Using artificial neural network and quadratic algorithm for minimizing entropy generation of Al2O3-EG/W nanofluid flow inside parabolic trough solar collector

• Published in: Renewable energy Vol. 129; pp. 473 - 485
• Main Authors: Ebrahimi-Moghadam, Amir, Mohseni-Gharyehsafa, Behnam, Farzaneh-Gord, Mahmood
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2018
• Subjects: algorithms; Artificial neural network (ANN); entropy; Entropy generation minimization (EGM); heat transfer; Nanofluid; nanofluids; nanoparticles; neural networks; Parabolic trough solar collector; particle size; prediction; Quadratic optimization algorithm; renewable energy sources; Reynolds number; temperature; Entropy generation minimization (EGM); Artificial neural network (ANN); Nanofluid; Parabolic trough solar collector; Quadratic optimization algorithm
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2018.06.023

Entropy generation minimization approach, quadratic optimization algorithm and artificial neural network (ANN) have been applied to find optimal condition of the turbulent Al2O3-60:40% EG/W nanofluid flow inside the absorber tube of a parabolic trough solar collector (PTSC). A three-input ANN has been employed for predicting optimal volume fraction (ϕopt). The process is carried out for optimizing nanoparticle concentration, nanoparticle diameter, nanofluid average flow temperature and Reynolds number. Results show that the rate of the entropy generation decreases by decreasing volume fraction, increasing particle diameter and increasing average flow temperature. Adding the nanoparticles to the base-fluid increases frictional entropy generation and decreases thermal entropy generation. It causes an improvement in heat transfer but an increase in viscous irreversibility too. Finally, it was observed that for each particle sizes and average flow temperatures, there is a specific amount of optimal volume fraction, ϕopt; which is not dependent on the Re number. There is an optimal volume fraction for all Re numbers at constant particle size and mean flow temperature. Also, the optimum values of nanoparticle size, nanofluid average flow temperature and Reynolds number are found to be 90 nm, 360 K and 4000, respectively. [Display omitted] •EGM approach, QP optimization algorithm and ANN method are applied to optimize nanofluid flow.•Al­2O3-EG nanofluid turbulent flow inside the parabolic trough solar collector is studied.•Optimum volume fraction, particle size, average flow temperature, Re number is determined.•Adding nanoparticles to the base fluid increases frictional entropy generation and decreases thermal entropy generation.