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.

This study presents an improved comprehensive atomization model for a pressure swirl atomizer. The model integrates internal flow predictions, linear instability analysis of a swirling annular liquid sheet, primary atomization sub-model, and droplet velocity sub-model. Measurement data combined with the inviscid theory model predict the internal flow, providing liquid sheet velocity and thickness at the atomizer outlet. The dispersion relation of surface disturbances is obtained through linear instability analysis. A primary breakup predictive model for particle size distribution is constructed based on the wavelength and growth rate within the full unstable wavenumber range of the dispersion relation. Assuming uniform circumferential distribution and a normal distribution of spray angles, the droplet velocity is assigned according to the liquid sheet velocity. The model is implemented into Eulerian–Lagrangian simulations as initial conditions for discrete phase droplets to simulate the spray field. Results show the model can accurately predict the Sauter mean diameter with an error of less than 6% and effectively predicts the spray structure and spray cone angle. The dependency of the model on its parameters is also studied, determining that the values of the ligament constant and dispersion angle have an obvious impact on the prediction of Sauter mean diameter and spray structure.

In aero-engine combustion research, the pursuit of cost-effective and rapid methods for acquiring precise flow fields across various operating conditions remains a significant challenge. This study offers novel insights into the rapid modeling of complex multi-swirling flows, introducing flow-field-based analytical methods to evaluate flow topologies, spray dispersion, ignition dynamics, and flame propagation patterns. A data-driven model is proposed to predict the swirling velocity field inside a multi-swirl combustor, using spatial coordinates and air pressure drops as input features. Particle Image Velocimetry (PIV) experiments under different air pressure drops are performed to generate the necessary flow field dataset. A fully connected deep neural network is designed and optimized with a focus on prediction accuracy, training efficiency, and mitigation of over-fitting. The predicted flow characteristics, including swirling jets, shear layers, recirculation zones, and velocity profiles, align closely with the PIV experimental results. This demonstrates the model’s capability to effectively capture the intricate multi-swirling flow structures and the complex relationships between input parameters and the resulting flow field. Furthermore, the trained model shows excellent generalization capability, accurately predicting flow fields under previously unseen operating conditions. Finally, combustion-relevant characteristics, such as ignition and flame propagation, are successfully extracted and analyzed from the predicted flow fields using the proposed deep learning framework.

*Growing demands for low emission have pushed modern engine combustors to work at fuel lean conditions.* *However, the lean combustion will lead to poor lean blowout performances, which has been an important issue for low emission combustors.* *Furthermore, water is likely to be ingested into aeroengines under conditions of rainfall or hail, increasing the risk of flame blowout and

Stochastic flame particle tracking in conjunction with non-reacting combustor simulations can offer insights into the ignition processes and facilitate the combustor optimization. In this study, this approach is employed to simulate the ignition sequences in a separated dual-swirl spray flame, in...