Experimental and Artificial Neural Network (Ann) Simulation of a Solar Cavity Collector
Lakshmipathy.B1, Sivaraman.B2, Senthilkumar.M3, Kajavalli.A4, Krishnan.S5

1Lakshmipathy.B, Professor, working in Department of Mechanical Engineering, Annamalai University.
2Sivaraman.B, Prof. & Head, Department of Mechanical Engineering, working in Annamalai University
3Senthilkumar.M, Assistant Professor, Department of Mechanical Engineering working in Annamalai University.
4Kajavalli.A, Associate Professor, working in Department of Mechanical Engineering, Annamalai University
5Krishnan.S, Assistant Professor in the Mechanical Engineering Department of Annamalai University
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 2163-2170 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8579088619/2019©BEIESP | DOI: 10.35940/ijeat.F8579.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Solar cavity collector (SCC) is an improvised version of flat plate solar collector (FPC). A SCC of outer radius 16mm positioned concentrically and placed in a 50 mm metal box. Five numbers of such cavities with a provision of inlet and outlet water pipes has been fabricated and experimented for its optimal performance. This experimental gadget is used to heat the water. As the physical dimensions of solar cavity collector influence the performances of the cavity collector, it includes the comparison of 5 numbers of cavities and 7 numbers of cavities, effect of aperture entry have been taken as investigation parameters in the present study. Inclination angle of the collector, water mass flow rates and mode of flow are the other parameters taken for the present study. Experimental data are trained and tested using Artificial Neural Network (ANN) tool of MATLAB software and ANN simulation results have been validated and verified with the available experimental data. Simulations for other set of variables have been predicted with the developed ANN model.
Keywords: Solar energy, Cavity receivers, Different shapes of cavity, Length to Diameter ratio, Water mass flow rates, ANN.