Mehrabi, M.Sharifpur, MohsenMeyer, Josua P.2013-10-312013-10-312013-12Mehrabi, M, Sharifpur, M & Meyer JP 2013, 'Modelling and multi-objective optimisation of the convective heat transfer characteristics and pressure drop of low concentration TiO2-water nanofluids in the turbulent flow regime', International Journal of Heat and Mass Transfer, vol. 67, no.12, pp. 646-653.0017-9310 (print)1879-2189 (online)10.1016/j.ijheatmasstransfer.2013.08.013http://hdl.handle.net/2263/32230In the research for this paper, a GA–PNN hybrid system was used for modelling the convective heat transfer characteristics and pressure drop of TiO2–water a nanofluid in a fully developed turbulent flow based on an experimentally obtained train and test data set. Models were developed for the Nusselt number and the pressure drop of the nanofluid as a function of Reynolds and Prandtl numbers, nanofluid volume concentration and average nanoparticle diameter. The results of the proposed models were compared with experimental data and with existing correlations. The validity of the proposed models was benchmarked by using statistical criteria and NSGA-II was used for multi-objective optimisation for the convective heat transfer. In the optimisation procedure model, the Nusselt number and pressure drop were considered as the objective functions. However, when the set of decision variables was selected based on the Pareto set, it ensures the best possible combination of objectives. The Pareto front of multi-objective optimisation of the Nusselt number and pressure drop proposed models were also shown and discussed. It was found that application of the multi-objective optimisation method for the turbulent convective heat transfer characteristics and pressure drop of TiO2–water nanofluid could lead to finding the best design points based on the importance of the objective function in the design procedure.en© 2013 Elsevier. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in International Journal of Heat and Mass Transfer. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Heat and Mass Transfer, vol. 67, no.12, pp. 646-653, 2013, doi : 10.1016/j.ijheatmasstransfer.2013.08.013TiO2–water nanofluidNusselt numberPressure dropGenetic algorithm–polynomial neuralNetwork (GA–PNN)Group method of data handling (GMDH)Multi-objective optimisationNon-dominated sorting genetic algorithm II (NSGA-II)Heat -- TransmissionNanofluids -- Thermal propertiesMicrofluidicsTitanium dioxide -- Thermal propertiesPressure -- MeasurementPostprint Article