Buckling Analysis of Cantilever Nanoactuators Immersed in an Electrolyte: A Close Form Solution Using Duan-Rach Modified Adomian Decomposition Method

A new modified Adomian Decomposition Method (ADM) is utilized to obtain an analytical solution for buckling of the nanocantilever actuators immersed in liquid electrolytes. The nanoactuators in electrolytes are subject to different nonlinear forces including ionic concentration, van der Waals, external voltage and electrochemical forces. The Duan–Rach modified Adomian decomposition method is utilized to obtain a full explicate solution for the buckling of nanoactuators free of any undetermined coefficients. The results are compared with the results of Wazwas ADM as well as the results of a finite element method available in the literature and found in excellent agreement

An intelligence parameter classification approach for energy storage and natural convection and heat transfer of nano-encapsulated phase change material: Deep neural networks

A deep neural network is utilized to classify the parameters of a natural convection heat transfer of a nano-encapsulated phase change material suspension using the isotherm images for the first time. A natural convection flow and heat transfer simulation dataset were created and used as a training and validation tool. Then, a deep neural network, consisting of three parts, was used for the classification task. The first part was made of several conventional layers, and a rectified linear unit activation layer supported each layer. The second part was a preparation layer for reshaping from 2D images to 1D classification. The third layer was made of a classifier layer. The results showed that the impact of the Rayleigh number and volume concentrations of nanoparticles could be classified by 99.8 and 93.32% accuracy, respectively. However, the Stefan number was classified weakly. As a part of the current research, a transfer learning approach was used to improve accuracy. The learning transfer approach was quite effective and improved the accuracy of the Stefan number classification by 16.6%