This review explores a variety of techniques that utilize air injections beneath a vessel’s hull to reduce drag and consequently improve energy efficiency. It focuses on the methodologies of microbubble drag reduction (MBDR), air layer drag reduction (ALDR), and air cavity drag reduction (ACDR), offering insights into their design, operational mechanisms, and potential applications. This review provides a detailed examination of the underlying principles of these technologies, incorporating a blend of experimental research, numerical simulations, and mathematical modelling to offer a comprehensive understanding. It references recent experimental data, highlighting how these findings corroborate with numerical simulations and are further explained through mathematical models. Conclusively, this review accentuates the transformative influence of air injection methods in drag reduction within the maritime industry, emphasizing their pivotal role in boosting operational efficiency, reducing environmental impact, and driving the evolution of naval design and transportation. Through a balanced and detailed analysis, this review provides a holistic view of the current state and future prospects of these innovative resistance reduction strategies.

Ergonomics focuses on the analysis of the interaction between human beings and their working environment. During the riding of a motorbike, ergonomics studies the rider’s posture on the motorbike. An incorrect posture can lead to physical and psychological discomfort, and can affect the perception of risk and the handling of the motorcycle. It is important for motorcyclists to adopt a good riding posture, for their health and road safety. The aim of this work is to propose a fast, cheap, and sufficiently robust method for the 3D reconstruction of the posture assumed by a motorcyclist. The stereo vision and the application of OpenPose made it possible to obtain a 3D reconstruction of the key points, and their evolution over time. The evaluation of the distances between the 3D key points, which represent the length of the various parts of the body, appears to remain sufficiently stable over time, and faithful to the real distances, as taken on the motorcyclist themself. The 3D reconstruction obtained can be applied in different fields: ergonomics, motorsport training, dynamics, and fluid dynamics analysis.

Oscillating Water Column (OWC) systems harness wave energy using a partially submerged chamber with an underwater opening. The Savonius turbine, a vertical-axis wind turbine, is well-suited for this purpose due to its efficiency at low speeds and self-starting capability, making it an ideal power take-off (PTO) mechanism in OWC systems. This study tested an OWC device with a Savonius turbine in an air duct to evaluate its performance under varying flow directions and loads. An innovative aspect was assessing the influence of power augmenters (PAs) positioned upstream and downstream of the turbine. The experimental setup included load cells, Pitot tubes, differential pressure sensors and rotational speed sensors. Data obtained were used to calculate pressure differentials across the turbine and torque. The primary goal of using PA is to increase the C_P–λ curve area without modifying the turbine geometry, potentially enabling interventions on existing turbines without rotor dismantling. Additionally, another novelty is the implementation of a regression Machine-Learning algorithm based on decision trees to analyze the influence of various features on predicting pressure differences, thereby broadening the scope for further testing beyond physical experimentation.

Savonius turbines are widely used in energy recovery applications, including urban-integrated wind energy systems and Oscillating Water Column (OWC) setups for wave energy conversion. This study explores the use of a ducted Savonius turbine. Experimental tests were conducted on a scaled turbine to evaluate its performance. A Computational Fluid Dynamics (CFDs) model, incorporating Sliding Mesh and Dynamic Fluid Body Interaction (DFBI) techniques, was developed to replicate the experimental conditions. The accuracy of the model was confirmed through validation against experimental data. In total, four conditions were studied: one without a Power Augmenter, one with the Bell-Metha Power Augmenter, and two custom ones obtained by increasing the slope at the end of the Power Augmenters. To facilitate rapid turbine characterization, a fast computational method was developed, allowing the derivation of characteristic curves using only three CFD simulations per configuration. The reliability of this approach was assessed by comparing predictions with experimental results. Developing such a model is crucial, as it enables seamless integration with Reduced-Order Models (ROMs), significantly improving efficiency in evaluating multiple operating points. Compared to traditional experimental testing, this approach provides a faster and more efficient way to obtain performance insights, paving the way for enhanced turbine optimization and real-world deployment.