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Abstract：
The utilization of Maxwell fluid with nanoparticle suspension exhibits promising prospects in enhancing the efficacy of energy conversion and storage mechanisms. They have the potential to be utilized in sophisticated cooling systems for power generation facilities, thereby augmenting the overall energy efficacy. Keeping this in mind, the current research examines the Maxwell nanofluid flow over a rotating disk with the impact of a heat source/sink. The present study centers on the examination of flow characteristics in the existence of a uniform magnetic field. The conversion of governing equations into ordinary differential equations is achieved using appropriate similarity variables. To derive the Nusselt number (Nu) and skin friction (SF) model related to the flow and temperature parameters, the suggested back-propagation artificial neural networking (ANN) technique is used. The Runge-Kutta-Fehlberg fourth-fifth order (RKF-45) method is used to solve the reduced equations and produce the necessary data to create the Nu and SF model. Both the Nu and SF models require 1000 data for training the network, respectively. Graphs are utilized to communicate numerical outcomes. The results concluded that the upsurge in magnetic parameter drops the velocity profile but advances the heat transport. Rise in the thermal conductivity parameter, increases the heat transport.
摘要：
麦克斯韦流体与纳米颗粒悬浮液的利用在增强能量转换和存储机制的功效方面显示出有希望的前景。它们有潜力用于发电设施的复杂冷却系统，从而增强整体能量功效。记住这一点，当前的研究检查了麦克斯韦纳米流体在旋转圆盘上的流动与热源/散热器的影响。本研究的重点是在存在均匀磁场的情况下检查流动特性。使用适当的相似性变量实现了控制方程向常微分方程的转换。为了得出与流量和温度参数相关的努塞尔数（Nu）和皮肤摩擦（SF）模型，使用了建议的反向传播人工神经网络(ANN)技术。Runge-Kutta-Fehlberg四阶（RKF-45）方法用于求解简化的方程，并产生必要的数据以创建Nu和SF模型。Nu和SF模型都需要1000个数据来训练网络，分别。图形用于传达数值结果。结果得出结论，磁参数的激增降低了速度分布，但促进了热传输。导热系数参数上升，增加热传输。
