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)
| Autor: | Mehdi Shanbedi, Maryam Savari, Amin Hedayati Moghaddam, Mohamad Nizam Ayub, Ahmad Amiri |
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| Rok vydání: | 2017 |
| Předmět: |
Fluid Flow and Transfer Processes
Adaptive neuro fuzzy inference system Materials science Artificial neural network 020209 energy Prandtl number Nanotechnology 02 engineering and technology 021001 nanoscience & nanotechnology Condensed Matter Physics Nusselt number chemistry.chemical_compound symbols.namesake Nanofluid chemistry Heat transfer 0202 electrical engineering electronic engineering information engineering symbols 0210 nano-technology Biological system Ethylene glycol Test data |
| Zdroj: | Heat and Mass Transfer. 53:3073-3083 |
| ISSN: | 1432-1181 0947-7411 |
| Popis: | Herein, artificial neural network and adaptive neuro-fuzzy inference system are employed for modeling the effects of important parameters on heat transfer and fluid flow characteristics of a car radiator and followed by comparing with those of the experimental results for testing data. To this end, two novel nanofluids (water/ethylene glycol-based graphene and nitrogen-doped graphene nanofluids) were experimentally synthesized. Then, Nusselt number was modeled with respect to the variation of inlet temperature, Reynolds number, Prandtl number and concentration, which were defined as the input (design) variables. To reach reliable results, we divided these data into train and test sections to accomplish modeling. Artificial networks were instructed by a major part of experimental data. The other part of primary data which had been considered for testing the appropriateness of the models was entered into artificial network models. Finally, predictad results were compared to the experimental data to evaluate validity. Confronted with high-level of validity confirmed that the proposed modeling procedure by BPNN with one hidden layer and five neurons is efficient and it can be expanded for all water/ethylene glycol-based carbon nanostructures nanofluids. Finally, we expanded our data collection from model and could present a fundamental correlation for calculating Nusselt number of the water/ethylene glycol-based nanofluids including graphene or nitrogen-doped graphene. |
| Databáze: | OpenAIRE |
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