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In this work, a numerical evaluation of the melting/solidification performance of phase change material (PCM) filled inside a triplex‐tube latent heat storage unit has been carried out. To enhance the melting/solidification performance, the porous Cu metal foam (MF) was embedded inside PCM (termed as composite PCM). Alternative segments of pure PCM and composite PCM have been allocated in such a way that both the pure PCM and composite PCM occupy the equal annular area (i.e., equal volumes). Influence of increasing number of segments was delineated on the melting/solidification rate, complete melting time, and thermal energy storage/recovery enhancement. The comparisons were drawn with reference to the model having two segments of PCM and composite PCM. The results show that the model containing 64 segments with alternate allocations of PCM and composite PCM has a faster melting/solidification rate than other models. With 32 alternate segments of MF, the full melting/solidification time reduced by 23%/77% with respect to the case with one segment of MF only. The melting/solidification performance gets saturated beyond 32 segments (M‐5) and negligible variation (only ~1%) in the thermal performance was noticed upon further segmentation. Finally, the model M‐5 proved as the best model considering the aspects of augmented melting/solidification rate and associated complexities. Moreover, the heterogeneity of MF applied in 32‐segment model confirmed that the anisotropic MF results in an increased melting rate and leads over other random isotropic distributions of MF.
In this work, a numerical evaluation of the melting/solidification performance of phase change material (PCM) filled inside a triplex‐tube latent heat storage unit has been carried out. To enhance the melting/solidification performance, the porous Cu metal foam (MF) was embedded inside PCM (termed as composite PCM). Alternative segments of pure PCM and composite PCM have been allocated in such a way that both the pure PCM and composite PCM occupy the equal annular area (i.e., equal volumes). Influence of increasing number of segments was delineated on the melting/solidification rate, complete melting time, and thermal energy storage/recovery enhancement. The comparisons were drawn with reference to the model having two segments of PCM and composite PCM. The results show that the model containing 64 segments with alternate allocations of PCM and composite PCM has a faster melting/solidification rate than other models. With 32 alternate segments of MF, the full melting/solidification time reduced by 23%/77% with respect to the case with one segment of MF only. The melting/solidification performance gets saturated beyond 32 segments (M‐5) and negligible variation (only ~1%) in the thermal performance was noticed upon further segmentation. Finally, the model M‐5 proved as the best model considering the aspects of augmented melting/solidification rate and associated complexities. Moreover, the heterogeneity of MF applied in 32‐segment model confirmed that the anisotropic MF results in an increased melting rate and leads over other random isotropic distributions of MF.
Industrial processes often generate substantial amounts of wastewater with significant thermal energy content, which is typically discarded as waste. A promising approach to increase energy efficiency and advance sustainable resource management is waste water heat recovery. Utilizing a phase change material (PCM) to extract waste heat from wastewater and transfer it to cold water is an innovative method that separates the demand and supply of heat, while also integrating storage and transmission within a single heat exchanger (HE). A 3D numerical model of PCM‐based HE is developed and simulated. The thermal behavior of PCM and preheating of cold water are investigated in this study. In order to increase the thermal conductivity of the PCM, fins are strategically positioned. Around 71.13% of melting time is reduced by adding fins. Further, the 10° orientation of the fins is also numerically observed and it is found that it helps to improve natural circulation of molten PCM. Thus, melting time is reduced by 34% compared to the vertical fin. A 3.5°C–4.5°C temperature rise in cold water is obtained with the inclined fin, which is 14.28% higher than the vertical fin model.
This study presents a novel approach to investigating the combined influence of fin position and shape on the constrained melting behavior of phase change material (PCM) within a spherical capsule (S.C.) through numerical analysis. Unlike previous research, which predominantly focused on single fin shapes or positions, this work uniquely explores the impact of double, simple, and easily manufacturable fin shapes. A two‐dimensional computational model employing the enthalpy–porosity method assesses melting behavior, temperature distribution, and PCM flow. Numerous fin shapes, namely rectangular, trapezoidal converging, trapezoidal diverging stepped, inverse stepped, and triangular, are considered in the analysis. The study reports the influence of the location of two identically shaped fins on the thermal performance. The fins' cross‐sectional area and base thickness are kept equal in all cases. The thermal performance of an S.C.‐integrated fin system is evaluated by analyzing various attributes such as total saving in the duration of melting, enhancement ratio, and Nusselt number. The results indicate that the position of the fins has a more significant impact on melting performance than the fin shape. The best performance is achieved when fins are placed in the lower half of the capsule, followed by the center and upper halves, regardless of fin shape. For rectangular fins, shifting the position of the fin from the bottom half to the center increases the melting time by 24.7% and the top half by 68.3%. The shortest melting time of 93 min is observed for lower‐half rectangular fins, followed by center‐placed triangular fins (94 min). This study offers a theoretical foundation for optimizing the performance of different technologies using latent heat thermal energy storage systems such as packed‐bed, cascaded thermal energy storage systems.
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