Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan, ROC.
Electrical Engineering Department, School of Engineering, Australian College of Kuwait, 13015, Safat, Kuwait City, Kuwait.
Sci Rep. 2021 Nov 19;11(1):22550. doi: 10.1038/s41598-021-98490-y.
Estimation of the effectiveness of Au nanoparticles concentration in peristaltic flow through a curved channel by using a data driven stochastic numerical paradigm based on artificial neural network is presented in this study. In the modelling, nano composite is considered involving multi-walled carbon nanotubes coated with gold nanoparticles with different slip conditions. Modeled differential system of the physical problem is numerically analyzed for different scenarios to predict numerical data for velocity and temperature by Adams Bashforth method and these solutions are used as a reference dataset of the networks. Data is processed by segmentation into three categories i.e., training, validation and testing while Levenberg-Marquart training algorithm is adopted for optimization of networks results in terms of performance on mean square errors, train state plots, error histograms, regression analysis, time series responses, and auto-correlation, which establish the accurate and efficient recognition of trends of the system.
本文提出了一种基于人工神经网络的基于数据驱动随机数值范例的方法，用于估计在弯曲通道中通过蠕动流的 Au 纳米粒子浓度的有效性。在建模中，考虑了包含多壁碳纳米管的纳米复合材料，其表面涂覆有不同滑移条件的金纳米粒子。对物理问题的模型微分系统进行了数值分析，以预测不同情况下速度和温度的数值数据，方法是采用 Adams-Bashforth 方法，这些解被用作网络的参考数据集。数据通过分割成训练、验证和测试三个类别进行处理，而 Levenberg-Marquart 训练算法则用于优化网络结果，以均方误差、训练状态图、误差直方图、回归分析、时间序列响应和自相关的性能为指标，从而准确、有效地识别系统的趋势。
