The analysis of electromagnetic maxwell nanofluid flow over an axisymmetric cylindrical surface using artificial neural networks for active and passive control

This work presents a novel approach to active and passive control of fluid flow on a stretching cylinder, considering the collective effects of electromagnetism, and chemical reactivity. The Maxwell fluid flow over the stretched surface of a cylinder is responsible for the improvement in heat transfer. The proposed methodology leverages the power of Artificial Neural Networks (ANN) to effectively manipulate and optimize fluid flow behavior in this complex system. By integrating ANN into the control strategy, we achieve enhanced propulsion system design and optimization capabilities, enabling improved performance around cylinders. Additionally, insights into the interconnection of electromagnetic effects and heat sources provide valuable implications for thermal management systems, where understanding and controlling fluid flow behavior are essential for efficient heat transfer. The design approach for addressing different aspects of the fluid problem consists of a sequence of operations, which encompass training, testing, and validation using a reference dataset. Furthermore, visual representations are employed to portray the flow model parameters concerning momentum, energy, and concentration profiles, effectively illustrating the results of the investigation, all of which are closely aligned with each other on average 10-8.