Vehicle tailgate structure optimization method based on two-step improved PSO (particle swarm optimization) algorithm

A technology for improving particle swarms and optimization algorithms, applied in design optimization/simulation, computing, special data processing applications, etc.

Inactive Publication Date: 2018-07-10
SHANGHAI JIAO TONG UNIV
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  • Abstract
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  • Claims
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Problems solved by technology

[0004] Aiming at the problem characteristics of the existing automobile tailgate structure optimization and the problems of the current constraint optimization algorithm, the present invention proposes an automobile tailgate structure optimization method based on a two-step improved particle swarm optimization algorithm, which can record flying to the vicinity of the constraint boundary The particles then obtain the position information of the constraint boundary, and then perform a local search at the constraint boundary, which can relax the adjustment requirements for the control parameters, and finally improve the global optimization ability of the constraint particle swarm optimization algorithm and improve the results of the optimal design of the car tailgate structure

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  • Vehicle tailgate structure optimization method based on two-step improved PSO (particle swarm optimization) algorithm
  • Vehicle tailgate structure optimization method based on two-step improved PSO (particle swarm optimization) algorithm
  • Vehicle tailgate structure optimization method based on two-step improved PSO (particle swarm optimization) algorithm

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Embodiment Construction

[0028] Such as figure 1 As shown, the specific steps of this embodiment include:

[0029] Step 1) Use Hypermesh as the pre-processing software platform to carry out finite element modeling on the tailgate of the car.

[0030] Such as figure 2 As shown, the established finite element model of the automobile tailgate includes 70,961 elements and 71,484 nodes, including 2,053 triangle elements, accounting for 2.89%, and the accuracy of the finite element model meets the requirements.

[0031] Step 2) as in image 3 As shown, the thickness of the tailgate inner panel, spoiler and lower trim in the finite element model is selected as the design variables, and X 1 、X 2 and x 3 express.

[0032] Using the finite element model of the automobile tailgate established in step 1), aiming at 10 stiffness conditions such as outer panel stiffness, dent resistance, lateral stiffness, torsional stiffness, tailgate handle mounting point stiffness, and side strut mounting point stiffness,...

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Abstract

The invention provides a vehicle tailgate structure optimization method based on a two-step improved PSO (particle swarm optimization) algorithm. The method comprises the following steps: determiningdesign variables from a finite element model of a vehicle tailgate structure and calculating response of the model at each sample point, and establishing a high-precision surrogate model between eachworking condition response and the design variables; establishing a target function for the optimization of the vehicle tailgate structure, and using the two-step improved PSO algorithm, namely, a constrained PSO algorithm to perform global search and local search at a constrained boundary for optimization to obtain the optimized vehicle tailgate structure. The method has the advantages that the problem of dependence of the PSO algorithm optimizing capacity on adjustment of control parameters is solved, the optimizing capacity of the PSO algorithm in optimization of the vehicle tailgate structure with complex constraints can be improved effectively, and an optimized design result of the vehicle tailgate structure is improved remarkably.

Description

technical field [0001] The invention relates to a technology in the field of automobile manufacturing, in particular to an automobile tailgate structure optimization method based on a two-step improved particle swarm optimization algorithm. Background technique [0002] The tailgate of a car is one of the load-bearing plates of the car body. It is generally required that the tailgate of a car must have a certain ability to bear loads and resist deformation. There are 10 working conditions including torsional stiffness, tailgate handle mounting point stiffness, and side strut mounting point stiffness. Numerous stiffness-constrained working conditions put forward higher requirements for the constrained optimization algorithm used in the structural optimization design of the automotive tailgate. [0003] At present, the constraint processing mechanism of particle swarm optimization algorithm is mainly aimed at the adjustment of control parameters, but the adjustment process of ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F30/15G06F30/23
Inventor 刘钊李泽阳张海潮朱平
Owner SHANGHAI JIAO TONG UNIV
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