Performance analysis of multi-gap V-roughness with staggered elements of solar air heater based on artificial neural network and experimental investigations.

Among all renewable energy sources, solar power is one of the major sources which contributes for pollution control and protection of environment. For a number of decades, technologies for utilizing the solar power have been the area of research and development. In the current research, thermal performance parameters of multi-gap V-roughness with staggered elements of a solar air heater (SAH) are experimentally investigated. The artificial neural network (ANN) is also utilized for predicting the thermal performance parameters of SAH. Experiments were executed in a rectangular channel with one roughened side at the top exposed to a uniform heat flux. A significant rise in thermal efficiency performance was reported under a predefined range of Reynolds number (Re) from 3000 to 14000 with an optimized value of relative roughness pitch ratio (P/e) and relative staggered rib length (w/g) as 12 and 1, respectively. The maximum thermal efficiency was attained in the range from 42.15 to 87.02% under considered Reynolds numbers for optimum value of P/e as 12 and w/g as 1. A multilayered perceptron (MLP) feed-forward ANN trained by the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm was utilized to predict the thermal efficiency (ηth), friction (f), and Nusselt number (Nu). The thermal performance parameters such as P/e, w/g, Re, and temperature at the inlet, outlet, and plate were the critical input parameters/signals used in the ANN method. The optimum ANN arrangement/structure to predict the Nu, f, and ηth demonstrate higher accurateness in assessing the performance characteristics of SAH by attaining the root mean squared error (RMSE) in prediction and the Pearson coefficient of association (R2) of 1.591 and 0.994; 0.0012 and 0.851; and 0.025 and 0.981, respectively. The prediction profile plots of the ANN demonstrate the influence of various input parameters on the thermal performance parameters.