Cryogenic Spray Characteristics of a Multi-Slit Type Gas–Liquid Pintle Injector With Different Pintle Tip Configurations
Subeom Heo, Juhwan Jeong, Inho Kim, Dae Hwan Kim, Youngbin Yoon, Bok Jik Lee
This paper describes cryogenic spray characteristics of a multi-slit type throttleable gas–liquid pintle injector with different pintle tip configurations. Spray characteristics including spray angle and pressure drop of cryogenic spray from the pintle injector were measured and analyzed through cold flow experiment using gaseous nitrogen and liquid nitrogen. The experimental results show that the cryogenic spray characteristics of pintle injectors are significantly different from the previously reported atmospheric spray characteristics, including effects of pintle tip configurations.
Numerical analysis of misfire in an automotive lean-burn direct-injection spark-ignition engine
Linus Engelmann, Jongkwon Lee, Bok Jik Lee, Ji-Ho Kim
IF 6.4
Case Studies in Thermal Engineering
Charge dilution strategies, such as exhaust gas recirculation and lean-burn operation, enhance internal combustion engine (ICE) efficiency and reduce NOx emissions. However, strong charge dilution can lead to cycle-to-cycle variation (CCV) and misfires. In-cylinder aerodynamics significantly influence flow evolution and mixture formation, introducing variation in governing flow variables. This study investigates flow and mixture states during ignition and their impact on flame propagation using multi-cycle Large-Eddy Simulation (LES) of an automotive lean-burn direct-injection spark-ignition (DISI) engine. Special focus is given to flow phenomena near the spark plug. Flow fields and equivalence ratios are compared across fast, slow, and misfire cycles during compression to assess differences in flow evolution and mixture formation. Key characteristics, including spark gap velocities, turbulent kinetic energy, and equivalence ratios, are analyzed at the spark plug during ignition. Analysis of the tumble flow evolution shows high tumble intensity in both propagating and misfire cycles; however, in fast cycles, a marked reduction is observed toward the end of compression. Mixture evolution indicates leaner conditions in misfire and slow cycles. At the spark plug location, misfire cycles exhibit the highest flow velocities but the lowest turbulent kinetic energies, whereas fast cycles show the inverse behavior. Spray influence on tumble formation is examined, revealing differences in spray cone characteristics and penetration. Misfire cycles exhibit slightly lower penetration depths than fired cycles. These findings provide insights into mitigating misfire in lean-burn DISI engines.
Numerical Investigation of a Supersonic Wind Tunnel Diffuser Optimization
Riccardo Nicoletti, Francesco Margani, Luca Armani, Antonella Ingenito, Chihiro Fujio, Hideaki Ogawa, Seoeum Han, Bok Jik Lee
IF 2.2
Aerospace
The objective of this study is to enhance the methodology for the design of a supersonic wind tunnel, improving the process with advanced computational techniques. The supersonic wind tunnel is intended to operate within a flight envelope of Mach 2.5 to 4 and altitudes between 18 and 20 km; this study focuses on the operative condition of Mach 3.5. The research is based on computational fluid dynamics, enabling a deeper understanding of fluid flow phenomena that can deteriorate the operability of the wind tunnel. Additionally, a detailed mesh independence study has been conducted to ensure the reliability and robustness of the computational results. These new analyses allowed for a more comprehensive optimization in the state of the art of tunnel geometry and operational conditions, further enhancing the ability to sustain supersonic flow for extended durations. Particular attention was given to the second throat, which plays a crucial role in the overall performance of the facility, especially during the start-up process. Its design has been refined to improve efficiency by reducing the minimum starting pressure.
Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion
M H Mousavi, Caleb Caldwell, Jacky Baltes, Muteb Aljasem, Bok Jik Lee
ArXiv.org
Achieving clean combustion systems is crucial in terms of solving environmental impacts, decarbonization needs and sustainability matters. Traditional combustion modeling techniques via computational fluid dynamics with accurate chemical kinetics face obstacles in computational cost and accurate representation of turbulence-chemistry interactions. Physically Informed Neural Networks (PINNs) as a new framework, merges physical laws with data-driven learning and shows great potential as an alternative methodology. By directly integrating conservation equations into their training process, PINNs achieve accurate mesh-free modeling of complex combustion phenomena despite having limited data sets. This review examines how this approach applies to clean combustion systems while focusing on their impact in aerospace applications including flame dynamics, turbulent combustion, emission prediction, and instability management in propulsion systems. Next-generation aerospace engines rely on PINNs to reduce computational costs while increasing predictive performance and enabling real-time control methods. This analysis concludes by exploring current barriers and future paths, while demonstrating how PINNs can revolutionize sustainable and efficient combustion technologies in aerospace propulsion systems.
