Discovering two general characteristic times of transient responses in solid oxide cells.

A comprehensive understanding of the transient characteristics in solid oxide cells (SOCs) is crucial for advancing SOC technology in renewable energy storage and conversion. However, general formulas describing the relationship between SOC transients and multiple parameters remain elusive. Through comprehensive numerical analysis, we find that the thermal and gaseous response times of SOCs upon rapid electrical variations are on the order of two characteristic times (τh and τm), respectively. The gaseous response time is approximately 1τm, and the thermal response time aligns with roughly 2τh. These characteristic times represent the overall heat and mass transfer rates within the cell, and their mathematical relationships with various SOC design and operating parameters are revealed. Validation of τh and τm is achieved through comparison with an in-house experiment and existing literature data, achieving the same order of magnitude for a wide range of electrochemical cells, showcasing their potential use for characterizing transient behaviors in a wide range of electrochemical cells. Moreover, two examples are presented to demonstrate how these characteristic times can streamline SOC design and control without the need for complex numerical simulations, thus offering valuable insights and tools for enhancing the efficiency and durability of electrochemical cells.

Optimization of Effective Thermal Conductivity of Thermal Interface Materials Based on the Genetic Algorithm-Driven Random Thermal Network Model.

Polymer-based thermal interface materials (TIMs) are indispensable for reducing the thermal contact resistance of high-power electronic devices. Owing to the low thermal conductivity of polymers, adding multiscale dispersed particles with high thermal conductivity is a common approach to enhance the effective thermal conductivity. However, optimizing multiscale particle matching, including particle size distribution and volume fraction, for improving the effective thermal conductivity has not been achieved. In this study, three kinds of filler-loaded samples were prepared, and the effective thermal conductivity and average particle size of the samples were tested. The finite element model (FEM) and the random thermal network model (RTNM) were applied to predict the effective thermal conductivity of TIMs. Compared with the FEM, the RTNM achieves higher accuracy with an error less than 5% and higher computational efficiency in predicting the effective thermal conductivity of TIMs. Combining the abovementioned advantages, we designed a set of procedures for an RTNM driven by the genetic algorithm (GA). The procedure can find multiscale particle-matching ways to achieve the maximum effective thermal conductivity under a given filler load. The results show that the samples with 40 vol %, 50 vol %, and 60 vol % filler loading have similar particle size distribution and volume fractions when the effective thermal conductivity reaches the highest. It should be emphasized that the optimized effective thermal conductivity can be improved obviously with the increase in the volume fraction of the filler loading. The high efficiency and accuracy of the procedure show great potential for the future design of high-efficiency TIMs.