Large complex thin-wall structural member simulation method, system and device and storage medium

Abstract

The invention discloses a large complex thin-wall structural member simulation method, system and device and a storage medium. The method comprises the steps of obtaining experimental data of a real object sample of a large complex thin-wall structural member under a plurality of target working conditions; constructing an initial CAE model, and determining a thickness change function of each variable thickness partition; acquiring simulation data of the current CAE model under the target working condition; performing error analysis and sensitive area positioning according to the experimental data and the simulation data to obtain a sensitive area to be optimized; performing parametric modeling and optimization on the wall thickness distribution of the sensitive area to obtain the sensitive area after wall thickness adjustment; and performing model correction on the current CAE model according to the sensitive area subjected to wall thickness adjustment, and returning to the step of obtaining the simulation data of the current CAE model under the target working condition until a preset convergence condition is reached, thereby obtaining the target CAE model. The simulation precision of the large complex thin-wall structural member is improved, and the method can be applied to the technical field of structural design.

Description

Technical Field

[0001] The present invention relates to the technical field of structural design, and in particular to a simulation method, system, device and storage medium for large complex thin-walled structural parts. Background Art

[0002] With the development of automobile manufacturing technology, large and complex thin-walled structural parts are gradually being used in automobile body structures due to their advantages such as lightweight, high precision and low cost. However, when predicting the dynamic performance of one-piece die-cast parts, existing CAE simulation models often deviate from the actual structural performance, resulting in insufficient accuracy in subsequent simulation analysis. Traditional experimental analysis methods usually rely on theoretical calculations or single experimental data, which makes it difficult to fully reflect the dynamic characteristics of the actual structure. Therefore, developing an accurate modeling method that can combine actual experiments with CAE simulation analysis is of great significance for improving the simulation accuracy of large and complex thin-walled structural parts.

[0003] The existing technology has the following problems:

[0004] (1) Traditional CAE models have problems such as material parameter deviation and boundary condition inaccuracy in modal analysis, resulting in an error of 15%-20% between simulation and measurement;

[0005] (2) Existing benchmarking methods mostly use single corrections and do not form a closed-loop optimization process;

[0006] (3) There is a lack of systematic correlation analysis methods for test-simulation data, which makes it difficult to effectively guide model correction.

[0007] Large, complex, thin-walled structural components are widely used due to lightweighting demands. However, their complex structures and non-uniform wall thickness distribution (such as gradient changes or local thickening) lead to significant errors in the thickness assignment of shell elements in traditional CAE modeling. Existing technologies usually assign uniform thickness based on design drawings, or roughly assign average thickness based on partitions. This fails to accurately reflect the actual geometric characteristics, resulting in large deviations between simulation analysis results and experimental data, affecting the reliability of subsequent structural optimization. Therefore, an optimization method that combines experimental data with parametric modeling is urgently needed to solve the problem of insufficient simulation accuracy of large, complex, thin-walled structural components. Summary of the Invention

[0008] The purpose of the present invention is to solve one of the technical problems existing in the prior art to at least a certain extent.

[0009] To this end, one purpose of an embodiment of the present invention is to provide a simulation method for large and complex thin-walled structural parts. This method is driven by experimental data, combines parametric definition of variable thickness partitions with intelligent optimization algorithms, dynamically corrects the thickness distribution of shell units, and can establish a high-precision CAE model of large and complex thin-walled structural parts, significantly improving the simulation accuracy of large and complex thin-walled structural parts and providing a reliable foundation for subsequent structural design.

[0010] Another object of an embodiment of the present invention is to provide a large-scale complex thin-walled structural component simulation system.

[0011] In order to achieve the above technical objectives, the technical solutions adopted by the embodiments of the present invention include:

[0012] In a first aspect, an embodiment of the present invention provides a simulation method for a large complex thin-walled structural part, comprising the following steps:

[0013] Obtain experimental data on physical samples of large, complex, thin-walled structural parts under multiple target operating conditions;

[0014] Constructing an initial CAE model of the large complex thin-walled structural component and determining the thickness variation function of each variable thickness partition;

[0015] Acquire simulation data of the current CAE model under the target working conditions;

[0016] performing error analysis and sensitive area positioning according to the experimental data and the simulation data to obtain the sensitive area to be optimized;

[0017] Performing parameterized modeling and optimization on the wall thickness distribution of the sensitive area to obtain the sensitive area after adjusting the wall thickness;

[0018] The current CAE model is corrected according to the sensitive area after the wall thickness adjustment, and the process returns to the step of obtaining simulation data of the current CAE model under the target working condition until a preset convergence condition is reached to obtain the target CAE model.

[0019] Furthermore, in one embodiment of the present invention, constructing the initial CAE model of the large complex thin-walled structural component and determining the thickness variation function of each variable thickness partition specifically includes:

[0020] Obtaining a three-dimensional geometric model of the large, complex, thin-walled structural component based on design drawings or 3D scanning data;

[0021] Meshing the three-dimensional geometric model, establishing a finite element model based on shell elements and setting material properties to obtain the initial CAE model;

[0022] Dividing the large complex thin-walled structural component into a plurality of variable thickness partitions according to structural characteristics, and determining a thickness variation function of each variable thickness partition;

[0023] The thickness variation function is mapped to shell element properties of the initial CAE model.

[0024] Furthermore, in one embodiment of the present invention, performing error analysis and sensitive area positioning based on the experimental data and the simulation data to obtain the sensitive area to be optimized specifically includes:

[0025] Calculating the relative error of the modal frequency and the consistency of the vibration mode under each of the target working conditions based on the experimental data and the simulation data;

[0026] When the relative error of the modal frequency is greater than a preset first threshold, and / or the consistency of the mode shape is less than a preset second threshold, determining the corresponding variable thickness partition as a sensitive area to be optimized;

[0027] Calculating the sensitivity of the thickness change of each of the variable thickness partitions to the modal frequency based on the experimental data and the simulation data;

[0028] The variable thickness subarea having a sensitivity greater than a preset third threshold is determined as a sensitive area to be optimized.

[0029] Furthermore, in one embodiment of the present invention, the parameterized modeling and optimization of the wall thickness distribution of the sensitive area to obtain the sensitive area after the wall thickness is adjusted specifically includes:

[0030] Taking the parameters of the thickness variation function of the sensitive area as optimization variables, a multi-objective optimization model for modal frequency relative error and vibration shape consistency is constructed;

[0031] Optimizing and solving the multi-objective optimization model by using a response surface model or a genetic algorithm to obtain an optimal parameter combination;

[0032] Adjusting the thickness variation function of the sensitive area according to the optimal parameter combination to obtain the sensitive area after the wall thickness is adjusted;

[0033] The multi-objective optimization model includes an objective function, a thickness process constraint, and a gradient continuity constraint.

[0034] Furthermore, in one embodiment of the present invention, the objective function is:

[0035]

[0036] Among them, M represents the set of modal orders, N represents the set of working conditions, and w irepresents the relative error weight of the i-th mode, Δf i (k) It represents the relative error of the modal frequency between the i-th experimental mode and the i-th simulation mode under working condition k, λ represents the vibration mode consistency weight, Indicates the consistency of vibration mode between the i-th experimental mode and the i-th simulation mode under working condition k.

[0037] Furthermore, in one embodiment of the present invention, the multi-objective optimization model is optimized and solved by a response surface model to obtain an optimal parameter combination, which specifically includes:

[0038] The optimized variable samples were generated by Latin hypercube sampling, and the response surface model was obtained by fitting the response surface using a second-order polynomial.

[0039] A sequential quadratic programming algorithm is used to search and solve the response surface model to obtain the optimal parameter combination.

[0040] Furthermore, in one embodiment of the present invention, the multi-objective optimization model is optimized and solved by a genetic algorithm to obtain an optimal parameter combination, which specifically includes:

[0041] Randomly assigning values ​​to the optimization variables according to the constraint conditions to obtain multiple initial parameter combinations;

[0042] Initializing a population according to the initial parameter combination, and determining a fitness function according to the objective function;

[0043] Calculating the fitness of each individual in the population according to the fitness function;

[0044] Perform selection, crossover, and mutation operations on individuals in the population based on fitness;

[0045] Return to the step of calculating the fitness of each individual in the population according to the fitness function until the fitness change rate is lower than the preset fourth threshold or the number of iterations reaches the preset fifth threshold, and take the individual with the highest current fitness as the optimal parameter combination.

[0046] In a second aspect, an embodiment of the present invention provides a large-scale complex thin-walled structural component simulation system, comprising:

[0047] Real-time data acquisition module, used to obtain experimental data of physical samples of large, complex, thin-walled structural parts under multiple target working conditions;

[0048] A simulation model building module is used to build an initial CAE model of the large complex thin-walled structural component and determine the thickness variation function of each variable thickness partition;

[0049] A simulation data acquisition module is used to obtain simulation data of the current CAE model under the target working conditions;

[0050] A sensitive area positioning module, configured to perform error analysis and sensitive area positioning based on the experimental data and the simulation data to obtain the sensitive area to be optimized;

[0051] A wall thickness distribution optimization module is used to perform parameterized modeling and optimization on the wall thickness distribution of the sensitive area to obtain the sensitive area after the wall thickness is adjusted;

[0052] The simulation model correction module is used to correct the current CAE model according to the sensitive area after the wall thickness adjustment, and return to the step of obtaining simulation data of the current CAE model under the target working conditions until a preset convergence condition is reached to obtain the target CAE model.

[0053] In a third aspect, an embodiment of the present invention provides a large-scale complex thin-walled structural part simulation device, comprising:

[0054] at least one processor;

[0055] at least one memory for storing at least one program;

[0056] When the at least one program is executed by the at least one processor, the at least one processor implements the above-mentioned large-scale complex thin-walled structural component simulation method.

[0057] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium storing a program executable by a processor, wherein the program executable by the processor is used to execute the above-mentioned method for simulating a large complex thin-walled structural part when executed by the processor.

[0058] The advantages and benefits of the present invention will be described in part in the following description and will become apparent from the following description or learned through practice of the present invention:

[0059] The embodiment of the present invention obtains experimental data of physical samples of large and complex thin-walled structural parts under multiple target working conditions, constructs an initial CAE model of the large and complex thin-walled structural parts, determines the thickness variation function of each variable thickness partition, obtains simulation data of the current CAE model under the target working conditions, performs error analysis and locates sensitive areas based on the experimental data and simulation data, obtains sensitive areas to be optimized, performs parameterized modeling and optimization on the wall thickness distribution of the sensitive areas, obtains sensitive areas after the wall thickness is adjusted, corrects the current CAE model based on the sensitive areas after the wall thickness is adjusted, and returns to the step of obtaining simulation data of the current CAE model under the target working conditions until the preset convergence conditions are reached to obtain the target CAE model. The embodiment of the present invention is driven by experimental data, combines parameterized definition of variable thickness partitions with intelligent optimization algorithms, and dynamically corrects the thickness distribution of shell elements, thereby being able to establish a high-precision CAE model of large and complex thin-walled structural parts, significantly improving the simulation accuracy of large and complex thin-walled structural parts and providing a reliable foundation for subsequent structural design. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following introduction is made to the drawings required for use in the embodiments of the present invention. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative work.

[0061] Figure 1 A flowchart of a method for simulating large, complex, thin-walled structural parts provided by an embodiment of the present invention;

[0062] Figure 2 A structural block diagram of a large-scale complex thin-walled structural component simulation system provided by an embodiment of the present invention;

[0063] Figure 3 This is a structural block diagram of a large-scale complex thin-walled structural part simulation device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided for ease of explanation only and do not limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0065] In the description of the present invention, "a plurality" means two or more. The terms "first" and "second" are used solely to distinguish technical features and are not to be construed as indicating or implying relative importance, or as implicitly indicating the number of the indicated technical features, or as implicitly indicating the order of the indicated technical features. Furthermore, unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art.

[0066] Reference Figure 1 The embodiment of the present invention provides a simulation method for large complex thin-walled structural parts, which specifically includes the following steps:

[0067] S101. Obtain experimental data of physical samples of large, complex, thin-walled structural parts under multiple target working conditions;

[0068] S102. Construct an initial CAE model of a large, complex, thin-walled structural component and determine a thickness variation function for each variable thickness partition;

[0069] S103, obtaining simulation data of the current CAE model under target working conditions;

[0070] S104, performing error analysis and sensitive area positioning based on experimental data and simulation data to obtain sensitive areas to be optimized;

[0071] S105. Perform parameterized modeling and optimization on the wall thickness distribution of the sensitive area to obtain the sensitive area after the wall thickness is adjusted;

[0072] S106 , correcting the current CAE model according to the sensitive area after the wall thickness adjustment, and returning to the step of obtaining simulation data of the current CAE model under target working conditions, until a preset convergence condition is reached to obtain the target CAE model.

[0073] Specifically, a complete implementation process of an embodiment of the present invention is as follows:

[0074] 1) Experimental analysis data collection of large and complex thin-walled structural parts

[0075] Physical samples: Physical samples of large, complex, thin-walled structural parts to ensure that their geometric dimensions and material parameters are consistent with actual production.

[0076] Experimental data acquisition: Measure and record the experimental dynamic characteristic parameters of large, complex, thin-walled structural parts.

[0077] 2) Establishment of CAE simulation model

[0078] Geometric model import: Obtain 3D geometric models of large, complex, thin-walled structural parts based on design drawings or 3D scan data.

[0079] Meshing and shell element application: The model is meshed and a finite element model is established using shell elements (2D) to adapt to the characteristics of complex thin-walled structural parts.

[0080] Material property settings: Set material properties (such as elastic modulus, Poisson's ratio, density, etc.) and apply boundary conditions and loads.

[0081] 3) CAE model simulation analysis

[0082] Set the same boundary conditions and working conditions as the experiment to perform simulation analysis and calculation and output the simulation results.

[0083] 4) Benchmarking between experiments and simulations

[0084] Comparative analysis of results: Compare and analyze the experimental results with the simulation results, and calculate the relative error of the analysis results.

[0085] Error source identification: Through error analysis and sensitivity analysis, the error sources in the simulation model are identified, focusing on areas where the wall thickness is inaccurately assigned.

[0086] 5) Sensitivity analysis and optimization direction determination

[0087] Sensitivity analysis: Perform a sensitivity analysis on the model to determine which areas have the greatest impact on the experimental-simulation results due to changes in wall thickness.

[0088] Determine the optimization direction: Based on the results of sensitivity analysis, determine the areas that need to be optimized and the optimization direction.

[0089] 6) Wall thickness adjustment and model correction

[0090] Parametric modeling: In the determined optimization area, the thickness of the shell element is parametrically modeled, and the wall thickness is used as the design variable.

[0091] Application of optimization algorithm: Use optimization algorithms (such as response surface method, genetic algorithm, etc.) to find the optimal wall thickness distribution, taking into account the gradual change and gradient of wall thickness to ensure the smoothness and rationality of the model.

[0092] 7) Iterative optimization and model verification

[0093] Iterative optimization process: Repeat the simulation analysis, benchmarking and error analysis, sensitivity analysis and wall thickness adjustment process until the error between the simulation results and the experimental results meets the design requirements.

[0094] Application of experimental design methods: During the optimization process, experimental design methods (such as orthogonal experiments, uniform design, etc.) can be used to efficiently explore the wall thickness parameter space and improve optimization efficiency.

[0095] 8) Establishment and application of accurate models

[0096] Accurate model establishment: After multiple iterative optimizations, an accurate CAE model that is highly consistent with the actual modal characteristics is obtained.

[0097] Model Application: Use this precise model for subsequent structural optimization design and performance analysis, such as stiffness enhancement and vibration suppression, to improve the dynamic performance and reliability of large, complex, thin-walled structural components.

[0098] It can be seen that the embodiments of the present invention, driven by experimental data, combine parametrically defined variable thickness partitions with intelligent optimization algorithms to dynamically correct the thickness distribution of shell elements, enabling the establishment of high-precision CAE models of large, complex, thin-walled structural components. This significantly improves the simulation accuracy of large, complex, thin-walled structural components and provides a reliable foundation for subsequent structural design. Furthermore, the embodiments of the present invention can improve the predictive accuracy of simulation models, reduce the number of experimental verifications, and lower R&D costs. They can optimize the design of large, complex, thin-walled structural components, enhance the dynamic performance and reliability of the entire vehicle, provide technical support for lightweight automotive design, and promote the application of CAE analysis of body structural components.

[0099] As an optional implementation, an initial CAE model of a large complex thin-walled structural component is constructed, and the thickness variation function of each variable thickness partition is determined, which specifically includes:

[0100] S1021. Obtain a 3D geometric model of a large, complex, thin-walled structural component based on design drawings or 3D scanning data;

[0101] S1022. Meshing the three-dimensional geometric model, establishing a finite element model based on shell elements and setting material properties to obtain an initial CAE model;

[0102] S1023. Divide the large complex thin-walled structural component into a plurality of variable thickness partitions according to structural characteristics, and determine a thickness variation function of each variable thickness partition;

[0103] S1024. Map the thickness variation function to the shell element properties of the initial CAE model.

[0104] Specifically, the process of constructing the initial CAE model and performing thickness mapping in the embodiment of the present invention is as follows:

[0105] 1) Initial CAE model establishment: Use HyperMesh to divide the shell element mesh and generate the geometric model based on the design drawings or 3D scan data.

[0106] 2) Variable thickness parameterized partitioning: Divide complex thin-walled structural parts into multiple sub-regions (such as region A, region B, and region C) according to structural features (such as reinforcement ribs and transition zones), and define a thickness variation function in each region (such as a linear gradient function t(x, y) = t0 + k xx+k y y, or a step function).

[0107] 3) Thickness mapping technology: For gradient areas, use HyperMesh's Thickness Gradient Tool or a custom script to map the parameterized thickness function to the shell element properties; for step-change areas, define discrete thickness values ​​through Property Table partitions.

[0108] Afterwards, run simulation analysis in Altair Radioss or OptiStruct to extract simulation data.

[0109] As an optional implementation, error analysis and sensitive area positioning are performed based on experimental data and simulation data to obtain sensitive areas to be optimized, which specifically includes:

[0110] S1041. Calculate the relative error of modal frequency and consistency of vibration mode under each target working condition based on experimental data and simulation data;

[0111] S1042: When the relative error of the modal frequency is greater than a preset first threshold, and / or the mode shape consistency is less than a preset second threshold, determining the corresponding variable thickness partition as a sensitive area to be optimized;

[0112] S1043. Calculate the sensitivity of the thickness change of each variable thickness partition to the modal frequency based on the experimental data and simulation data;

[0113] S1044: Determine the variable thickness partition whose sensitivity is greater than a preset third threshold as the sensitive area to be optimized.

[0114] Specifically, the embodiment of the present invention calculates the relative error of each working condition between the experiment and the simulation, and evaluates the consistency of the analysis results through corresponding confidence criteria, and locates the sensitive area in combination with sensitivity analysis. The specific process is as follows:

[0115] 1) Relative error calculation and consistency evaluation under multiple working conditions

[0116] (1) Working condition definition and data alignment

[0117] Working condition classification: Define different analysis conditions based on experimental conditions (such as free-free boundary, constrained boundary) or excitation method (hammer method, shaker sweep frequency).

[0118] Data alignment: Import experimental measurement point coordinates into HyperView to ensure one-to-one correspondence between CAE model nodes and experimental measurement points (either through geometric coordinate system matching or manual mapping).

[0119] For areas with missing measurement points, interpolation methods are used to supplement experimental data (such as Kriging interpolation).

[0120] (2) Relative error calculation and confidence criteria

[0121] Modal frequency relative error:

[0122]

[0123] Where i represents the modal order, k represents the operating condition number, represents the experimental modal frequency of mode i under working condition k, represents the simulated modal frequency of mode i under working condition k.

[0124] Average error according to working conditions:

[0125]

[0126] Set a modal frequency error threshold (e.g. 5%) and mark the modal orders that exceed the threshold.

[0127] Mode consistency (MAC value calculation):

[0128] For each mode of each working condition, extract the experimental and simulation modal vibration vector φ exp and φ sim , calculate the modal assurance criterion MAC matrix:

[0129]

[0130] in is the i-th order experimental mode shape vector. is the j-th order simulation mode vibration vector. If the value is lower than 0.8 (the ideal value is 1), the mode shape matching of this order is considered insufficient. If the value is higher than 0.2, there may be a mode order misalignment (the model constraints or material properties need to be checked).

[0131] 2) Sensitive area positioning

[0132] (1) Mode node analysis method:

[0133] Mark all satisfied or The modal-operating condition combination is used to extract the distribution of its vibration mode nodes (areas with minimal displacement or phase reversal). The sensitive areas are associated with the geometric features of complex thin-walled structural parts to determine the thickness partitions that need to be optimized and locate the sensitive areas for thickness assignment:

[0134] Transition zone: the boundary between adjacent partitions (such as the area where the thickness gradient changes).

[0135] Geometric mutation area: local features such as fillets, holes, and rib roots.

[0136] (2) Thickness sensitivity analysis:

[0137] Define the thickness parameter perturbation in HyperStudy and calculate the sensitivity of each partition thickness change to the modal frequency:

[0138]

[0139] Among them, t k is the thickness parameter of the kth partition.

[0140] The partitions whose absolute sensitivity is greater than the set value (such as 1 Hz / mm) are marked as sensitive areas.

[0141] As an optional implementation, parameterized modeling and optimization are performed on the wall thickness distribution of the sensitive area to obtain the sensitive area after the wall thickness is adjusted, which specifically includes:

[0142] S1051. Using the parameters of the thickness variation function of the sensitive area as optimization variables, a multi-objective optimization model for modal frequency relative error and vibration shape consistency is constructed;

[0143] S1052. Optimizing and solving the multi-objective optimization model using a response surface model or a genetic algorithm to obtain an optimal parameter combination;

[0144] S1053. Adjust the thickness variation function of the sensitive area according to the optimal parameter combination to obtain the sensitive area after the wall thickness is adjusted;

[0145] The multi-objective optimization model includes objective function, thickness process constraint and gradient continuity constraint.

[0146] Specifically, the embodiment of the present invention sets the thickness function parameters of the sensitive area (such as the gradient coefficient k x 、k y , gradient thickness values ​​t1, t2) are set as optimization variables, a multi-objective optimization model is constructed based on the relative error of modal frequency and vibration shape consistency, and a response surface model is established or a genetic algorithm (Altair HyperStudy) is used for multivariable optimization to obtain the optimal parameter combination.

[0147] As an optional implementation, the objective function is:

[0148]

[0149] Among them, M represents the set of modal orders, N represents the set of working conditions, and w i represents the relative error weight of the i-th mode, Δf i (k)It represents the relative error of the modal frequency between the i-th experimental mode and the i-th simulation mode under working condition k, λ represents the vibration mode consistency weight, Indicates the consistency of vibration mode between the i-th experimental mode and the i-th simulation mode under working condition k.

[0150] Specifically, the process of determining optimization variables and building a multi-objective optimization model in the embodiment of the present invention is as follows:

[0151] 1) Parameterized thickness function variables

[0152] For the thickness distribution of sensitive areas, the following design variables are defined:

[0153] Gradient region: define thickness distribution function t(x,y)=t0+k x x+k y y, the initial thickness t0, gradient coefficient k x and k y Set as a continuous design variable.

[0154] Step area: define sub-partition thickness values ​​t1, t2, ..., t n It is a discrete design variable, which constrains the thickness variation of adjacent partitions to not exceed ±20%.

[0155] Transition area: coordinates of control points of thickness variation curve (such as node positions of cubic spline interpolation).

[0156] 2) Variable constraints:

[0157] Physical range of thickness: set upper and lower limits of thickness based on the feasibility of manufacturing process

[0158] Gradient continuity: The thickness change of adjacent partitions must meet the process requirements, such as the gradient coefficient k x and k y It should be within a reasonable range.

[0159] 3) Parameter association and automated script thickness mapping:

[0160] Implementing parametric thickness mapping in HyperMesh via Tcl / Python scripting:

[0161] An example of using Python to update the gradient thickness of a region is as follows:

[0162] def update_thickness_gradient(kx,ky,t0):

[0163] hm_entity = get_entity("A certain area") # Get a certain area unit set

[0164] for elemin hm_entity.elements:

[0165] x,y=get_element_centroid(elem)#Get the coordinates of the element center

[0166] t=t0+kx*x+ky*y

[0167] set_element_property(elem,"Thickness",t)

[0168] 4) Construction of multi-objective optimization model

[0169] Objective function:

[0170]

[0171] Weight distribution: low-frequency modes have higher weights (such as w1 = 0.4, w2 = 0.3, w3 = 0.2, ...), and λ is the mode weight (usually 0.3 to 0.5).

[0172] Constraints:

[0173] Thickness process constraint: t min ≤t j ≤t max

[0174] Modal frequency deviation: Δf i (k) ≤2% (final convergence condition).

[0175] As an optional implementation, the multi-objective optimization model is optimized and solved by a response surface model to obtain the optimal parameter combination, which specifically includes:

[0176] S10521. Generate optimized variable samples by Latin hypercube sampling, and use second-order polynomials to perform response surface fitting to obtain a response surface model;

[0177] S10522. Use the sequential quadratic programming algorithm to search and solve the response surface model to obtain the optimal parameter combination.

[0178] Specifically, the process of optimizing and solving the multi-objective optimization model through the response surface model is as follows:

[0179] 1) Design of Experiments (DOE):

[0180] Latin hypercube sampling (LHS) is used in HyperStudy to generate design variable samples to cover the variable space.

[0181] 2) Response surface fitting:

[0182] Using a second-order polynomial model Perform fitting, calculate R by fitting coefficients using the least squares method 2 Value verification model accuracy (requires R 2 >0.9).

[0183] 3) Optimization solution:

[0184] The sequential quadratic programming (SQP) algorithm is used to search for the optimal solution on the response surface.

[0185] As an optional implementation, the multi-objective optimization model is optimized and solved by a genetic algorithm to obtain the optimal parameter combination, which specifically includes:

[0186] S10523. Randomly assign values ​​to optimization variables according to the constraints to obtain multiple initial parameter combinations;

[0187] S10524. Initialize the population according to the initial parameter combination, and determine the fitness function according to the objective function;

[0188] S10525. Calculate the fitness of each individual in the population according to the fitness function;

[0189] S10526. Perform selection, crossover, and mutation operations on individuals in the population based on fitness;

[0190] S10527. Return to the step of calculating the fitness of each individual in the population according to the fitness function until the fitness change rate is lower than the preset fourth threshold or the number of iterations reaches the preset fifth threshold, and take the individual with the highest current fitness as the optimal parameter combination.

[0191] Specifically, the process of optimizing and solving the multi-objective optimization model through genetic algorithm is as follows:

[0192] 1) Genetic Algorithm (GA) Configuration

[0193] Algorithm parameters:

[0194] Population size: 50-100, maximum number of generations: 50, crossover probability: 0.8, mutation probability: 0.05, elite retention: 2

[0195] Fitness function:

[0196] Fitness=1 / (F+∈)(∈=1e-5"to prevent division by zero")

[0197] 2) Parallel Computing: Using the Altair PBS Works job scheduling system, multiple simulation tasks were submitted simultaneously to accelerate optimization, and the iterations were performed until convergence (fitness change rate < 1% or the maximum number of generations reached 50).

[0198] Afterwards, the current CAE model is corrected based on the sensitive area after the wall thickness adjustment, and the simulation data of the current CAE model under the target working conditions is obtained again until the preset convergence conditions are reached. The target CAE model can be obtained.

[0199] In some optional embodiments, the optimized CAE model simulation analysis results are compared with the newly added experimental data to confirm the accuracy improvement; the optimized parameters are saved to the CAE model library for dynamic characteristic prediction and structural optimization of large and complex thin-walled structural parts in subsequent vehicle model development.

[0200] This embodiment of the present invention can address the uneven and gradual thickness variations of complex thin-walled structures. Through parametric modeling, intelligent partitioning, sensitivity analysis, and optimization algorithms, thickness parameters can be efficiently corrected, bringing the simulation model closer to experimental data. By combining CAE modeling, optimization algorithms, and experimental data processing, the system's practicality and efficiency are ensured.

[0201] The above describes the method flow of the embodiment of the present invention. It can be recognized that the embodiment of the present invention is driven by experimental data, combined with parameterized definition of variable thickness partitions and intelligent optimization algorithms, and dynamically corrects the thickness distribution of shell units, and can establish a high-precision CAE model of large and complex thin-walled structural parts, significantly improving the simulation accuracy of large and complex thin-walled structural parts, and providing a reliable foundation for subsequent structural design. In addition, the embodiment of the present invention can improve the prediction accuracy of the simulation model, reduce the number of experimental verifications, reduce R&D costs, optimize the design of large and complex thin-walled structural parts, improve the dynamic performance and reliability of the entire vehicle, and provide technical support for lightweight design of automobiles, and promote the application of CAE analysis of body structural parts.

[0202] Reference Figure 2 , an embodiment of the present invention provides a large-scale complex thin-walled structural part simulation system, comprising:

[0203] Real-time data acquisition module, used to obtain experimental data of physical samples of large, complex, thin-walled structural parts under multiple target working conditions;

[0204] The simulation model building module is used to build the initial CAE model of large and complex thin-walled structural parts and determine the thickness variation function of each variable thickness partition;

[0205] A simulation data acquisition module is used to obtain simulation data of the current CAE model under target working conditions;

[0206] Sensitive area positioning module, used to perform error analysis and sensitive area positioning based on experimental data and simulation data to obtain the sensitive area to be optimized;

[0207] The wall thickness distribution optimization module is used to perform parametric modeling and optimization on the wall thickness distribution of sensitive areas to obtain the sensitive areas after wall thickness adjustment;

[0208] The simulation model correction module is used to correct the current CAE model according to the sensitive area after the wall thickness adjustment, and return to the step of obtaining the simulation data of the current CAE model under the target working conditions until the preset convergence conditions are reached to obtain the target CAE model.

[0209] The contents of the above method embodiments are all applicable to the present system embodiments. The functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0210] Reference Figure 3 The embodiment of the present invention provides a large-scale complex thin-walled structural part simulation device, comprising:

[0211] at least one processor;

[0212] at least one memory for storing at least one program;

[0213] When the at least one program is executed by the at least one processor, the at least one processor implements the above-mentioned method for simulating a large complex thin-walled structural component.

[0214] The contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0215] An embodiment of the present invention further provides a computer-readable storage medium storing a program executable by a processor. When the program is executed by the processor, it is used to perform the above-mentioned method for simulating large complex thin-walled structural parts.

[0216] A computer-readable storage medium according to an embodiment of the present invention can execute a large-scale complex thin-walled structural component simulation method provided by an embodiment of the present invention, can execute any combination of implementation steps of the embodiment of the method, and has the corresponding functions and beneficial effects of the method.

[0217] The embodiment of the present invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs Figure 1 The method shown.

[0218] In some optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the above-mentioned boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operation and logic flow presented herein. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0219] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the above-mentioned functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the routine skills of an engineer. Therefore, a person skilled in the art can implement the present invention set forth in the claims using ordinary skills without undue experimentation. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0220] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the above methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0221] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0222] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable media on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0223] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0224] In the above description of this specification, reference to the terms "one embodiment / example," "another embodiment / example," or "certain embodiments / examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0225] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

[0226] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A simulation method for large complex thin-walled structural parts, characterized in that: The following steps are involved: Obtain experimental data on physical samples of large, complex, thin-walled structural parts under multiple target operating conditions; Constructing an initial CAE model of the large complex thin-walled structural component and determining the thickness variation function of each variable thickness partition; Acquire simulation data of the current CAE model under the target working conditions; performing error analysis and sensitive area positioning according to the experimental data and the simulation data to obtain the sensitive area to be optimized; Performing parameterized modeling and optimization on the wall thickness distribution of the sensitive area to obtain the sensitive area after adjusting the wall thickness; The current CAE model is corrected according to the sensitive area after the wall thickness adjustment, and the process returns to the step of obtaining simulation data of the current CAE model under the target working condition until a preset convergence condition is reached to obtain the target CAE model.

2. A large complex thin-walled structural component simulation method according to claim 1, characterized in that: The initial CAE model of the large complex thin-walled structural component is constructed, and the thickness variation function of each variable thickness partition is determined, which specifically includes: Obtaining a three-dimensional geometric model of the large, complex, thin-walled structural component based on design drawings or 3D scanning data; Meshing the three-dimensional geometric model, establishing a finite element model based on shell elements and setting material properties to obtain the initial CAE model; Dividing the large complex thin-walled structural component into a plurality of variable thickness partitions according to structural characteristics, and determining a thickness variation function of each variable thickness partition; The thickness variation function is mapped to shell element properties of the initial CAE model.

3. The method for simulating large complex thin-walled structural parts according to claim 1, characterized in that: The error analysis and sensitive area positioning are performed based on the experimental data and the simulation data to obtain the sensitive area to be optimized, which specifically includes: Calculating the relative error of the modal frequency and the consistency of the vibration mode under each of the target working conditions based on the experimental data and the simulation data; When the relative error of the modal frequency is greater than a preset first threshold, and / or the consistency of the mode shape is less than a preset second threshold, determining the corresponding variable thickness partition as a sensitive area to be optimized; Calculating the sensitivity of the thickness change of each of the variable thickness partitions to the modal frequency based on the experimental data and the simulation data; The variable thickness subarea having a sensitivity greater than a preset third threshold is determined as a sensitive area to be optimized.

4. The method for simulating large complex thin-walled structural parts according to claim 1, characterized in that: The parameterized modeling and optimization of the wall thickness distribution of the sensitive area to obtain the sensitive area after the wall thickness is adjusted specifically includes: Taking the parameters of the thickness variation function of the sensitive area as optimization variables, a multi-objective optimization model for modal frequency relative error and vibration shape consistency is constructed; Optimizing and solving the multi-objective optimization model by using a response surface model or a genetic algorithm to obtain an optimal parameter combination; Adjusting the thickness variation function of the sensitive area according to the optimal parameter combination to obtain the sensitive area after the wall thickness is adjusted; The multi-objective optimization model includes an objective function, a thickness process constraint, and a gradient continuity constraint.

5. A large complex thin-walled structural component simulation method according to claim 4, characterized in that: The objective function is: Among them, M represents the set of modal orders, N represents the set of working conditions, and w i represents the relative error weight of the i-th mode, Δf i (k) represents the relative error in modal frequency between the i-th order experimental mode and the i-th order simulation mode under working condition k, λ represents the mode consistency weight, Indicates the consistency of vibration mode between the i-th experimental mode and the i-th simulation mode under working condition k.

6. A large complex thin-walled structural component simulation method according to claim 4, characterized in that: The multi-objective optimization model is optimized and solved by a response surface model to obtain the optimal parameter combination, which specifically includes: The optimized variable samples were generated by Latin hypercube sampling, and the response surface model was obtained by fitting the response surface using a second-order polynomial. A sequential quadratic programming algorithm is used to search and solve the response surface model to obtain the optimal parameter combination.

7. The method for simulating large complex thin-walled structural parts according to claim 4, characterized in that: The multi-objective optimization model is optimized and solved by a genetic algorithm to obtain the optimal parameter combination, which specifically includes: Randomly assigning values ​​to the optimization variables according to the constraint conditions to obtain multiple initial parameter combinations; Initializing a population according to the initial parameter combination, and determining a fitness function according to the objective function; Calculating the fitness of each individual in the population according to the fitness function; Perform selection, crossover, and mutation operations on individuals in the population based on fitness; Return to the step of calculating the fitness of each individual in the population according to the fitness function until the fitness change rate is lower than the preset fourth threshold or the number of iterations reaches the preset fifth threshold, and take the individual with the highest current fitness as the optimal parameter combination.

8. A large complex thin-walled structural component simulation system, characterized in that: include: Real-time data acquisition module, used to obtain experimental data of physical samples of large, complex, thin-walled structural parts under multiple target working conditions; A simulation model building module is used to build an initial CAE model of the large complex thin-walled structural component and determine the thickness variation function of each variable thickness partition; A simulation data acquisition module is used to obtain simulation data of the current CAE model under the target working conditions; A sensitive area positioning module, configured to perform error analysis and sensitive area positioning based on the experimental data and the simulation data to obtain the sensitive area to be optimized; A wall thickness distribution optimization module is used to perform parameterized modeling and optimization on the wall thickness distribution of the sensitive area to obtain the sensitive area after the wall thickness is adjusted; The simulation model correction module is used to correct the current CAE model according to the sensitive area after the wall thickness adjustment, and return to the step of obtaining simulation data of the current CAE model under the target working conditions until a preset convergence condition is reached to obtain the target CAE model.

9. A large complex thin-walled structural part simulation device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the large-scale complex thin-walled structural component simulation method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that: The processor-executable program is used to execute a large-scale complex thin-walled structural component simulation method according to any one of claims 1 to 7 when executed by the processor.

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