This article reviews the critical role of material selection and design in ensuring efficient performance and safe operation of gas turbine engines fuelled by ammonia–hydrogen. As these energy fuels present unique combustion characteristics in turbine combustors, the identification of suitable materials becomes imperative. Detailed material characterisation is indispensable for discerning defects and degradation routes in turbine components, thereby illuminating avenues for improvement. With elevated turbine inlet temperatures, there is an augmented susceptibility to thermal degradation and mechanical shortcomings, especially in the high-pressure turbine blade—a critical life-determining component. This review highlights challenges in turbine design for ammonia–hydrogen fuels, addressing concerns like ammonia corrosion, hydrogen embrittlement, and stress corrosion cracking. To ensure engine safety and efficacy, this article advocates for leveraging advanced analytical techniques in both material development and risk evaluation, emphasising the interplay among technological progress, equipment specifications, operational criteria, and analysis methods.

Lithium-ion batteries (LIBs) are critical for various applications, and effective thermal management is important for their safety, performance, and lifespan. Traditional physics-based modeling of battery thermal behavior is computationally complex and requires detailed parameters. Using data-driven modeling to predict thermal characteristics of batteries offers a promising alternative. This review comprehensively examines the utilization of data-driven methods in predicting LIB thermal behavior and designing battery thermal management systems. It explores commonly used data-driven techniques and focuses on their applications in predicting heat generation, temperature distribution, and cooling performance. Specific data-driven models for battery thermal prediction are presented, with a comparative analysis of their strengths and weaknesses. The review concludes that data-driven models can effectively predict battery thermal behavior, offering computational efficiency compared to physics-based simulations. Future research directions include hybrid data-driven/physical modeling, ensemble modeling, and incorporating explainable artificial intelligence techniques to enhance model interpretability. These advancements will lead to more accurate and interpretable models, contributing to the safe and efficient applications of LIB systems.