Analysis of heat pipe solar collector using artificial neural network

Abstract

Artificial neural network (ANN) has been used to analyze effects of L/di, (total length/ inner diam of heat pipe), Lc/Lℯ, (condenser length/ evaporator length), water inlet temperature, collector tilt angle and solar intensity on heat pipe solar collector (HPSC). Heat pipes (5 each) having two different Lc/Lℯ and L/di ratios have been designed, fabricated and used in solar collector absorber. Copper container, stainless steel wick material and methanol as working fluid were used for heat pipes, which are designed to have heat transport factor of around 194 W and 260 W of thermal energy. Experiments were conducted during summer with different collector tilt angles to the horizontal. Collector efficiency, which increases with decrease in L/di ratio and increase in Lc/Lℯ ratio, is due to increase in heat transport factor of heat pipe.