标题：Improving The Thermal Performance Of Flat Plate Solar Air Collector Using Pv Driven Honeycomb Texture Formation And Prediction Of Fpsc Using Mdfa-Ann
摘要：Thermal performance of Flat Plate Solar Collector (FPSC) is low and different techniques are adopted to increase the performance of FPSCs, such as fins, artificial roughness, etc. In this paper, a PV-driven honeycomb texture formation is proposed to improve the thermal performance of a flat plate solar air heater by considering the different system and operating parameters to obtain maximum thermal performance. And also the MDFA-ANN is proposed to predict the thermal performance of the FPSC. PV panels were used to drive an electric fan that provides mechanical air circulation without additional energy supply. The effect of irradiance and PV panel coverage ratio on the overall thermal efficiency of the collector was investigated. The results confirmed that the PV coverage ratio can maximize the overall efficiency and an MDFA-ANN methodology can predict the thermal behaviour of the system under practical conditions.
关键词：Solar Air Collector;Flat Plate Solar Collector (FPSC);Honey Comb Texture;PV panels;Artificial Neural Network (ANN);Solar Radiation Heat flux;Mass flow rate
