The Casson-Maxwell model is particularly useful for studying viscoplastic fluids or fluids with yield stress, making it applicable to various engineering applications, including extrusion processes, coating applications, and biomedical fluid dynamics. Casson-Maxwell fluid flow enhances mass transfer rates due to the combined effects of non-Newtonian viscosity and viscoelastic behavior. This is particularly useful in processes where mass transfer limitations play a significant role, such as in multiphase reactions or reactive distillation systems. In the context of the above applications the present model, is the combination of Casson- Maxwell fluids that flow through the varying gap of the spinning cone and disk system (CDS) for the heat transfer enhancement. The magnetic field is also imposed in the upright direction to the flow field. The solution of the transform equations has been obtained through artificial neural networks (ANN). The skin friction, heat transfer rate, and comparative analysis have been done. The Casson-Maxwell parameters that depend on the viscoelastic behavior and high viscosity term, causes the fluid to slow down and tends to store the temperature, for a long time as compared to traditional fluids. The radial component of velocity decreases due to the increase in magnetic field. The relative error of the reference and targeted dates is calculated to demonstrate the best precision efficiency of ANN, with a range of 10 to 10.

中文翻译：

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人工神经网络策略分析通过锥盘系统空间的磁流体动力学卡森-麦克斯韦纳米流体流动

Casson-Maxwell 模型对于研究粘塑性流体或具有屈服应力的流体特别有用，使其适用于各种工程应用，包括挤出工艺、涂层应用和生物医学流体动力学。由于非牛顿粘度和粘弹性行为的综合作用，卡森-麦克斯韦流体流动提高了传质速率。这在传质限制发挥重要作用的过程中特别有用，例如在多相反应或反应蒸馏系统中。在上述应用的背景下，本模型是流过旋转锥盘系统 (CDS) 的不同间隙以增强传热的卡森-麦克斯韦流体的组合。磁场也沿垂直方向施加到流场。变换方程的解是通过人工神经网络（ANN）获得的。进行了表面摩擦、传热速率和对比分析。与传统流体相比，取决于粘弹性行为和高粘度项的卡森-麦克斯韦参数会导致流体减速并倾向于长时间储存​​温度。由于磁场的增加，速度的径向分量减小。计算参考日期和目标日期的相对误差，以证明 ANN 的最佳精度效率，范围为 10 到 10。