Dimpled surfaces have gained increasing attention in recent years for their potential to reduce turbulent skin friction, a capability previously acknowledged for its beneficial implications on heat transfer. As a passive drag reduction method, dimpled surfaces offer significant advantages for marine applications due to their effectiveness and practical applicability. However, despite numerous studies, conflicting opinions and inconsistent drag reduction rates persist in the literature. This paper addresses these ambiguities and offers valuable insights into the effectiveness of dimpled surfaces for drag reduction in fully turbulent flows. We conducted an extensive experimental investigation involving various dimple configurations, including different depth-to-diameter ratios, diameters and orientations, utilising a specialised Fully Turbulent Flow Channel facility and a Particle Image Velocimetry system. Our findings demonstrated that circular dimple geometries, particularly those with low depth ratios, can achieve significant drag reduction of up to 27% as the Reynolds number increases. These results highlight the substantial potential of dimpled surfaces for improving energy efficiency in marine applications, where skin friction accounts for a significant portion of the total drag experienced by large vessels.

For propeller-driven vessels, cavitation is the most dominant noise source producing both structure-borne and radiated noise impacting wildlife, passenger comfort, and underwater warfare. Physically plausible and accurate predictions of the underwater radiated noise at design stage, i.e., for previously untested geometries and operating conditions, are fundamental for designing silent and efficient propellers. State-of-the-art predictive models are based on physical, data-driven, and hybrid approaches. Physical models (PMs) meet the need for physically plausible predictions but are either too computationally demanding or not accurate enough at design stage. Data-driven models (DDMs) are computationally inexpensive ad accurate on average but sometimes produce physically implausible results. Hybrid models (HMs) combine PMs and DDMs trying to take advantage of their strengths while limiting their weaknesses but state-of-the-art hybridisation strategies do not actually blend them, failing to achieve the HMs full potential. In this work, for the first time, we propose a novel HM that recursively correct a state-of-the-art PM by means of a DDM which simultaneously exploits the prior physical knowledge in the definition of its feature set and the data coming from a vast experimental campaign at the Emerson Cavitation Tunnel on the Meridian standard propeller series behind different severities of the axial wake. Results in different extrapolating conditions, i.e., extrapolation with respect to propeller rotational speed, wakefield, and geometry, will support our proposal both in terms of accuracy and physical plausibility.

The potential impact of underwater radiated noise from maritime operations on marine fauna has become an important issue. The most dominant noise source on a propeller-driven vessel is propeller cavitation, producing both structure-borne and radiated noise, with a broad spectrum that covers a wide range of frequencies. To ensure acceptable noise levels for sustainable shipping, accurate prediction of the noise signature is essential, and procedures able to provide a reliable estimate of propeller cavitation noise are becoming a fundamental tool of the design process. In this work, we investigate the potential of using computationally cheap methods for the prediction of underwater radiated noise from cavitating marine propellers. We compare computational and experimental results on a subset of the Meridian standard propeller series, behind different severities of axial wake, for a total of 432 experiments. The results indicate that the approaches employed can be a convenient solution for noise analysis during the design process.

Shipping is responsible for approximately the 90% of world trade leading to significant impacts on the environment. As a consequence, a crucial issue for the maritime industry is to develop technologies able to increase the ship efficiency, by reducing fuel consumption and unnecessary maintenance operations. For example, the marine fouling phenomenon has a deep impact, since to prevent or reduce its growth which affects the ship consumption, costly drydockings for cleaning the hull and the propeller are needed and must be scheduled based on a speed loss estimation. In this work a data driven Digital Twin of the ship is built, leveraging on the large amount of information collected from the on-board sensors, and is used for estimating the speed loss due to marine fouling. A thorough comparison between the proposed method and ISO 19030, which is the de-facto standard for dealing with this task, is carried out on real-world data coming from two Handymax chemical/product tankers. Results clearly show the effectiveness of the proposal and its better speedloss prediction accuracy with respect to the ISO 19030, thus allowing reducing the fuel consumption due to fouling.