Influence of artificial roughness parametric variation on thermal performance of solar thermal collector: An experimental study, response surface analysis and ANN modelling

Abstract

The influence of the attack angle (αa) of the perforated baffles on thermohydraulic performance ηp of a solar thermal collector (STC) has been investigated experimentally. The experimentations have been performed to obtain the Nusselt number (Nurs) and friction factor (frs) by varying Reynolds number (Re) from 5000 to 17,000 and αa from 35° to 65°. The other geometrical parameters of angled perforated baffles are fixed in accordance with previous studies. The relative baffle height HB/HD, relative baffle pitch PB/HB, relative hole position OB/HB and open area ratio β0 are fixed at 0.50, 10, 0.266 and 12% respectively. The STC with roughened angled perforated baffles improves the Nurs and frs by 3.82 and 7.14 times respectively as compared to STC without baffles wall. The angled perforated baffles at αa of 55° provides highest thermo-hydraulic performance at Re of 9000. A thermo-hydraulic performance of 1.94 is obtained for the array of designed parameters. The statistical correlations are established for the Nurs and frs by using experimental data. The correlations obtained for the Nurs and frs predict the experimental results within a variation of ±9.7% and ±5.0% respectively. Central Composite Design (CCD) of thermo-hydraulic performance parameters is carried for the surface response analysis. Modelling of thermal parameters using CCD and Artificial Neural Network (ANN) is done which predict them with good accuracy. The previously studies on STC roughened with various types of roughness show appreciable heat transfer enhancement but very few studies have utilized angled perforated baffles. As per author knowledge, a comprehensive study on the performance analysis of STC roughened with angled perforated baffles using different techniques is not available. The study become novel because in addition to the experimental examination of STC performance, correlations for Nurs and frs from experimental data are developed and modelling of thermal parameters using CCD and Artificial Neural Network (ANN) is also performed.