Numerical simulation of magnetohydrodynamic radiative double-diffusive convective heat and mass transfer of a ternary hybrid nanofluid with thermophoretic particle deposition in an inclined annulus is studied. Both sides of cylinders are preserved at uniform temperatures, while the remaining sides are thermally insulated. The coupled nonlinear partial differential equations are solved using a finite difference approach. The detailed computational results of heat and mass transfer rate, temperature, concentration and flow fields are presented and discussed for a distinct range of significant physical parameters. The results demonstrated that increasing the angle of the inclination factor increases fluid flow. In light of buoyancy, when the annular cavity is set downward, the enhanced shear velocity is transported upwards, where it reaches its optimal temperature. The higher temperature difference leads to augmented dynamic fluid motion, particularly in the radial direction, as the system seeks to balance the thermal energy through convective transfer. The Brownian motion parameter has abrupt mobility of nanoparticles in liquid, which promotes particle collisions with liquid molecules, yielding kinetic energy. Increased values of thermophoretic parameters augment the thermophoresis force. Thermophoretic particle deposition increases the concentration of nanoparticles in an annulus due to the migration of particles from hot to cold regions under the influence of temperature gradient. The heat and mass transfer characteristics of ternary hybrid nanofluid are forecasted through an artificial neural network-based Levenberg–Marquardt backpropagated algorithm. The built model shows the heat and mass transfer rate root mean squared values through the Levenberg–Marquardt algorithm as one and mean squared error values as 1e-07 and 1e-08 respectively.

中文翻译：

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基于集成神经网络的热泳粒子沉积斜环内三元混合纳米流体流动的热溶液辐射双扩散对流模拟


研究了斜环内热泳颗粒沉积三元混合纳米流体磁流体动力学辐射双扩散对流传热传质的数值模拟。圆筒的两侧均保持均匀的温度，而其余侧则隔热。使用有限差分法求解耦合非线性偏微分方程。针对不同范围的重要物理参数，给出并讨论了传热传质速率、温度、浓度和流场的详细计算结果。结果表明，增加倾斜因子的角度会增加流体流量。考虑到浮力，当环形腔向下设置时，增强的剪切速度向上传输，在那里达到最佳温度。较高的温差导致动态流体运动增强，特别是在径向方向，因为系统试图通过对流传递来平衡热能。布朗运动参数使纳米颗粒在液体中具有突然的流动性，从而促进颗粒与液体分子碰撞，产生动能。热泳参数值的增加会增强热泳力。由于颗粒在温度梯度的影响下从热区域迁移到冷区域，热泳颗粒沉积增加了环形空间中纳米颗粒的浓度。通过基于人工神经网络的 Levenberg-Marquardt 反向传播算法来预测三元混合纳米流体的传热传质特性。 构建的模型将通过 Levenberg-Marquardt 算法得出的传热和传质均方根值显示为 1，均方误差值分别为 1e-07 和 1e-08。