Although the classic Kelvin-Helmholtz model of aerodynamically driven jet breakup(primary breakup) has been widely employed in engine CFD codes for the last three decades, the model is not generally predictive. This lack of predictive capability points to the likelihood of an incorrect physical basis for the model formulation. As such, there have been more recent spray-model development efforts that incorporate additional sources of jet instability and breakup, including nozzle-generated turbulence and cavitation but predictive capabilities have remained elusive. Meanwhile, it should be noted that modern combustors increasingly operate under low-temperature combustion(LTC) conditions, where ambient densities and aerodynamic forces are much lower than under classical operating conditions. Therefore, further consideration of physical model formulation is needed. The previous literature introduced a new primary atomization modeling approach premised on experimental measurements by the Faeth group, which demonstrate that breakup is governed by nozzle-generated turbulence under low ambient density conditions. In this new modeling approach, termed the KH-Faeth model, two different primary breakup models are combined to allow the hybrid breakup modeling approach, i.e. Kelvin- Helmholtz instability breakup mechanism and turbulence-induced breakup are competed via dominant breakup rate evaluation. In the current work, we implement this hybrid KH-Faeth model within the open-source CFD framework OpenFOAM and validate the model against detailed drop sizing measurements stemming from collaborative experiments between Georgia Tech and Argonne National Laboratory.

In this study, a cold-flow test was conducted under ambient conditions to investigate the impact of key geometrical parameters on the spray characteristics of coaxial injectors. Two types of injectors were examined: shear coaxial and swirl coaxial. The primary geometrical variables considered were the recess length and taper angle. The effects of each geometric parameter on the pressure drop, discharge coefficient, breakup length, and spray angle were analyzed. In the swirl coaxial injector, the recess length and the presence of taper affected the discharge coefficient more than in the shear coaxial injector. In terms of breakup length and spray angle, the shear coaxial injector and the swirl coaxial injector showed different results, due to the combination of the jet or swirl injection of the oxidizer and the geometrical variables of the injector. The breakup length and spray angle of the swirl coaxial injector were superior to those of the shear coaxial injector. It is expected that the swirl coaxial injector will have better combustion performance in hot-firing tests.

Heat pumps play a crucial role in achieving the 2050 Net-Zero targets in many countries worldwide. This study addresses this issue by modifying simulation conditions and selecting relevant cases to determine optimal COP conditions using Simulink. By analyzing the efficiency of heat pump heating, utilizing Simulink models provided by MATLAB, and evaluating economic feasibility, we aim to identify the key factors influencing heat pump performance, including the heat source type, refrigerant, heating set temperature, and piping. Our goal is to establish optimal conditions for maximizing the efficiency of these eco-friendly systems while considering economic factors. In this study, it was confirmed that the heat exchanger flow arrangement of the air-source heat pump using R407c was set to cross-flow, and the diameter of the heat exchanger inner tube was set to 0.95 times the existing size, which was found to be optimal for maximizing efficiency. The COP was approximately 4.07 under these conditions.

An experimental study was conducted on the film boiling of nanofluid droplets at a surface temperature range of 300 to 500℃. The nanofluid was made by mixing pure water with copper oxide powder of diameter of 80 nm. The initial volume of the nanofluid droplet ranged from about 21 to 44 ㎕, and the volume, base diameter, and time were measured during the evaporation process. It was found that nanofluid droplets evaporate faster as the surface temperature increases. Also experimental results showed the droplets evaporate quickly at the beginning of evaporation, but as the volume of the droplets decreases, the evaporation rate gradually slows down, and this trend becomes stronger as the surface temperature increases. In addition, the evaporation rate of nanofluid droplets was slightly faster than that of pure water droplets, this was believed to be because the contact area of nanofluid droplets increased.

This study investigates the application of effervescent nozzles to liquid rockets using a laboratory-scale twin fluids spray test system. Typically, rocket engines using a single liquid propellant control thrust by adjusting the mass flow rate, which is influenced by the spray differential pressure between the fluid pressure at the injector front and atmospheric pressure. As the differential pressure decreases, uneven atomization and combustion instability can occur. To address this issue, this research explores an effervescent nozzle that mixes gaseous and liquid propellants to maintain injection pressure. Experiments were conducted on eight different nozzle types with varying orifice and aerator sizes. The experimental results demonstrated internal pressure variations and differences in spray characteristics based on nozzle design, which were analyzed and discussed. if these injection characteristics are applied in the future, it is expected that they could also be utilized in eco-friendly alternative fuels such as Sustainable Aviation Fuel (SAF).

This study explores the rate of injection (ROI) and injection quantities of a solenoid-type high-pressure injector under varying conditions by integrating experimental methods with machine learning (ML) techniques. Experimental data for fuel injection were obtained using a Zeuch-based HDA Moehwald injection rate measurement system, which served as the foundation for developing a machine learning model. An artificial neural network (ANN) was employed to predict the ROI, ensuring accurate representation of injection behaviors and patterns. The present study examines the impact of ambient conditions, including chamber temperature, chamber pressure, and injection pressure, on the transient profiles of the ROI, quasi-steady ROI, and injection duration. Results indicate that increasing the injection pressure significantly increases ROI, with chamber pressure affecting its initial rising peak. However, the chamber temperature effect on ROI is minimal. The trained ANN model, incorporating three input conditions, accurately reflected experimental measurements and demonstrated expected trends and patterns. This model facilitates the prediction of various ROI profiles without the need for additional experiments, significantly reducing the cost and time required for developing injection control systems in next-generation aero-engine combustors.