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Parameter identification of a non-linear viscoelastic model for engineering polymers using a genetic algorithm

机译:使用遗传算法的工程聚合物非线性粘弹性模型参数辨识

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This paper presents an alternative approach for determining non-linear viscoelastic parameters of a constitutive model for glassy polymers using a multi-objective diversity control oriented genetic algorithm (MODCGA). The algorithm is a stochastic multi-objective optimisation technique. In this work, minimisation objectives are defined from errors between the experimental data (creep-recovery and constant strain rate compressive tests) and the simulation results. The parameters obtained by the MODCGA are found to be similar to that obtained previously using a knowledge-based iterative trial-and-error manual fitting method. However, the use of MODCGA requires much less human-computer interaction during the optimisation process and more refined solutions can be obtained without initial guess values being provided.
机译:本文提出了一种替代方法,该方法使用面向多目标多样性控制的遗传算法(MODCGA)确定玻璃态聚合物本构模型的非线性粘弹性参数。该算法是一种随机的多目标优化技术。在这项工作中,根据实验数据(蠕变恢复和恒定应变率压缩测试)与模拟结果之间的误差定义了最小化目标。发现通过MODCGA获得的参数与以前使用基于知识的迭代试错人工拟合方法获得的参数相似。但是,在优化过程中使用MODCGA所需的人机交互少得多,并且无需提供初始猜测值即可获得更完善的解决方案。

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