Comprehensive heat transfer correlation for water/ethylene glycol-based graphene (nitrogen-doped graphene) nanofluids derived by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS)

摘要: Herein, artificial neural network and adaptive neuro-fuzzy inference system are employed for modeling the effects of important parameters on heat transfer fluid flow characteristics a car radiator followed by comparing with those experimental results testing data. To this end, two novel nanofluids (water/ethylene glycol-based graphene nitrogen-doped graphene nanofluids) were experimentally synthesized. Then, Nusselt number was modeled respect to variation inlet temperature, Reynolds number, Prandtl concentration, which defined as the input (design) variables. reach reliable results, we divided these data into train test sections accomplish modeling. Artificial networks instructed major part The other part primary had been considered appropriateness models entered models. Finally, predictad compared evaluate validity. Confronted high-level validity confirmed that proposed procedure by BPNN one hidden layer five neurons is efficient it can be expanded all water/ethylene carbon nanostructures nanofluids. our data collection from model could present fundamental correlation calculating including or graphene.