Effect of pitch and hole-hydraulic depth of perforated V-shaped baffles on the heat transfer and thermohydraulic performance of an air heater duct

Abstract

The introduction of V-shaped or inclined perforated baffles in a heat exchanger duct influences the flow dynamics significantly and enhances its thermal performance. The appropriate spacing and perforation shape of baffles further improve the heat transfer mechanism. This work aims to experimentally compare the thermal attributes of a smooth air heater duct with those by incorporating perforated V-shaped baffles with staggered non-circular openings of a constant perforation ratio in the flow passage. The effects obtained on the Nusselt number (Nu), friction factor (f) and the thermohydraulic performance parameter (η) of the duct are studied under the turbulent airflow Reynolds number (Re) range of 5500 to 15,000 by modifying three different values of pitch ratio (p/H) and hole-hydraulic depth-to-baffle height ratio ( ${\mathrm{d}}_{\mathrm{h}}/\mathrm{e}$ ) for both V-down and V-up arrays. The investigation also encompasses developing the correlations of criteria Nu, f and η. The results show that the Nu ameliorates by increasing ${\mathrm{d}}_{\mathrm{h}}/\mathrm{e}$ at reduced p/H, whereas the f value increases by reducing the hole-hydraulic depth. The maximum Nusselt number ratio ( $\mathrm{Nu}/\mathrm{N}{\mathrm{u}}_{\mathrm{s}}$ ) obtained for V-down and V-up baffles, respectively, are 3.36 and 3.58 at p/H of 1 and ${\mathrm{d}}_{\mathrm{h}}/\mathrm{e}$ of 0.84 with the Re range of around 13,600. The V-down and the V-up array at p/H of 1 shows the maximum friction factor ratio ( $\mathrm{f}/{\mathrm{f}}_{\mathrm{s}}$ ) as 11.9 and 13.14, respectively, for ${\mathrm{d}}_{\mathrm{h}}/\mathrm{e}$ values of 0.74 and 0.79 at the extreme Re range. The V-up array at p/H of 3 with increased ${\mathrm{d}}_{\mathrm{h}}/\mathrm{e}$ (0.84) outperforms obtaining the highest η value of 1.67. The correlations developed show good agreement between the experimental and predicted data.