Effect of Radial Porosity Oscillation on the Thermal Performance of Packed Bed Latent Heat Storage

Owing to its high heat storage capacity and fast heat transfer rate,packed bed latent heat storage(LHS)is considered as a promising method to store thermal energy.In a packed bed,the wall effect can impact the packing arrangement of phase change material(PCM)capsules,inducing radial porosity oscillation.In this study,an actual-arrangement-based three-dimensional packed bed LHS model was built to consider the radial porosity oscillation.Its fluid flow and heat transfer were analyzed.With different cylindrical sub-surfaces intercepted along the radial direction in the packed bed,the corresponding relationships between the arrangement of capsules and porosity oscillation were identified.The oscillating distribution of radial porosity led to a non-uniform distribution of heat transfer fluid(HTF)velocity.As a result,radial temperature distributions and liquid fraction distributions of PCMs were further affected.The effects of different dimensionless parameters(e.g.,tube-to-capsule diameter ratio,Reynolds number,and Stefan number)on the radial characteristics of HTF and PCMs were discussed.The results showed that different diameter ratios correspond to different radial porosity distributions.Further,with an increase in diameter ratio,HTF velocity varies significantly in the near wall region while the non-uniformity of HTF velocity in the center region will decrease.The Reynolds and Stefan numbers slightly impact the relative velocity distribution of the HTF-while higher Reynolds numbers can lead to a proportional improvement of velocity,an increase in Stefan number can promote heat storage of the packed bed LHS system.

Exploring high thermal conductivity polymers via interpretable machine learning with physical descriptors

The efficient and economical exploitation of polymers with high thermal conductivity(TC)is essential to solve the issue of heat dissipation in organic devices.Currently,the experimental preparation of functional polymers with high TC remains a trial-and-error process due to the multi-degrees of freedom during the synthesis and characterization process.Polymer informatics equips machine learning(ML)as a powerful engine for the efficient design of polymers with desired properties.However,available polymer TC databases are rare,and establishing appropriate polymer representation is still challenging.In this work,we propose a high-throughput screening framework for polymer chains with high TC via interpretable ML and physical feature engineering.The hierarchical down-selection process stepwise optimizes the 320 initial physical descriptors to the final 20 dimensions and then assists the ML models to achieve a prediction accuracy R2 over 0.80,which is superior to traditional graph descriptors.Further,we analyze the contribution of the individual descriptors to TC and derive the explicit equation for TC prediction using symbolic regression.The high TC polymer structures are mostlyπ-conjugated,whose overlapping p-orbitals enable easy maintenance of strong chain stiffness and large group velocities.Ultimately,we establish the connections between the individual chains and the amorphous state of polymers.Polymer chains with high TC have strong intra-chain interactions,and their corresponding amorphous systems are favorable for obtaining a large radius of gyration and causing enhanced thermal transport.The proposed data-driven framework should facilitate the theoretical and experimental design of polymers with desirable properties.