Gary Rosengarten

The thermal management of electrochemical batteries is critical for their safe operation and life-span. This study evaluates the thermal and electrochemical performance of lithium iron phosphate (LiFePO₄) batteries under different operating conditions. Full charge and discharge cycles at standard and maximum rates showed that cell temperatures will increase beyond the recommended range, with discharging causing more heating than charging under the same conditions. Despite this, using thermocouples and thermal imaging we show that cells exhibited an approximately uniform internal temperature distribution, with no sizable localised hot spots. Hybrid pulse power tests across 50–80% state of charge (SOC) showed lower heat generation at higher SOC, indicating reduced internal resistance in this range. Thermal analysis showed that the cell’s effective specific heat capacity was approximately 890 J/kg·K, with cell thermal conductivity anisotropic and ranging from 0.7 to2.7 W/m·K depending on the direction. This is consistent with the way these prismatic cells are manufactured. For convective heat transfer coefficients of approximately 22–32 W/m²·K, the corresponding convection resistance decreased from about 0.63 K/W to 0.43 K/W with increasing airflow, and the enhanced airflow reduced temperature gradients within the cell. The overall uncertainty of the estimated thermal parameters was below approximately 7% across all operating and airflow conditions, demonstrating good reliability of the experimental measurements. The results provide a robust experimental basis for developing, calibrating and validating electro thermal models and optimised thermal management strategies for LiFePO₄ battery modules and packs.

Packed bed thermal energy storage stores heat using solid particles, offering a potentially cost-effective and robust solution for high-temperature applications. One design consideration in configuring such units is maximising heat transfer rate while minimising the pressure drop that contributes to parasitic energy loss. This study investigates the effect on performance of cascading distinct layers containing particles with different sizes within a storage unit. Numerical simulations were conducted to evaluate thermocline behaviour, energy utilisation, and pressure drop during charging and discharging. Results show that whilst smaller particles enhance thermal stratification and increase energy storage density under a controlled outlet temperature strategy, they result in higher pressure drop. Introducing smaller particles to the middle section of the unit yielded the best thermo-hydraulic performance, achieving a utilisation efficiency of 23% with an associated normalised pressure drop of 0.68. Configurations with smaller particles at the bottom and top achieved utilisation efficiencies of 19% and 18%, respectively, with normalised pressure drop of 0.67 and 0.69. The effect of varying the thickness of the central small-particle layer was evaluated, identifying an optimal layering ratio of 0.5. Additionally, polynomial and linear correlations were established to predict utilisation efficiency and pressure drop, offering a practical tool for system design optimisation. Overall, the results confirm that particle-size cascading is an effective strategy to improve both thermal and hydraulic performance in high-temperature PBTES units.

Extracorporeal membrane oxygenation (ECMO) is a life-support technique used to treat cardiac and pulmonary failure, including severe cases of COVID-19 (coronavirus disease 2019) involving acute respiratory distress syndrome. Blood clot formation in the circuit is one of the most common complications in ECMO, having potentially harmful and even fatal consequences. It is therefore essential to regularly monitor for clots within the circuit and take appropriate measures to prevent or treat them. A review of the various methods used by hospital units for detecting blood clots is presented. The benefits and limitations of each method are discussed, specifically concerning detecting blood clots in the oxygenator, as it is concluded that this is the most critical and challenging ECMO component to assess. We investigate the feasibility of solutions proposed in the surrounding literature and explore two areas that hold promise for future research: the analysis of small-scale pressure fluctuations in the circuit, and real-time imaging of the oxygenator. It is concluded that the current methods of detecting blood clots cannot reliably predict clot volume, and their inability to predict clot location puts patients at risk of thromboembolism. It is posited that a more in-depth analysis of pressure readings using machine learning could better provide this information, and that purpose-built imaging could allow for accurate, real-time clotting analysis in ECMO components.