Abstract In this paper, numerical study and thermal prediction for a nanofluid jet impingement cooling of an isothermal hot surface with an adiabatic rotating cylinder were performed. Finite volume method was used for the solution of resulting governing equations along with the boundary conditions. Influence of various pertinent parameters such as Reynolds number (between 100 and 400), angular rotational velocity of the cylinder (between −0.1 and 0.1), horizontal location of the cylinder (between 0 and 3.75w) and solid particle volume fraction (between 0 and 0.04) on the fluid flow thermal characteristics were examined. It was observed that cylinder rotation and its location affect the cooling performance of the hot surface. It can be used as control element for heat and fluid flow. At the highest angular rotational speed as compared to motionless cylinder case, average Nusselt number reduces by about 20.16 % for clockwise rotation. Solid particle addition to the base fluid affects the variation of first and secondary peaks in the Nusselt number along the hot wall. At the highest solid when the cylinder is away from the inlet slot and average Nusselt number enhancement is by about 8.08 % at the highest volume fraction. An efficient modeling strategy was developed based on proper orthogonal decomposition and radial basis neural networks for thermal predictions. Accurate and fast results were achieved as compared to high fidelity computational fluid dynamics simulation results.

中文翻译：

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旋转圆柱体作用下等温面的纳米流体射流冲击冷却分析与预测建模

摘要 本文对具有绝热旋转圆柱体的等温热表面的纳米流体射流冲击冷却进行了数值研究和热预测。有限体积法用于求解所得到的控制方程以及边界条件。各种相关参数的影响，例如雷诺数（100 到 400 之间）、圆柱的角旋转速度（-0.1 到 0.1 之间）、圆柱的水平位置（0 到 3.75w 之间）和固体颗粒体积分数（0 之间）和 0.04) 对流体流动热特性进行了检验。据观察，气缸旋转及其位置会影响热表面的冷却性能。它可以用作热量和流体流动的控制元件。与静止圆柱体情况相比，在最高角转速下，顺时针旋转的平均努塞尔数减少了约 20.16%。添加到基液中的固体颗粒会影响努塞尔数沿热壁的第一和第二峰的变化。在圆柱体远离入口槽时的最高固体和最高体积分数下平均努塞尔数增加约 8.08%。基于适当的正交分解和径向基神经网络开发了一种有效的建模策略，用于热预测。与高保真计算流体动力学模拟结果相比，获得了准确和快速的结果。添加到基液中的固体颗粒会影响努塞尔数沿热壁的第一和第二峰的变化。在圆柱体远离入口槽时的最高固体和最高体积分数下平均努塞尔数增加约 8.08%。基于适当的正交分解和径向基神经网络开发了一种有效的建模策略，用于热预测。与高保真计算流体动力学模拟结果相比，获得了准确和快速的结果。添加到基液中的固体颗粒会影响努塞尔数沿热壁的第一和第二峰的变化。在圆柱体远离入口槽时的最高固体和最高体积分数下平均努塞尔数增加约 8.08%。基于适当的正交分解和径向基神经网络开发了一种有效的建模策略，用于热预测。与高保真计算流体动力学模拟结果相比，获得了准确和快速的结果。基于适当的正交分解和径向基神经网络开发了一种有效的建模策略，用于热预测。与高保真计算流体动力学模拟结果相比，获得了准确和快速的结果。基于适当的正交分解和径向基神经网络开发了一种有效的建模策略，用于热预测。与高保真计算流体动力学模拟结果相比，获得了准确和快速的结果。