Local-Nusselt-number-correlations-for-heat-transfer-of-nanofluids-by-genetic-programming

Here, we derived correlation equations for estimating Nu_x of nanofluids in a horizontal circular tube by Genetic Programming (GP). From literature, we collected a data set for varied nanoparticles (e.g., Al2O3, multi-walled Carbon Nanotubes, Graphene Nanoplates, ZnO, Graphene Oxide) in varied fluids (e.g., water, ethylene glycol/water). By appropriately designing the program, correlations were successfully derived in mathematical equations for three categories of flow regimes: all ranges, laminar, and transition/turbulent. Results show the possibility of obtain accurate Nu_x models ( R^2>0.9) for all flow regimes when GP tree structures are larger than three levels (depth 3).