Modeling and estimation of thermal performance factor of MgO-water nanofluids flow by artificial neural network based on experimental data

Using a simple computational tool with a very high connection and the determining role of connections between neurons in identifying network function are the two similarities between natural and Artificial Neural Networks (ANNs). In this article, the very significant subjects of nanofluids efficiency and the thermal performance factor of these fluids operating as heat transfer have been investigated. To model the data in this article, ANNs are used. This modeling is presented for different.and modeling results have been compared with experimental data for MgOwater nanofluids flow. The data relating to the efficiency of these nanofluids use complicated patterns, so a type of ANNs has been used with the ability to distinguish the number of neurons required for modeling and following the data pattern. To evaluate the accuracy of the modeling using ANN, the experimental data have been compared with ANN results in different volume fraction of nanoparticles (phi). The results show that ANN has a better agreement with experimental data in estimating the data with a higher Reynolds number.

Automated summary

The article discusses the efficiency of nanofluids and the thermal performance factor, using Artificial Neural Networks for modeling and predicting experimental data. The results show that ANN has better agreement with experimental data in estimating data at high Reynolds numbers.