The dynamic load from slamming is an important issue for the structure integrity of a trimaran hull. The slamming loads on a typical trimaran section, based on both experimental measurements and numerical simulations, are reported in this paper. The impact pressure at crucial positions in both space and time domain, the vertical acceleration of the specimen and the detailed motion of the flow field were monitored in the experiments. The trimaran slamming phenomenon also was simulated by using a modified MPS [Sun Z, Djidjeli K, Xing JT, Cheng F. 2015. Modified MPS method for the 2D fluid structure interaction problem with free surface. Comput Fluids. 122:47–65] method proposed by the author. A thorough comparison of various results with experiment were conducted to validate the numerical model. In the end, the flow field obtained from both high-speed camera images and numerical simulations were used to analyse the extreme values and distributions of the slamming load on the hull.

As a Lagrangian particle method, the moving particle semi-implicit (MPS) method has demonstrated distinct advantages in addressing problems involving large deformations of free surfaces and interfaces. This paper comprehensively reviews the MPS method, including its development, advancements, and applications within ocean engineering. The article focuses on the crucial aspects—stability, accuracy, and efficiency—that affect the application of numerical methods. Additionally, it summarizes an overview of the latest developments and technological frameworks for multiphase flow and fluid–structure interaction models. Regarding applications, this paper highlights the achievements and challenges of the MPS method in ocean engineering. Finally, the paper discusses the MPS method's current challenges and future research directions, offering valuable insights for advancing its development and application.

This study investigates the application of data-driven modeling techniques for understanding the complex dynamics of pitching airfoils at low Reynolds numbers and high angles of attack. Linear and nonlinear dimensionality reduction methods, namely principal component analysis (PCA) and isometric mapping (ISOMAP), are employed to obtain low-dimensional representations of the flow field. Subsequently, sparse identification of nonlinear dynamics (SINDy) is utilized to model the governing equations. The key findings are as follows: PCA primarily captures linear information, with the first two to three dimensions maintaining relatively low reconstruction errors. In contrast, ISOMAP excels in capturing nonlinear features, exhibiting noticeably smaller reconstruction errors. The main information is concentrated in the two-dimensional plane constructed by PCA1 and PCA2 (or ISOMAP1 and ISOMAP2). Differences in trajectory planes formed by combinations of other axes reflect flow field disparities. ISOMAP provides a nonlinear low-dimensional representation, advantageous for capturing nonlinear relationships between flow field characteristics and governing equations. The combination of ISOMAP and SINDy yields virtually no errors in identifying governing equations. Conversely, PCA and SINDy result in significantly different linear trajectories, leading to higher reconstruction errors. The identified governing equations using ISOMAP and SINDy remain consistent across different datasets, demonstrating the method's stability and robustness in accurately characterizing flow field properties under similar conditions.