Super-resolution reconstruction of propeller wake based on deep learning DOI
Changming Li, Bingchen Liang,

Yingdi Wan

и другие.

Physics of Fluids, Год журнала: 2024, Номер 36(11)

Опубликована: Ноя. 1, 2024

This paper proposes a super-resolution (SR) reconstruction method based on deep learning, which efficiently reconstructs the global high-resolution wake flow field from low-resolution (LR) data of propeller. The extensive for propeller under various operating conditions are generated using numerical simulations delayed detached eddy simulation model. proposed approach, convolutional neural networks (PSCNN), uses dilated module to capture multi-scale spatial characteristics fields. performance SR is evaluated by improving resolution different scaling factors, and its superiority demonstrated comparing accuracy with that two other typical methods. results indicate PSCNN can effectively improve 32 times, an overall mean relative error three velocity components being less than 4.0%, reconstructed agrees well ground truth in distribution variation. Furthermore, reconstruct reasonable unseen conditions, further proving generalizability model capturing relationships wake. Overall, has significant applications obtaining snapshots fluid experiments.

Язык: Английский

Investigation of bubble interaction and influence on acoustic signals DOI

Haoyang Qi,

Jingting Liu, Xinyu Sun

и другие.

Physics of Fluids, Год журнала: 2024, Номер 36(11)

Опубликована: Ноя. 1, 2024

Bubble flow is widely used in various industrial scenarios. Usually, bubbles often do not exist alone, resulting interactions that affect bubble patterns, even the efficiency of mass and heat transfer or radiation acoustics feature. In this paper, two identical nozzles with adjustable center distance are adopted to study effect interaction on pattern acoustic signal. The results show will change trajectory bubbles. When time interval between larger, domain signal more likely have obvious peaks. weaken vibration intensity bubble, so frequency band distribution uniform, peak value lower. This work great control patterns for passive emission technology.

Язык: Английский

Процитировано

1
Super-resolution reconstruction of propeller wake based on deep learning DOI
Changming Li, Bingchen Liang,

Yingdi Wan

и другие.

Physics of Fluids, Год журнала: 2024, Номер 36(11)

Опубликована: Ноя. 1, 2024

This paper proposes a super-resolution (SR) reconstruction method based on deep learning, which efficiently reconstructs the global high-resolution wake flow field from low-resolution (LR) data of propeller. The extensive for propeller under various operating conditions are generated using numerical simulations delayed detached eddy simulation model. proposed approach, convolutional neural networks (PSCNN), uses dilated module to capture multi-scale spatial characteristics fields. performance SR is evaluated by improving resolution different scaling factors, and its superiority demonstrated comparing accuracy with that two other typical methods. results indicate PSCNN can effectively improve 32 times, an overall mean relative error three velocity components being less than 4.0%, reconstructed agrees well ground truth in distribution variation. Furthermore, reconstruct reasonable unseen conditions, further proving generalizability model capturing relationships wake. Overall, has significant applications obtaining snapshots fluid experiments.

Язык: Английский

Процитировано

1