Water-copper nanofluid flow in flat and ribbed microchannels: numerical modeling and optimization

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

Automated summary

The paper simulated nanofluid forced convection in a microchannel and found that increasing rib height and slip coefficient at high Reynolds numbers and nanofluid volume fractions improved heat transfer rate. The ribs also affected flow physics, with slip velocity decreasing as nanofluid volume fraction and rib height increased. The study used forced heat transfer of water-copper nanofluid in a two-dimensional microchannel and investigated the effects of various parameters on the Nusselt number and other factors. After studying the relationships between different variables, a non-parametric function was estimated using artificial neural networks, and a set of optimal decision parameters was established using Genetic Algorithm.