Nasir Saleem, Berrouk Abdallah, Aamir Asim
Mechanical and Nuclear Engineering Department, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates.
Center for Catalysis and Separation (CeCas), Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates.
Sci Rep. 2024 Oct 26;14(1):25505. doi: 10.1038/s41598-024-76757-4.
Artificial Neural Networks are incredibly efficient at handling complicated and nonlinear mathematical problems, making them very useful for tackling these challenges. Artificial neural networks offer a special computational architecture that is extremely valuable in disciplines like biotechnology, biological computing, and computational fluid dynamics. The present work investigates the applicability of back-propagation artificial neural networks in conjunction with the Levenberg-Marquardt algorithm for evaluating heat transmission in hybrid nanofluids. This work focuses on the computational analysis of a MgO + GO/EG hybrid nanofluid's steady mixed convection flow over an exponentially stretched sheet, considering multiple slip boundary conditions, thermal conductivity, heat generation, and thermal radiation. A nonlinear system of ordinary differential equations is produced from the basic associated partial differential system by performing the proper exponential similarities modifications. For generating benchmark datasets, the resulting ordinary differential equations are processed employing the bvp4c method. Considering benchmark datasets set aside for training (70%), testing (15%), and validation (15%), the Levenberg-Marquardt algorithm, which employs back-propagation in artificial neural networks, is implemented. The accuracy of the suggested strategy for handling nonlinear problems is verified utilizing mean squared error, error histograms, and regression analysis, which are all used to evaluate the methodology. Outstanding agreement is seen when ANN outputs are compared to numerical results. The flow properties, including temperature, velocity, and concentration profiles, are shown graphically and numerically. For practical purposes, it is therefore essential to analyze the flow and heat transfer in hybrid nanofluids over exponentially extending and shrinking surfaces under mixed convection and heat source scenarios. Hybrid nanofluid problems have a wide range of practical and industrial applications, such as medication delivery, manufacturing, microelectronics, nuclear plant cooling, and marine engineering.
人工神经网络在处理复杂的非线性数学问题方面效率极高，这使得它们在应对这些挑战时非常有用。人工神经网络提供了一种特殊的计算架构，在生物技术、生物计算和计算流体动力学等学科中极具价值。本研究探讨了反向传播人工神经网络结合Levenberg-Marquardt算法在评估混合纳米流体热传递方面的适用性。这项工作重点对MgO + GO/EG混合纳米流体在指数拉伸平板上的稳态混合对流流动进行计算分析，考虑了多种滑移边界条件、热导率、热生成和热辐射。通过进行适当的指数相似性修正，从基本的相关偏微分系统中产生了一个常微分方程的非线性系统。为了生成基准数据集，使用bvp4c方法处理得到的常微分方程。考虑到预留用于训练（70%）、测试（15%）和验证（15%）的基准数据集，实施了在人工神经网络中采用反向传播的Levenberg-Marquardt算法。利用均方误差、误差直方图和回归分析来验证所提出的处理非线性问题策略的准确性，这些都用于评估该方法。将人工神经网络的输出与数值结果进行比较时，发现有出色的一致性。以图形和数值方式展示了包括温度、速度和浓度分布在内的流动特性。因此，出于实际目的，分析混合纳米流体在混合对流和热源场景下在指数扩展和收缩表面上的流动和传热至关重要。混合纳米流体问题在药物输送、制造、微电子、核电站冷却和海洋工程等广泛的实际和工业应用中都有涉及。
