Experimental study on the heat transfer performance of finned-tube heat exchangers in latent thermal energy storages: Effects of PCM types and operating conditions

Abstract

In this paper, the thermal performance of a new Latent Thermal Energy Storage (LTES) system made of a finned-tube heat exchanger dipped in a paraffinic Phase Change Material (PCM) is studied experimentally and compared to that of a Sensible Thermal Energy Storage (STES), obtained by immersing the same heat exchanger in water. In order to present general results, the influence of the heat exchanger geometry, PCM type and operating conditions on the LTES heat storing capacity and heat transfer rate between PCM and Heat Transfer Fluid (HTF) was assessed. In particular, 1-row and 2-row finned-tube heat exchangers were immersed in two commercially available paraffinic PCMs, characterised by different melting ranges and latent heat capacities. The HTF mass flow rate and HTF inlet temperature were varied during test cycles, and the charging and discharging processes of the TES systems were investigated. The experimental results show that the heat-storing capacity of the LTES can be increased by up to 200 % with respect to that of an equivalent STES during both processes. Even though high values of thermal power during the LTES charging/discharging cycles can be achieved, with a peak of 1900 W, the system thermal performance is reduced compared to that of a STES operated under the same conditions. For HTF flows with Reynolds numbers higher than 2000, the average heat transfer rate between HTF and PCM decreases by up to 40 % with respect to an equivalent sensible storage. However, selecting the optimal system configuration and proper operating conditions, such as the use of a 1-row heat exchanger and low values of the HTF mass flow rate, allows for limiting that penalisation to 5–10 %. The results of the present study emphasise how the LTES configuration, PCM type, and operating conditions strongly influence the system thermal performance. The experimental data can also be used as a benchmark to optimise the design of latent thermal energy storages and validate numerical models.