A particle swarm optimization algorithm for optimization of thermal performance of a smooth flat plate solar air heater

This work is undertaken with an objective to develop and implement a trained particle swarm optimization (PSO) algorithm for prediction of an optimized set of design and operating parameters for a smooth flat plate solar air heater (SFPSAH). The simulation is carried out based on the basis of the algorithm developed for three different cases using the climatic condition data of the city Hamirpur, India situated between (latitude) 31°25′–31°52′ N and (longitude) 76°18′ to 76°44′ E. The final results obtained from this algorithm are compared with experimental results and found to be satisfactory as far as flexibility, speed and global convergence are concerned.

► This research work proposes an algorithm based upon trained particle swarm optimization.
► It explores set of optimized design and operating parameters of SFPSAH for maximum thermal performance.
► Effect of Reynolds number, number of glass plate and irradiance on thermal performance of SFPSAH is adjudged quite satisfactorily.
► The results derived from PSO algorithm are validated against actual experimental values for among its robustness and versatility.