TY - JOUR
T1 - A multi-scale online method to predict the unsteady pressure of the ventilated cavities around an axisymmetric body
AU - Li, Yipeng
AU - Huang, Renfang
AU - Qiu, Rundi
AU - Wang, Yiwei
AU - Hao, Liang
AU - Liu, Taotao
N1 - Publisher Copyright:
© 2025 Author(s).
PY - 2025/5/1
Y1 - 2025/5/1
N2 - During the underwater motion of an axisymmetric body, unsteady shedding of the ventilated cavity causes severe pressure fluctuations on its downstream surface, affecting the motion stability. A traditional control system relies on monitoring data for posture adjustment, but may fail due to the hysteresis effect. To address this, a multi-scale online method is proposed to predict the unsteady pressure caused by cavitation shedding. This method decomposes the unsteady pressure into two scales: large-scale pressure, predicted using a medium support vector regression (SVR) model, and small-scale fluctuating pressure, predicted via a multi-round online deployment (MROD) method. The MROD method employs an offline-trained double-layer long short-term memory network, iteratively invoked to intermittently incorporate real-time data for advanced predictions. The prediction accuracy and speed of this method are influenced by key hyperparameters, including the input sequence length, output sequence length, real-time interval between time steps, and time step interval between consecutive real-time data inputs. Results show that both MROD and SVR models exhibit high prediction accuracy and robust generalization ability for predicting the small-scale fluctuating pressure and large-scale pressure, respectively. The proposed method achieves weighted mean relative errors below 1% for both interpolation and extrapolation of unsteady pressure, demonstrating its effectiveness in predicting unsteady pressure for axisymmetric bodies under unknown operating conditions. This high-accuracy prediction ensures stable motion of the axisymmetric body in complex marine environments.
AB - During the underwater motion of an axisymmetric body, unsteady shedding of the ventilated cavity causes severe pressure fluctuations on its downstream surface, affecting the motion stability. A traditional control system relies on monitoring data for posture adjustment, but may fail due to the hysteresis effect. To address this, a multi-scale online method is proposed to predict the unsteady pressure caused by cavitation shedding. This method decomposes the unsteady pressure into two scales: large-scale pressure, predicted using a medium support vector regression (SVR) model, and small-scale fluctuating pressure, predicted via a multi-round online deployment (MROD) method. The MROD method employs an offline-trained double-layer long short-term memory network, iteratively invoked to intermittently incorporate real-time data for advanced predictions. The prediction accuracy and speed of this method are influenced by key hyperparameters, including the input sequence length, output sequence length, real-time interval between time steps, and time step interval between consecutive real-time data inputs. Results show that both MROD and SVR models exhibit high prediction accuracy and robust generalization ability for predicting the small-scale fluctuating pressure and large-scale pressure, respectively. The proposed method achieves weighted mean relative errors below 1% for both interpolation and extrapolation of unsteady pressure, demonstrating its effectiveness in predicting unsteady pressure for axisymmetric bodies under unknown operating conditions. This high-accuracy prediction ensures stable motion of the axisymmetric body in complex marine environments.
UR - http://www.scopus.com/inward/record.url?scp=105006992277&partnerID=8YFLogxK
U2 - 10.1063/5.0271025
DO - 10.1063/5.0271025
M3 - Article
AN - SCOPUS:105006992277
SN - 1070-6631
VL - 37
JO - Physics of Fluids
JF - Physics of Fluids
IS - 5
M1 - 053340
ER -