1. Introduction
A fully cushioned hovercraft (hereinafter referred to as “hovercraft”) is a water surface vehicle that uses air cushions as the support to hover over water. Owing to their high speed and low resistance, hovercrafts have become an important type of vessel for ocean exploration, maritime rescue, and military operations [
1]. It is an irreplaceable and important piece of equipment for water surface transportation. Its working principle is to use the lift fan on the vessel to generate airflow that passes through the air ducts and flexible annular nozzles (referred to as “fingers”) around the bottom of the flexible skirt. This airflow is then directed into the air chambers enclosed by the lower deck, the skirt, and the water surface. The presence of the skirt structure means that the motion and wave load of the hovercraft depend significantly on the response properties of the craft, which differ markedly from traditional surface displacement vessels and require further study.
The prediction of wave loads on hovercrafts is critical for structure design and safety, but the complexities arising from air-solid-liquid interactions make developing numerical methods for estimating motion and wave loads challenging. Even when using scale models for testing, the number of sensors available for measuring physical quantities is severely limited due to the weight and distribution restrictions of the models. Furthermore, the continuous variations in load and response within this complex system make it difficult to observe key physical quantities such as air chamber flow rates and flow coefficients [
2]. Given the limited number of sensors available, appropriate methodologies must be employed to ensure the accuracy and reliability of the measurements.
Significant advancements have been made in the study of wave loads on hovercrafts, particularly in analytical formulations and numerical simulations. In the 1970s, Reynolds [
3,
4] paved the foundation for the dynamic modeling of a hovercraft with linear motion equations. Based on Reynolds’ work, Doctors et al. [
5] extended the motion theory from the frequency domain to the time domain, which significantly facilitated the derivation of nonlinear differential equations describing hovercraft motion in waves. This advancement enabled the description of hovercraft motion at any given time and, for the first time, accounted for the nonlinear effects of the fan, air duct, and skirt.
In recent decades, numerical simulation techniques, particularly Computational Fluid Dynamics (CFD), have significantly enhanced the accuracy in predicting hovercraft behavior in complex marine environments. Milewski et al. [
6,
7] developed the ACVSIM program based on the Immersed Boundary Method (IBM) to provide numerical support for the U.S. Navy’s LCAC hovercraft or landing craft. Xu et al. [
8] conducted a numerical study on the pressure response characteristics of hovercraft skirt cushion systems, particularly those with finger-type skirts, proposing a method for analyzing these characteristics and the influence of the skirt’s geometric parameters and cushion parameters on pressure response. Jiang et al. [
9,
10], building upon the numerical model of automotive safety airbags, introduced a numerical simulation method by combining the Control Volume (CV) method and the Arbitrary Lagrangian-Eulerian (ALE) method to analyze the response of airbags under various factors, such as entry speed, loading mass, material properties, and inclination angle. Gu et al. [
11] performed an in-depth analysis of the air cushion wave-making phenomenon of Air Cushion Vehicles (ACVs) under different sailing conditions and its impact on the motion characteristics of the vessel, including investigations into the air cushion wave-making phenomenon, numerical calculation methods, and dynamic coupling models. Petoft et al. [
12] studied the influence of geometric shapes on the lift force of hovercraft and proposed a new formula for estimating air cushion lift based on parameters such as air gap, side gap, intake velocity, and scaling factor.
In almost all existing methods for predicting wave loads on fully cushioned hovercrafts, simplified models are employed for the air cushion structure to reduce complexity. For instance, the fan characteristic formula used to describe the relationship between the cushion pressure and flow rate within the air chamber ignores the dynamic response characteristics of the air cushion interacting with the water and the flexible skirt, making it valid only under certain low-sea-state conditions. Numerical simulations face significant challenges in accurately modeling the gas-solid-liquid coupling dynamics, and strong assumptions and simplifications are typically made in existing numerical studies.
In the past two decades, several experimental studies have been conducted to investigate the behavior of hovercrafts. Pinkster et al. [
13] used a 1:200 scale model to examine the behavior of an air-supported mobile offshore base and found that the mid-ship bending moment was reduced due to the air cushion effect. Ricci et al. [
14] performed an experimental comparative study on the seakeeping performance of four types of skirts on the US LCAC hovercraft; they obtained and analyzed the amplitude-frequency responses of the LCAC in regular waves [
6,
7]. Zhang et al. [
15] conducted segmented ship model tests on a four-chamber hovercraft to determine the wave load characteristics and cushion pressure response characteristics. Liu et al. [
16] performed segmented seakeeping model tests on a similar hovercraft, measuring the motion and wave load response in both regular and irregular waves, and conducted a structural strength assessment based on the wave loads. Xu et al. [
17] analyzed the calculation method of skirt configuration and its impact on the static hovering characteristics on land through segmented skirt configuration tests and static hovering tests with a hovercraft scale model. These studies primarily focused on the motion of the hovercraft in waves and the wave load response. However, since the dynamic flow in the air chamber is difficult to observe, the relationship between the cushion pressure and flow rate in the air chamber remains understudied and unclear.
Model-test-based methods remain the most reliable approach to study the wave load characteristics of hovercrafts. In this study, a comprehensive analysis of the hovercraft cushion pressure, acceleration, motion, and cross-sectional bending moment responses is carried out based on model test data. A kinematic model of a hovercraft is established, and physical quantities are deduced from the test data to validate the accuracy and reliability of the measurements. Through an analysis of the experimental data and numerical calculations, the relationship between the cushion pressure and cushion volume is investigated.
4. Conclusions
Based on the analysis of the results, the following conclusions are drawn. The data indicate that the cushion pressure and mid-ship acceleration reach a local maximum when the wavelength-to-cushion length ratio is approximately 1.6. Additionally, the wave appears to have a significant impact on the wave load when the wavelength-to-cushion length ratio is around 1.2.
When a hovercraft encounters waves, the airflow within the air chamber experiences dynamic inflow and outflow rather than remaining in a static state as it would in a fully enclosed chamber. The cushion pressure is shown to be not only closely related to the chamber volume but also directly influenced by the inlet and outlet airflow. An analysis of the results reveals a complex interdependence among the cushion pressure, cushion volume, and the inflow/outflow dynamics. This suggests that the inherent correlation between the cushion pressure and leakage flow rate can be indirectly inferred through monitoring the changing patterns of cushion pressure and volume. The fitting and calculation methods used in this study demonstrate significant feasibility and practicality for analyzing hovercraft wave loads.
In this study, the variation in volume within the air chamber is estimated based on the vessel’s motion, which might introduce errors. However, due to the technical challenges associated with monitoring both the spatial and temporal variations in the volume and flow rate within the air chamber, the experimental validation and quantification of these errors appear unlikely. The analysis and conclusions presented here are applicable to medium and low sea states, and further studies are required for higher sea states.