K. Kadri, A. Kallel, G. Guerard, A. Ben Abdallah, S. Ballut, J. Fitoussi, M. Shirinbayan
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引用次数: 0
摘要
本文旨在研究复合材料在静压条件下的降解问题。高压条件与氢气罐内遇到的情况类似。损伤建模用于评估氢气罐在高压下的行为。提出了一种将有限元法(FEM)模拟和机器学习(ML)算法相结合的实用方法。将代表性体积元素(RVE)与行为法则和损伤法则的选择结合起来作为输入数据。使用了 K-近邻(k-NN)和具有动态时间扭曲度量的特殊 K-NN 等 ML 分类算法。通过树枝图的可视化分层聚类,可以展示与纤维、基体特性和纤维体积分数有关的复合材料参数对外部静压下应变降解的影响。接下来,将展示降解值较低的最佳 RVE。
Study of composite polymer degradation for high pressure hydrogen vessel by machine learning approach
The aim of this article is to study the degradation of a composite material under static pressure. The high pressure condition is similar to the one encountered inside hydrogen tanks. Damage modeling was used to evaluate the behavior of hydrogen tanks to high pressure. A practical approach, coupling a finite element method (FEM) simulation and machine learning (ML) algorithm, is suggested. The representative volume element (RVE) was used in association with a choice of a behavior law and a damage law as an input data. Algorithms for ML classification such as K-nearest neighbors (k-NN) and a special k-NN with a dynamic time warping metric were used. The hierarchical clustering through dendrograms visualizations allowed to exhibit the impact of composite parameters in relation to fiber, matrix properties and fiber volume fraction on the strain degradation under external static pressure. Continuing this, the optimum RVE which shows a low degradation value will be exhibited.