Comparative analysis of CFD and ANFIS for predicting heat transfer enhancement in water-Fe2O3 nanofluids across various flow regions

Abstract

Models for enhancement of heat transfer in nanofluids made wide use of adaptive neural fuzzy inference system (ANFIS) and the multi-phase mixture model in recent years. These models originate from two separate but complementary branches of engineering: computational mechanics and machine intelligence. Not only have prior studies used only a small subset of nanofluid and flow parameters in their analyses, but no one has ever compared the two methods to determine which one is more applicable to certain flow regimes to forecast how much heat transfer development nanofluids will exhibit. The purpose of this study was to compare the accuracy of two methods ? CFD and ANFIS in predicting the heat transfer improvement of water-Fe2O3 nanofluid for variety of nanofluid formations and flow characteristics, and recommend the method that would be most useful in predicting this enhancement for each flow regime. While ANFIS consistently outperforms the mixture models in prediction of nanofluid heat transfer enhancement, the latter can sometimes produce results that differ greatly from experimental correlation; however, for nanofluid configurations, the mixture model?s predictions can be dependable (with 1% error).