., the maximum force borne by the roof) and the total mass of the model are used as output variables. Figure 6 The maximum force in the simulation is the optimization objective, and the total mass is used as a constraint. The solver module in the solver results port schedules a remote computing cluster to perform simulation calculations and acquire results via scripts. By constructing an end-to-end simulation workflow, the simulation workflow is automated, making the entire simulation process more efficient and accurate, reducing manual intervention, and lowering the error rate.

[0046] In some embodiments, the correlation between input and output variables in an end-to-end simulation workflow is obtained by analyzing the simulation results, including: constructing multiple end-to-end simulation workflows by using the thickness of multiple components as multiple input variables; inputting the multiple end-to-end simulation workflows into the simulation model for multiple iterative calculations to obtain multiple simulation results; and obtaining the correlation between input and output variables based on the multiple simulation results and multiple input variables.

[0047] In an embodiment, such as Figure 6 As shown, multiple end-to-end simulation workflows are constructed by using the thickness of multiple components as multiple input variables to determine the input parameters of the end-to-end simulation workflow related to compressive strength. These multiple end-to-end simulation workflows are input into the simulation model for multiple iterative calculations, resulting in multiple simulation results. This process yields more accurate simulation results after multiple iterations. Based on the multiple simulation results and multiple input variables, the correlation between input and output variables is obtained. This correlation analysis allows for the rapid identification of the impact of key factors on the simulation calculations, ensuring the accuracy of the simulation, reducing redundant simulation calculations, improving computational efficiency, and preparing for finding the globally optimal or closer-to-global optimal structural optimization scheme.

[0048] like Figure 7 The diagram shown illustrates the correlation between input and output variables in one embodiment of the present invention. For example, the correlation between the input variable (thickness of 17 structural components) and the two output variables (maximum force and mass) is obtained. A Taguchi experimental design is performed, and the aforementioned end-to-end simulation workflow is used for automated calculation. Post-processing yields the results shown below. Figure 7 The correlation analysis diagram between the input and output variables is shown.

[0049] In some embodiments, modifying the target input variable includes setting the range and accuracy of the initial thickness of several components.

[0050] In this embodiment, considering factors such as manufacturing processes, the target input variable can be modified based on empirical values ​​for different components. For example, empirical values ​​(such as the initial thickness range and accuracy) can be stored in a storage unit and retrieved during modification to set the initial thickness range and accuracy of the corresponding component, thereby making the obtained target input variable more accurate.

[0051] For example, based on correlation ranking, 10 components that are strongly correlated with the maximum force (maximum load on the vehicle roof) in the output variables are selected as target input variables for subsequent optimization, as shown in Table 1 below:

[0052] Based on the initial thickness of each part and considering manufacturing process issues, the variable range of the thickness variable of each part is defined during optimization. That is, upper and lower limits are set for the input variables, and the values ​​can only be taken to one decimal place, as shown in Table 2 below.

[0053]

[0054] In some embodiments, the preset optimization algorithm includes the differential evolution algorithm.

[0055] In this embodiment, the preset optimization algorithm includes the Differential Evolution (DE) algorithm, which is an emerging intelligent algorithm with the characteristics of few parameter settings, fast convergence speed, simple structure, and strong robustness.

[0056] The core theory of differential evolution is as follows: During optimization, differential evolution generates a new parameter vector by adding the weighted difference of a certain number of individuals selected from the previous generation to another individual. The essence of its strategy is the summation of the difference between two vectors and a third vector. , Where m=1...λ represents different indices, k represents an algebraic index, and r1,r2,r3∈[1,λ] represents a randomly selected individual.

[0057] The optimization steps of the differential evolution algorithm are as follows: Step 1: Initialization, usually a set of random independent vectors, with a population size of NP.

[0058] Step 2: Evaluate the fitness of all initial population vectors.

[0059] Step 3: Repeat: Loop i from 1 to NP.

[0060] Choose mutually distinct vectors (usually 3). , , To reproduce.

[0061] Construct a weighted difference vector and add it to the third vector: , Where F is the weighting factor.

[0062] Randomly select target vector and Crossover is used to obtain the test vector.

[0063] Loop n from 1 to D: , Where CR is the crossover rate, D is the dimension of the vector, rand is a random natural number in the generation set [0, 1), and rand(D) is a random integer in the generation set.

[0064] In the test vector and target vector They choose from among them, and the more suitable one survives to the next generation.

[0065] Step 4: Check the termination conditions (algorithm computation time, number of iterations, fitness of output results). If the conditions are met, stop; otherwise, return to step 3.

[0066] , The differential evolution algorithm described above is used to optimize the target performance of the vehicle body target structure through iterative simulation. With 10 optimization variables as input, the initial population size is set to 15 samples and the population size is 30. A total of 10 generations of iterative evolution are carried out. The end-to-end simulation workflow established in the above embodiment is used for automated calculation iteration.

[0067] In some embodiments, the termination condition includes at least one of the following: the computation time of the preset optimization algorithm reaches a preset time, the number of iterations reaches a preset number, and the fitness of the output result reaches a preset fitness.

[0068] In this embodiment, taking the differential evolution algorithm as an example, the termination conditions for the differential evolution algorithm include at least one of the following: the computation time of the algorithm reaches a preset time, the number of iterations reaches a preset number, and the fitness of the output result reaches a preset fitness. Constraining the termination conditions of the algorithm ensures that it stops running at an appropriate time, preventing it from entering an infinite loop.

[0069] For example, such as Figure 8 The diagram shown is a schematic of the optimized component according to an embodiment of the present invention. After the differential evolution algorithm satisfies the termination condition, after 10 generations of optimization, the results gradually converge towards the maximum force (maximum load-bearing capacity of the vehicle roof). Finally, in the 10th generation population, the 282nd sample point reaches the maximum value of 69.00 kN, which is 25.84 kN higher than the initial value, and the roof load-bearing capacity performance is improved by about 60%. Furthermore, it satisfies the constraint that the total mass increase does not exceed 10%. The optimization results are shown in Table 3 below.

[0070] Table 3 Optimization Results Data

[0071] After optimization, the material thickness distribution reaches its optimal state under the condition of bearing the top load. The deformation of the main load-bearing structural components can achieve a coordinated and complementary effect, and the top load transmission path reaches its ideal state. The optimized components are as follows: Figure 8 As shown.

[0072] The following is for reference. Figure 9 The vehicle structure simulation optimization method of this invention will be described in detail.

[0073] like Figure 9 The diagram shown is a flowchart of a vehicle structure simulation optimization method according to another embodiment of the present invention. The vehicle structure simulation optimization method of this embodiment includes at least steps S50-S59.

[0074] Step S50: Under a preset standard scenario, collect the pressure resistance generated in the vehicle cab, and build a finite element model of the Ls-Dyna vehicle body top compressive strength based on the pressure resistance to obtain a simulation model; wherein, the preset standard scenario includes: the vehicle is fixed on a horizontal plane, and all windows and doors are closed, and a forced velocity is applied to the pressure plate on the top of the vehicle with a first preset angle of horizontal downward tilt on the longitudinal axis, the longitudinal axis being parallel to the vertical plane passing through the longitudinal center line of the vehicle, and a second preset angle of horizontal downward tilt on the transverse axis, wherein the first preset angle is smaller than the second preset angle.

[0075] Step S51: Simplify the simulation model based on at least one of the model size, time step, and simulation operation end time.

[0076] Step S52: Based on the preset algorithm, the thickness of each part of the target vehicle body structure is used as the input variable, and the maximum load-bearing capacity of the vehicle top and the total mass of the simulation model are used as the output variables to construct an end-to-end simulation workflow. The maximum load-bearing capacity of the vehicle top is the target to be optimized, and the total mass of the simulation model is the constraint condition.

[0077] Step S53: Input the end-to-end simulation workflow into the simulation model for simulation calculation and obtain the simulation calculation results. The end-to-end simulation workflow includes input variables and output variables for the simulation model.

[0078] Step S54: Use the thickness of multiple components as multiple input variables to construct multiple end-to-end simulation workflows.

[0079] Step S55: Input multiple end-to-end simulation workflows into the simulation model for multiple iterative calculations to obtain multiple simulation results.

[0080] Step S56: Based on multiple simulation results and multiple input variables, obtain the correlation between input variables and output variables.

[0081] Step S57: Based on correlation, select several components with a strong to weak correlation with the output variable as target input variables for simulation optimization, and correct the target input variables to obtain the corrected target input variables.

[0082] Step S58 involves revising the definition of the target input variables, including setting the range and precision of the initial thickness of several components.

[0083] Step S59: Based on the corrected target input variables, the target performance of the vehicle body target structure is iteratively simulated and optimized using a preset optimization algorithm until the termination condition is met, and the simulation optimization result is obtained. The preset optimization algorithm includes a differential evolution algorithm, and the termination condition includes at least one of the following: the computation time of the preset optimization algorithm reaches a preset time, the number of iterations reaches a preset number, and the fitness of the output result reaches a preset fitness.

[0084] According to the vehicle structure simulation optimization method of this invention, a simulation model for the target performance of the vehicle body structure is constructed to provide a model foundation for vehicle structure simulation optimization. An end-to-end simulation workflow is then built to automate the process. The end-to-end simulation workflow is input into the simulation model for efficient and accurate simulation calculations, resulting in simulation results with minimal human intervention and a lower error rate. Based on the simulation results, the correlation between input and output variables is analyzed to quickly identify the impact of key factors on the simulation calculations, ensuring accuracy, reducing redundant calculations, and improving computational efficiency. Based on the correlation, several components with a strong to weak correlation to the output variables are selected as components for use in simulation optimization. The target input variables for simulation optimization are corrected to make them more accurate. Based on the corrected target input variables, a preset optimization algorithm is used to iteratively simulate and optimize the target performance of the vehicle body target structure. During the iteration process, the optimization parameters are automatically adjusted until the algorithm termination condition is met, resulting in accurate simulation optimization results. This approach aims to reduce reliance on manual labor, improve computational efficiency and flexibility, enhance the efficiency of finding optimization solutions, and increase the accuracy of determining the optimal structural design solution through preset optimization algorithms and simulation optimization. It also aims to reduce the computation time and resources required for optimization, lower the cost of structural simulation optimization, and achieve lightweight design effects while maintaining high vehicle performance.

[0085] The following is for reference. Figure 10 The vehicle structure simulation optimization device 100 of the present invention is described.

[0086] like Figure 10 The diagram shown is a block diagram of a vehicle structure simulation optimization device according to an embodiment of the present invention. The vehicle structure simulation optimization device 100 of this embodiment includes: a construction module 101, used to construct a simulation model for the target performance of a vehicle body target structure; a setup module 102, used to build an end-to-end simulation workflow for the simulation model, input the end-to-end simulation workflow into the simulation model for simulation calculation, and obtain simulation calculation results, wherein the end-to-end simulation workflow includes input variables and output variables for the simulation model; an analysis module 103, used to analyze the correlation between the input variables and output variables based on the simulation calculation results; a filtering module 104, used to filter out several components with a correlation from strong to weak with the output variables as target input variables for simulation optimization based on the correlation, and correct the target input variables to obtain corrected target input variables; and an iterative simulation optimization module 105, used to iteratively simulate and optimize the target performance of the vehicle body target structure based on the corrected target input variables using a preset optimization algorithm until a termination condition is met, and obtain the simulation optimization result.

[0087] According to an embodiment of the present invention, the vehicle structure simulation optimization device 100 constructs a simulation model of the target performance of the vehicle body target structure through a construction module 101 to provide a model basis for vehicle structure simulation optimization. Then, a setup module 102 builds an end-to-end simulation workflow to automate the process. The end-to-end simulation workflow is input into the simulation model for efficient and accurate simulation calculations, resulting in simulation results with less human intervention and a lower error rate. An analysis module 103 analyzes the correlation between input and output variables based on the simulation results to quickly identify the impact of key factors on the simulation calculations, ensuring accuracy, reducing redundant simulation calculations, and improving computational efficiency. Finally, a screening module 104 filters variables based on their correlation with the output variables, from strongest to weakest. Several components are used as target input variables for simulation optimization. The target input variables are corrected to make the corrected target input variables more accurate. The iterative simulation optimization module 105 uses a preset optimization algorithm to iteratively simulate and optimize the target performance of the vehicle body target structure based on the corrected target input variables. During the iteration process, the optimization parameters are automatically adjusted until the algorithm termination condition is met, and accurate simulation optimization results are obtained. This achieves the goal of reducing reliance on manual labor, improving computational efficiency and flexibility, improving the efficiency of finding optimization solutions, and improving the accuracy of determining the optimal solution for structural design through preset optimization algorithms and simulation optimization. It also reduces the computation time and resources required for optimization and lowers the cost of structural simulation optimization, achieving lightweight design effects while maintaining high vehicle performance.

[0088] In some embodiments, the construction module 101 is used to: construct a simulation model of the target performance of the vehicle body target structure, including the vehicle body top and the target performance including compressive strength, and to: collect the compressive strength generated by the vehicle cab under a preset standard scenario, and build an Ls-Dyna vehicle body top compressive strength finite element model based on the compressive strength to obtain a simulation model; wherein, the preset standard scenario includes: the vehicle is fixed on a horizontal plane and all windows and doors are closed, and a forced velocity is applied to the pressure plate on the top of the vehicle with a longitudinal axis forward tilt angle of a first preset angle downward, a longitudinal axis parallel to a vertical plane passing through the longitudinal center line of the vehicle, and a transverse axis outward tilt angle of a second preset angle downward, wherein the first preset angle is smaller than the second preset angle.

[0089] In some embodiments, after constructing a simulation model of the target performance of the vehicle body target structure, the construction module 101 further includes: simplifying the simulation model based on at least one of the model size, time step, and simulation calculation end time.

[0090] In some embodiments, the construction module 102 is used to: construct a simulation model of the target performance of the vehicle body target structure, including compressive strength, based on a preset algorithm, using the thickness of each part of the vehicle body target structure as input variables, and the maximum bearing capacity of the vehicle top and the total mass of the simulation model as output variables, to construct an end-to-end simulation workflow, wherein the maximum bearing capacity of the vehicle top is the target to be optimized, and the total mass of the simulation model is a constraint condition.

[0091] In some embodiments, the analysis module 103 is used to: analyze the correlation between input variables and output variables in the end-to-end simulation workflow based on the simulation results, including: constructing multiple end-to-end simulation workflows by using the thickness of multiple components as multiple input variables; inputting the multiple end-to-end simulation workflows into the simulation model for multiple iterative calculations to obtain multiple simulation results; and obtaining the correlation between input variables and output variables based on the multiple simulation results and multiple input variables.

[0092] In some embodiments, the filtering module 104 is used to: correct the target input variable, including: setting the value range and value accuracy of the initial thickness of several components.

[0093] In some embodiments, the preset optimization algorithm in the iterative simulation optimization module 105 includes the differential evolution algorithm.

[0094] In some embodiments, the termination conditions of the preset optimization algorithm in the iterative simulation optimization module 105 include at least one of the following: the computation time of the preset optimization algorithm reaches a preset time, the number of iterations reaches a preset number, and the fitness of the output result reaches a preset fitness.

[0095] According to an embodiment of the present invention, the vehicle structure simulation optimization device 100 constructs a simulation model of the target performance of the vehicle body target structure through a construction module 101 to provide a model basis for vehicle structure simulation optimization. Then, a setup module 102 builds an end-to-end simulation workflow to automate the process. The end-to-end simulation workflow is input into the simulation model for efficient and accurate simulation calculations, resulting in simulation results with less human intervention and a lower error rate. An analysis module 103 analyzes the correlation between input and output variables based on the simulation results to quickly identify the impact of key factors on the simulation calculations, ensuring accuracy, reducing redundant simulation calculations, and improving computational efficiency. Finally, a screening module 104 filters variables based on their correlation with the output variables, from strongest to weakest. Several components are used as target input variables for simulation optimization. The target input variables are corrected to make the corrected target input variables more accurate. The iterative simulation optimization module 105 uses a preset optimization algorithm to iteratively simulate and optimize the target performance of the vehicle body target structure based on the corrected target input variables. During the iteration process, the optimization parameters are automatically adjusted until the algorithm termination condition is met, and accurate simulation optimization results are obtained. This achieves the goal of reducing reliance on manual labor, improving computational efficiency and flexibility, improving the efficiency of finding optimization solutions, and improving the accuracy of determining the optimal solution for structural design through preset optimization algorithms and simulation optimization. It also reduces the computation time and resources required for optimization and lowers the cost of structural simulation optimization, achieving lightweight design effects while maintaining high vehicle performance.

[0096] The following describes a computer-readable storage medium according to embodiments of the present invention.

[0097] The computer-readable storage medium of this invention stores a vehicle structure simulation optimization program. When the vehicle structure simulation optimization program is executed by a processor, it implements the vehicle structure simulation optimization method as described in the above embodiments.

[0098] According to an embodiment of the present invention, a computer-readable storage medium stores a vehicle structure simulation optimization program corresponding to the vehicle structure simulation optimization method of the above embodiments. When the processor executes the vehicle structure simulation optimization program, it constructs a simulation model of the target performance of the vehicle body target structure to provide a model basis for vehicle structure simulation optimization. Then, it builds an end-to-end simulation workflow to achieve automated processing. The end-to-end simulation workflow is input into the simulation model for efficient and accurate simulation calculations, resulting in simulation results with no human intervention and a lower error rate. Based on the simulation results, the correlation between input and output variables is analyzed to quickly identify the impact of key factors on the simulation calculations, ensuring the accuracy of the simulation calculations, reducing redundant simulation calculations, and improving computational efficiency. The correlation between the output variables and the target input variables is determined by selecting several components with a strong to weak correlation as target input variables for simulation optimization. The target input variables are then corrected to make them more accurate. Based on the corrected target input variables, a preset optimization algorithm is used to iteratively simulate and optimize the target performance of the vehicle body target structure. During the iteration process, the optimization parameters are automatically adjusted until the algorithm termination condition is met, resulting in accurate simulation optimization results. This approach aims to reduce reliance on manual labor, improve computational efficiency and flexibility, enhance the efficiency of finding optimization solutions, and increase the accuracy of determining the optimal structural design solution through preset optimization algorithms and simulation optimization. It also aims to reduce the computation time and resources required for optimization and lower the cost of structural simulation optimization, achieving lightweight design effects while maintaining high vehicle performance.

[0099] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example.

[0100] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A vehicle structure simulation optimization method, characterized in that, include: Construct a simulation model for the target performance of the vehicle body target structure; For the simulation model, an end-to-end simulation workflow is built, and the end-to-end simulation workflow is input to the simulation model for simulation calculation to obtain simulation results. The end-to-end simulation workflow includes input variables and output variables for the simulation model. The correlation between the input variables and the output variables is analyzed based on the simulation results. Based on the correlation, several components with a correlation from strong to weak with the output variable are selected as target input variables for simulation optimization. The target input variables are then corrected to obtain the corrected target input variables. Based on the corrected target input variables, the target performance of the vehicle body target structure is iteratively simulated and optimized using a preset optimization algorithm until the termination condition is met, and the simulation optimization result is obtained.

2. The vehicle structure simulation optimization method according to claim 1, characterized in that, The target vehicle body structure includes the vehicle body top, and the target performance includes compressive strength. The construction of a simulation model for the target performance of the target vehicle body structure includes: Under a preset standard scenario, the pressure resistance generated in the vehicle cab is collected, and a finite element model of the compressive strength of the Ls-Dyna vehicle body top is built based on the pressure resistance to obtain the simulation model; The preset standard scenario includes: the vehicle is fixed on a horizontal plane, and all windows and doors are closed. A forced speed is applied to the pressure plate on the top of the vehicle with a first preset angle of forward tilt of the longitudinal axis downward, a longitudinal axis parallel to a vertical plane passing through the longitudinal center line of the vehicle, and a second preset angle of outward tilt of the transverse axis downward, wherein the first preset angle is smaller than the second preset angle.

3. The vehicle structure simulation optimization method according to claim 1 or 2, characterized in that, After constructing a simulation model of the target performance for the vehicle body structure, the following is also included: The simulation model is simplified based on at least one of the following: model size, time step, and simulation completion time.

4. The vehicle structure simulation optimization method according to claim 1, characterized in that, The target performance includes compressive strength; the construction of a simulation model for the target performance of the vehicle body structure; and the establishment of an end-to-end simulation workflow include: Based on a preset algorithm, the thickness of each part of the target vehicle body structure is used as the input variable, and the maximum load-bearing capacity of the vehicle top and the total mass of the simulation model are used as the output variables to construct the end-to-end simulation workflow. The maximum load-bearing capacity of the vehicle top is the target to be optimized, and the total mass of the simulation model is the constraint condition.

5. The vehicle structure simulation optimization method according to claim 1, characterized in that, The analysis of the correlation between input and output variables in the end-to-end simulation workflow based on the simulation results includes: Multiple end-to-end simulation workflows are constructed by using the thickness of multiple components as multiple input variables; Multiple end-to-end simulation workflows are input into the simulation model for multiple iterative calculations to obtain multiple simulation results. The correlation between the input and output variables is obtained based on multiple simulation results and multiple input variables.

6. The vehicle structure simulation optimization method according to claim 1, characterized in that, The modification of the target input variable includes: The range and accuracy of the initial thickness of the aforementioned components are set.

7. The vehicle structure simulation optimization method according to claim 1, characterized in that, The preset optimization algorithm includes the differential evolution algorithm.

8. The vehicle structure simulation optimization method according to claim 1, characterized in that, The termination condition includes at least one of the following: the computation time of the preset optimization algorithm reaches a preset time, the number of iterations reaches a preset number, and the fitness of the output result reaches a preset fitness.

9. A vehicle structure simulation and optimization device, characterized in that, include: The building module is used to construct simulation models of the target performance of the vehicle body target structure; A module is used to build an end-to-end simulation workflow for the simulation model, input the end-to-end simulation workflow into the simulation model for simulation calculation, and obtain simulation calculation results. The end-to-end simulation workflow includes input variables and output variables for the simulation model. The analysis module is used to analyze the correlation between the input variables and the output variables based on the simulation results. The filtering module is used to filter out several components with a strong to weak correlation with the output variable based on the correlation as target input variables for simulation optimization, and to correct the target input variables to obtain the corrected target input variables. The iterative simulation optimization module is used to perform iterative simulation optimization on the target performance of the vehicle body target structure based on the corrected target input variables and a preset optimization algorithm until the termination condition is met, and then obtain the simulation optimization result.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a vehicle structure simulation optimization program, which, when executed by a processor, implements the vehicle structure simulation optimization method as described in any one of claims 1-8.

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