基于粒子群优化算法的本构模型参数识别

PARAMETER IDENTIFICATION FOR CONSTITUTIVE MODEL BASED ON PARTICLE SWARM OPTIMIZATION ALGORITHM

  • 摘要: 基于群体与适应度的概念,应用改进的PSO算法,从随机解出发,提出了基于PSO算法的本构模型参数识别方法. 该方法解决了橡胶类材料大应变时硬化现象的本构模型参数的确定这一难题. 首先通过单轴拉伸本构模型实验,在针对硬化实验曲线存在拐点的情况下,应用PSO算法进行拟合,最后利用简单剪切实验进行验证. 结果表明该方法科学可行,且具有速度快、精度高、易于收敛等优点. 并且有效地解决了本构模型参数识别的困难,可广泛应用于各种复杂材料.

     

    Abstract: On basis of concepts of population and adaptive degree, anew method is proposed to identifyparameters in the constitutive model used to improve Particle Swarm OptimizationAlgorithm starting from a stochastic solution. This method can solvedifficult problems of identifyingparameters in a constitutive model for hardening rubber-like materialswith large strain. Firstly, by a constitutive model test of uniaxial tension, a point of inflexion in the experimental curve is obtained, the PSO is used toidentify the parameter, and then the result is testified by a simpleshearing test. The result shows that the PSO method enjoysefficiency, high precision and quick convergence,in identifying constitutive parameters and it canbe widely used for various complex materials.

     

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