ASDL 연구팀, Ocean Engineering 학술지 논문 게재
ASDL 연구팀에서 Ocean Engineering 학술지에 투고한 논문이 2023년 10월자로 게재되었습니다. 축하합니다!
Title: Computational investigation on the non-isothermal phase change during cavitation bubble pulsations
Author: Kyungjun Choi, Seonghak Kim, Hyunji Kim and Chongam Kim
Journal: Ocean Engineering
DOI: https://doi.org/10.1016/j.oceaneng.2023.115414
Abstract:
During cavitation bubble pulsations, a phase change intensively occurs near the collapsing moment due to high pressure and temperature inside bubbles, accompanying distinctive flow features: the rate of evaporation and condensation significantly changes according to the phase change regime. To account for this non-isothermal effect, the high-fidelity computational framework incorporating the physics-based cavitation model and a new fluid property model based on artificial neural network is proposed. The key finding of this study is the interplay between the thermal and inertial effects during multiple pulsations. At the early stages of bubble contraction, the phase change is primarily driven by fluid inertia. However, as the bubble continues to compress, the thermal effect becomes dominant and controls the entire phase change region at each moment of collapse. It is observed that the isothermal model relying on the inertial bubble growth rate only, does not capture this transition of dominance and eventually fails to predict multiple pulsations. The physics-based cavitation model successfully captures the bubble pulsation beyond the second collapse. These findings highlight that explicit consideration of the non-isothermal effect is essential for problems with varying phase change regimes, and a phase change model reflecting this effect is vital for accurate computations.
Title: Computational investigation on the non-isothermal phase change during cavitation bubble pulsations
Author: Kyungjun Choi, Seonghak Kim, Hyunji Kim and Chongam Kim
Journal: Ocean Engineering
DOI: https://doi.org/10.1016/j.oceaneng.2023.115414
Abstract:
During cavitation bubble pulsations, a phase change intensively occurs near the collapsing moment due to high pressure and temperature inside bubbles, accompanying distinctive flow features: the rate of evaporation and condensation significantly changes according to the phase change regime. To account for this non-isothermal effect, the high-fidelity computational framework incorporating the physics-based cavitation model and a new fluid property model based on artificial neural network is proposed. The key finding of this study is the interplay between the thermal and inertial effects during multiple pulsations. At the early stages of bubble contraction, the phase change is primarily driven by fluid inertia. However, as the bubble continues to compress, the thermal effect becomes dominant and controls the entire phase change region at each moment of collapse. It is observed that the isothermal model relying on the inertial bubble growth rate only, does not capture this transition of dominance and eventually fails to predict multiple pulsations. The physics-based cavitation model successfully captures the bubble pulsation beyond the second collapse. These findings highlight that explicit consideration of the non-isothermal effect is essential for problems with varying phase change regimes, and a phase change model reflecting this effect is vital for accurate computations.
