Khan Muhammad Fawad, Sulaiman Muhammad, Tavera Romero Carlos Andrés, Alkhathlan Ali
Department of Mathematics, Abdul Wali Khan University, Mardan 23200, Pakistan.
COMBA R&D Laboratory, Faculty of Engineering, Universidad Santiago de Cali, Cali 76001, Colombia.
Entropy (Basel). 2021 Oct 31;23(11):1448. doi: 10.3390/e23111448.
In this work, an important model in fluid dynamics is analyzed by a new hybrid neurocomputing algorithm. We have considered the Falkner-Skan (FS) with the stream-wise pressure gradient transfer of mass over a dynamic wall. To analyze the boundary flow of the FS model, we have utilized the global search characteristic of a recently developed heuristic, the Sine Cosine Algorithm (SCA), and the local search characteristic of Sequential Quadratic Programming (SQP). Artificial neural network (ANN) architecture is utilized to construct a series solution of the mathematical model. We have called our technique the ANN-SCA-SQP algorithm. The dynamic of the FS system is observed by varying stream-wise pressure gradient mass transfer and dynamic wall. To validate the effectiveness of ANN-SCA-SQP algorithm, our solutions are compared with state-of-the-art reference solutions. We have repeated a hundred experiments to establish the robustness of our approach. Our experimental outcome validates the superiority of the ANN-SCA-SQP algorithm.
在这项工作中，一种流体动力学中的重要模型通过一种新的混合神经计算算法进行分析。我们考虑了具有沿流向压力梯度的质量在动态壁面上传递的福克纳 - 斯坎（FS）模型。为了分析FS模型的边界层流动，我们利用了最近开发的一种启发式算法——正弦余弦算法（SCA）的全局搜索特性以及序列二次规划（SQP）的局部搜索特性。利用人工神经网络（ANN）架构构建数学模型的级数解。我们将我们的技术称为ANN - SCA - SQP算法。通过改变沿流向压力梯度质量传递和动态壁面来观察FS系统的动态特性。为了验证ANN - SCA - SQP算法的有效性，我们将我们的解与当前最先进的参考解进行比较。我们重复了一百次实验以确定我们方法的稳健性。我们的实验结果验证了ANN - SCA - SQP算法的优越性。
