Stability, thermal performance and artificial neural network modeling of viscosity and thermal conductivity of Al2O3-ethylene glycol nanofluids

• Published in: Powder technology 2020-03, Vol.363, p.360-368
• Main Authors: Li, Long, Zhai, Yuling, Jin, Yajuan, Wang, Jiang, Wang, Hua, Ma, Mingyan
• Format: Article
• Language: English
• Subjects: Aluminum oxide; ANN model; Artificial neural networks; Convective heat transfer; Ethylene; Ethylene glycol; Heat conductivity; Heat transfer; Nanofluids; Neural networks; Particle size distribution; Size distribution; Stability; Thermal conductivity; Thermal performance; Viscosity
• ISSN: 0032-5910; 1873-328X
• DOI: 10.1016/j.powtec.2020.01.006

The aim is to estimate the stability of Al2O3-ethylene glycol (EG) nanofluids using the particle size distribution and velocity ratio. The thermal conductivity and viscosity were measured under ultrasonic conditions for various time intervals, mass fraction (from 0 to 2.0 wt%), and temperature range (from 25 to 60 °C). Moreover, various criteria were presented to estimate the thermal performance in the convective heat transfer. Based on different sets of experimental data, new correlations and optimal artificial neural network models (ANN) were proposed. The results showed that Al2O3-EG nanofluids obtained by ultrasonation for 60 min exhibits the most encouraging properties. Moreover, the correlations for the experiment and ANN models can predict these two parameters. However, the ANN model is more precise. It is expected that the results to be useful for other studies of nanofluids stability especially since it recommends suitable selecting criteria based on heat transfer behavior before real applications. [Display omitted] •The stability of Al2O3-EG nanofluids is estimated by size of nanoparticle and velocity ratio.•The heat transfer performance of nanofluids is estimated based on different criteria.•Correlations and ANN model are used to predict viscosity and thermal conductivity.