Experimental Investigations on the Thermal Characteristics of Domestic Convectors

Duncan Gibb, Jack Oliphant, Ross Gary McIntosh, Taimoor Asim () and Aditya Karnik
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Duncan Gibb: School of Engineering, Robert Gordon University, Aberdeen AB10 7GJ, UK
Jack Oliphant: School of Engineering, Robert Gordon University, Aberdeen AB10 7GJ, UK
Ross Gary McIntosh: School of Engineering, Robert Gordon University, Aberdeen AB10 7GJ, UK
Taimoor Asim: School of Engineering, Robert Gordon University, Aberdeen AB10 7GJ, UK
Aditya Karnik: School of Engineering, Robert Gordon University, Aberdeen AB10 7GJ, UK

Energies, 2024, vol. 17, issue 5, 1-16

Abstract: Better understanding of local thermal characteristics of domestic convectors could play a crucial role in reducing energy consumption for space heating and decarbonizing the economy. The current study evaluates the impact of varying water inlet temperature and flowrate on the local surface temperature of domestic convectors through extensive empirical investigations. Experiments are performed using a custom-made test-rig featuring a 400 mm × 600 mm Type 11 convector within a large and well-ventilated environment, minimizing the thermal influence of the surrounding space on the thermal behavior of the convector. Infrared thermography (IR) is used to acquire local surface temperature data for further analysis. Based on the results obtained, it has been observed that the inlet water temperature has a negligible effect on thermal characteristics of the convector while increasing the flowrate substantially decreases the time required for the convector to reach maximum surface temperature. Based on the numerical data, an analytical model for average surface temperature has been developed using multiple variable regression analysis, demonstrating a prediction accuracy of >90% compared with the experimental data. A detailed understanding of the heating behavior exhibited by domestic convectors has led to a better understanding of the local thermal characteristics, while the prediction model can be used to develop machine learning algorithms to install better flow control techniques for efficient space heating.

Keywords: domestic convectors; local thermal characteristics; infrared thermography; surface temperature; multiple variable regression analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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