Vehicle air spring engineering optimization design method based on adaptive proxy model

Abstract

The invention discloses a vehicle air spring engineering optimization design method based on a self-adaptive chaos polynomial-Kriging proxy model. The method comprises the steps: establishing an air spring high-fidelity nonlinear fluid-solid coupling finite element model and a parameterized model thereof according to small sample experimental data; on the basis of a test design method and a statistical regression method, establishing a self-adaptive chaos polynomial-Kriging agent model by using a small amount of simulation calculation results to approach a complex and expensive simulation model; and finally, performing parameter global optimization on the performance function of the air spring system based on the self-adaptive agent model and a multi-objective intelligent optimization method. According to the method, the air spring system optimization design is achieved with the small number of high-fidelity model simulation times, the research and development period can be greatly shortened, the research and development cost is saved, an efficient means can be provided for rapid research and development design decision making of the air spring, and the method has good engineering adaptability and application prospects.

Description

technical field [0001] The invention belongs to the technical field of air spring design, and in particular relates to an engineering optimization design method of an air spring for vehicles based on an adaptive agent model. Background technique [0002] Air spring is the core component of vehicle air suspension. It uses the compressibility of air to make vehicle suspension have superior performance such as low frequency, variable stiffness and cushioning. A well-designed air spring system can greatly improve the driving comfort and driving stability of the vehicle, is conducive to the lightweight design of the vehicle, and can significantly reduce the impact and damage of the vehicle on the road surface while effectively reducing fuel consumption and saving costs. The national standard GB 1589-2016 has strict restrictions on road vehicle overloading and GB 7258-2017 has mandatory requirements for semi-trailers to be equipped with air suspension, which has brought unpreceden...

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Problems solved by technology

In the optimization calculation process, the simulation solution process needs to be called multiple times and the gradient information needs to be calculated, which makes the calculation cost higher; and because the performance index and constraints may be high-dimensional nonlinear functions of the design variables, the gradient calculation faces numerical difficulties, which may lead to optimization calculations. Does not converge or converges to a local optimum

Another method is to regard the air spring finite element simulation model as a black box function, and use intelligent optimization algorithms such as genetic algorithm and simulated annealing algorithm to search for the global optimal solution in the design space. Although gradient calculation is avoided, more calls are required The simulation solution process of the original model severely restricts the optimization design efficiency

In addition, actual engineering design often requires multiple rounds of iterations to adjust optimization parameters or make trade-offs and compromises between conflicting performance indicators, which will also require repeated high-fidelity but extremely time-consuming numerical simulation analysis, resulting in more expensive Computational costs and longer design cycles make it difficult to support rapid design decisions for air springs

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