About this Research Topic
Latent heat storage (LHS) is a subject that has drawn the attention of researchers for several decades now in various fields of thermal engineering, encompassing thermal energy utilization, building applications, thermal management of electronics and batteries etc. A variety of techniques and heat exchanger types have been developed for efficient transfer of heat from the storage medium to a working fluid that normally carries thermal energy between the storage medium and the heat source or end-use. Numerous studies, both computational and experimental ones have been conducted in the effort of obtaining the most optimal configuration for efficient heat transfer, which comprises optimal geometric dimensions and operating parameters.
LHS, in particular, compared to sensible heat storage, exhibits additional challenges due to the ensuing phase-change processes, involving melting and solidification, which are generally non-reversible due, mainly, to hysteresis phenomena (supercooling), but also greatly affected by the low, in general, thermal conductivity of the phase-change materials (PCM) as well as convective heat transfer in the melt.
As the analysis of the main heat transfer processes, including the liquid-solid phase transition, can be successfully carried out with available Computational Fluid Dynamics (CFD) tools, there is a further need for examining several alternative designs of the relevant storage devices in order to obtain the optimal one. With the progress in mathematical algorithms and computational tools, such optimization procedures can be tackled in a more systematic manner. Thus, the relevant algorithms and tools that are available for that purpose have gradually found in recent years increasing application in the field of thermal energy storage as well, both of the sensible and the latent-heat types.
The proposed topic aims, thus, at gathering together a number of studies based on mathematical and computational approaches that can be employed for the optimization of the various types of heat exchange configurations in latent-heat storage systems.
As far as the various configurations that are available in practice, these may include storage tanks/containers integrating shell-and-tube types of heat exchangers, finned structures (on single or multiple tubes and either radial or longitudinal fins), packed-bed configurations (encapsulated PCM), multiple (cascaded) PCM or other composite systems based on PCM.
As far as the methodologies, these may include multi-objective optimization algorithms, topology optimization, machine learning or deep learning methods, etc., combined or not with CFD and heat transfer modelling of phase-change processes. The optimization criteria can be based on energetic performance, thermodynamic (2nd law) considerations, cost aspects etc.
LHS, in particular, compared to sensible heat storage, exhibits additional challenges due to the ensuing phase-change processes, involving melting and solidification, which are generally non-reversible due, mainly, to hysteresis phenomena (supercooling), but also greatly affected by the low, in general, thermal conductivity of the phase-change materials (PCM) as well as convective heat transfer in the melt.
As the analysis of the main heat transfer processes, including the liquid-solid phase transition, can be successfully carried out with available Computational Fluid Dynamics (CFD) tools, there is a further need for examining several alternative designs of the relevant storage devices in order to obtain the optimal one. With the progress in mathematical algorithms and computational tools, such optimization procedures can be tackled in a more systematic manner. Thus, the relevant algorithms and tools that are available for that purpose have gradually found in recent years increasing application in the field of thermal energy storage as well, both of the sensible and the latent-heat types.
The proposed topic aims, thus, at gathering together a number of studies based on mathematical and computational approaches that can be employed for the optimization of the various types of heat exchange configurations in latent-heat storage systems.
As far as the various configurations that are available in practice, these may include storage tanks/containers integrating shell-and-tube types of heat exchangers, finned structures (on single or multiple tubes and either radial or longitudinal fins), packed-bed configurations (encapsulated PCM), multiple (cascaded) PCM or other composite systems based on PCM.
As far as the methodologies, these may include multi-objective optimization algorithms, topology optimization, machine learning or deep learning methods, etc., combined or not with CFD and heat transfer modelling of phase-change processes. The optimization criteria can be based on energetic performance, thermodynamic (2nd law) considerations, cost aspects etc.
Keywords: Latent Heat, Solid-Liquid phase change, Numerical Modelling, Optimization algorithms, Latent Heat Storage, Computational Fluid Dynamics, Heat Exchange Configuration
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