Purpose

This paper aims to simulate the nanofluid forced convection in a microchannel. According to the results, at high Reynolds numbers and higher nanofluid volume fractions, an increase in the rib height and slip coefficient further improved the heat transfer rate. The ribs also affect the flow physics depending on the Reynolds number so that the slip velocity decreases with increasing the nanofluid volume fraction and rib height.

Design/methodology/approach

Forced heat transfer of the water–copper nanofluid is numerically studied in a two dimensional microchannel. The effects of the slip coefficient, Reynolds number, nanofluid volume fraction and rib height are investigated on the average Nusselt number, slip velocity on the microchannel wall and the performance evaluation criterion.

Findings

In contrast, the slip velocity increases with increasing the Reynolds number and slip coefficient. Afterwards, a non-parametric function estimation is performed relying on the artificial neural network.

Originality/value

Finally, the Genetic Algorithm was used to establish a set of optimal decision parameters for the problem

中文翻译：

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平面和肋状微通道中的水-铜纳米流体流动：数值建模和优化

目的

本文旨在模拟微通道中的纳米流体强制对流。根据结果​​，在高雷诺数和更高的纳米流体体积分数下，肋高度和滑移系数的增加进一步提高了传热率。肋还根据雷诺数影响流动物理，因此滑移速度随着纳米流体体积分数和肋高度的增加而降低。

设计/方法/方法

在二维微通道中对水-铜纳米流体的强制传热进行了数值研究。研究了滑移系数、雷诺数、纳米流体体积分数和肋高度对平均努塞尔数、微通道壁上的滑移速度和性能评价标准的影响。

发现

相反，滑移速度随着雷诺数和滑移系数的增加而增加。之后，依靠人工神经网络进行非参数函数估计。

原创性/价值

最后，利用遗传算法为问题建立一组最优决策参数