热导率
粒子群优化
人工神经网络
体积分数
材料科学
纳米复合材料
算法
复合数
人工智能
机器学习
计算机科学
生物系统
复合材料
生物
作者
Bokai Liu,Nam Vu-Bac,Timon Rabczuk
标识
DOI:10.1016/j.compstruct.2021.114269
摘要
In this paper, we propose a hybrid machine learning method to predict the thermal conductivity of polymeric nanocomposites (PNCs). Therefore, a combination of artificial neural network (ANN) and particle swarm optimization (PSO) is applied to estimate the relationship between variable input and output parameters. The ANN is used for modeling the composite while PSO improves the prediction performance through an optimized global minimum search. We select the thermal conductivity of the fibers and the matrix, the kapitza resistance, volume fraction and aspect ratio as input parameters. The output is the macroscopic (homogenized) thermal conductivity of the composite. The results show that the PSO significantly improves the predictive ability of this hybrid intelligent algorithm, which outperforms traditional neural networks.
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