A multi-scale online method to predict the unsteady pressure of the ventilated cavities around an axisymmetric body

Yipeng Li, Renfang Huang*, Rundi Qiu, Yiwei Wang, Liang Hao, Taotao Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number053340
JournalPhysics of Fluids
Volume37
Issue number5
DOIs
Publication statusPublished - 1 May 2025
Externally publishedYes

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