Steady boundary layer flows around a two-cylinder configuration near a plane wall is investigated numerically. The Reynolds-averaged Navier-Stokes equations are solved using an upwind finite-element method with a $k\text{-}\omega$ turbulence model closure. The numerical model is validated against independent experimental data for flow past a single cylinder near a plane wall. The two cylinders investigated in the present study are structured in the so-called piggyback configuration, in which the small cylinder is placed directly above the large cylinder. The diameter ratio of the small cylinder $\left(d\right)$ to the large cylinder $\left(D\right)$ is set at a constant value of 0.2. Different values of the gap $\left(e\right)$ between the large cylinder and the plane wall and the spacing $\left(G\right)$ between the two cylinders are investigated. The effects of the gap ratio $e∕D$ and the spacing ratio $G∕D$ on the flow around and the hydrodynamic forces on the cylinders are investigated. Four vortex shedding modes are found around the two-cylinder system. It is found that the vortex shedding mode is dependent on the gap and spacing ratios defined in this study. The variations of the hydrodynamic forces with the gap and spacing ratios are also quantified.

In recent years, wave energy has gained attention for its sustainability and cleanliness. As one of the most important parameters of wave energy, significant wave height (SWH) is difficult to accurately predict due to complex ocean conditions and the ubiquitous chaotic phenomena in nature. Therefore, this paper proposes an integrated CEEMDAN-LSTM joint model. Traditional computational fluid dynamics (CFD) has a long calculation period and high capital consumption, but artificial intelligence methods have the advantage of high accuracy and fast convergence. CEEMDAN is a commonly used method for digital signal processing in mechanical engineering, but has not yet been used for SWH prediction. It has better performance than the EMD and EEMD and is more suitable for LSTM prediction. In addition, this paper also proposes a novel filter formulation for SWH outliers based on the improved violin-box plot. The final empirical results show that CEEMDAN-LSTM significantly outperforms LSTM for each forecast duration, significantly improving the prediction accuracy. In particular, for a forecast duration of 1 h, CEEMDAN-LSTM has the most significant improvement over LSTM, with 71.91% of RMSE, 68.46% of MAE and 6.80% of NSE, respectively. In summary, our model can improve the real-time scheduling capability for marine engineering maintenance and operations.