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In order to deeply study the hydrokinetic energy harnessing of flow induced vibration and provide design basis for industrial application, unsteady RANS method and Spalart-Allmaras turbulence model, combined with dynamic mesh technology, were used to study the vortex induced vibration-galloping coupling separation characteristics. It is found that hard galloping is caused by the separation of VIV-galloping under certain conditions, and is a cognitive deficiency caused by the absence of self-excited galloping branch in the reduced velocity range studied. When the mass ratio increases or the system stiffness decreases to a certain value, the separation of VIV-galloping will occur, which will be aggravated with the increase of mass ratio or the decrease of stiffness. The study also showed that the VIV-galloping coupling phenomenon is essentially controlled by the mass ratio and stiffness of the system. The mass ratio determines the range of upper branch, the smaller the mass ratio, the wider the upper branch. At the same time, the mass ratio and the stiffness determine the natural frequency of the system, and the smaller the natural frequency, the larger the critical reduced velocity of galloping(U c r i * = − 17.12 003 f n , w a t e r + 26.99599). In order to improve the output power of the system, the critical reduced velocity of galloping should not be greater than the maximum reduced velocity of the upper branch, to avoid occurrence of the phenomenon of VIV-galloping coupling separation. • The hard galloping belongs to the VIV-Galloping coupling separation phenomenon. • When the mass ratio increases or the system stiffness decreases to a certain value, the VIV-Galloping coupling separation phenomenon will occur. • The critical reduced velocity of galloping is negatively correlated with the natural frequency of-system(U c r i * = − 17.12 003 f n , w a t e r + 26.99599). • Vortex-Galloping coupling phenomenon is controlled by mass and natural frequency. [ABSTRACT FROM AUTHOR]

A theoretical and numerical investigation has been carried out to understand the exciting force generation mechanism of the pump jet propeller (PJP) with evenly spaced and unevenly spaced rotor blades (ESB and UESB), which operates behind a standard submarine model. Firstly, the exciting force of ESB was studied by the Reynolds-averaged Navier–Stokes (RANS) method. Numerical methods were validated by calibration model tests and independent studies. The results show that the two main components exist in exciting force of the rotor: symmetric (f = a BPF, a is a natural number) and asymmetric (f ≠ a BPF) exciting forces, and the formation mechanisms of these exciting forces are explained by the interference theory. Then, the exciting force equations of UESB were derived, and the optimal modulation amplitude (S) of UESB was obtained as 4.6°. The numerical results have validated the theoretical prediction that the exciting force of the UESB is reduced by 12.6% at blade passing frequency (BPF). Finally, an optimization process for UESB based on theoretical prediction and numerical simulation was proposed, in which the efficiency of optimization was improved when the theoretical method was introduced. The research is beneficial for comprehensive understanding of the exciting force of PJP and practical engineering design of noise-reduced PJPs. • Explanation for the formation mechanism of asymmetric exciting force. • Investigation on the exciting force characteristics of unevenly spaced blades. • A theory-based accelerating optimization method for unevenly spaced blades. [ABSTRACT FROM AUTHOR]

Accurate and reliable tidal current forecasting plays a key role in application of grid-connected tidal current power generation systems. Hence, a novel tidal current prediction method based on a hybrid machine learning method is proposed in this paper. Compared with previous tidal current prediction method, the proposed tidal current prediction method considers the influence of turbulence on multi-layer tidal velocity difference. A hybrid machine learning method based on hierarchical extreme learning machine(H-ELM) and long short-term memory (LSTM) is adopted to reduce effect of the turbulent flow, which enables to increase the accuracy of the prediction result. The tidal data that collected by using acoustic doppler current profiler (ADCP) in Zhejiang province China is used to validate the effectiveness of the proposed method. The results of the case study show that the proposed method has higher accuracy than results from standard harmonic analysis using UTide (a freely available academic research product). • A novel tidal current prediction model considering the multilayer difference caused by the turbulent flow is proposed. • A hybrid machine learning method is adopted to reduce effect of the turbulent flow and increase the prediction accuracy. • Both H-ELM and LSTM methods are applied in the short-term tidal current velocity prediction. [ABSTRACT FROM AUTHOR]