4.7 Article

CFD analysis of greenhouse heating using flue gas and hot water heat sink pipe networks

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ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2019.104853

关键词

CFD; Fluent; Heat transfer model; Greenhouse heating; Heat sink pipe; Waste heat recovery

资金

  1. Indian Council of Agricultural Research, New Delhi, India

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In this study, CFD analysis has been conducted and compared with already developed heat transfer model (HTM) to predict the heating load generated by (i) flue gas heat sink pipe network (FGHSPN) handling flue gas at 350 degrees C and (ii) hot water heat sink pipe network (HWHSPN) receiving hot water at 65 degrees C for accurately computing the heating requirements in order to determine the effect of each system on the greenhouse temperature rise. The heat was generated using innovatively designed forced draft paddy straw bale combustor (FDPSBC) through combustion of paddy straw biomass bales in single operation of FDPSBC which was coupled with a 100 m(2) area greenhouse. The results of both CFD and HTM have been compared with the experimental results performed during winter night in Ludhiana city (30.56 degrees N latitude, India). The heat released through radiation and free convection was computed through two methods viz; an unstructured mesh with 509,153 elements was used for simulation using CFD. The simulation was 3D pressure-based, transient analysis with activated energy, laminar viscous and surface to surface radiation models without taking into account the effect of incident solar radiation. Simulated results show that the total heat transfer by radiation and convection mode was 8.70 kW and 4.68 kW (total 13.38 kW) respectively resulting in steady-state greenhouse temperature rise of 10.35 degrees C above the ambient air. The CFD simulated results match with heat transfer model results of 12.98 kW (RMSE of 1.5%) and also comparable with experimentally observed temperature profile inside the greenhouse with RMSE of 1% which show the utility of CFD to study greenhouse microclimatic parameters and predict the heating load requirements without using complicated heat transfer equations and mathematical solutions.

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