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

Abstract

Abstract 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 ( ϕ opt ). 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, ϕ opt ; 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.