Investigation on performance of an asphalt solar collector: {CFD} analysis, experimental validation and neural network modeling

This paper presents a numerical model, development, and experimental validation of an Asphalt Solar Collector (ASC) which subsequently is modeled by Artificial Neural Network (ANN). In this research, 2 m long galvanized pipes are connected in parallel and embedded into an asphalt slab with 1 m(2) area. The slab is buried in the ground to have thermal heat transfer with adjacent soil and to resemble real conditions. Several experiments are carried out in both warm and cold months of the year with different water flow rates from 9:00 to 17:00. The ASC is modeled with CFD techniques and the desired parameters effect are independently studied based on a reference condition. An ANN model is proposed to expand the parametric study and reduce the high computational cost of numerical modeling. The inputs of the ANN include the design and the operating parameters and the outlet water temperature is considered as the output. These models are capable of evaluating the performance of the ASC at every hour during the day and under different boundary conditions. The parametric study shows that improvement of surface absorptivity and thermal conductivity of asphalt leads to a higher increase of the daily efficiency in August, however, the inlet water temperature has the same effect in November. The maximum water temperature difference and the thermal efficiency of the ASC can reach up to 24 degrees C and 45% in August and 14 degrees C and 35% in November respectively.