Two methods were compared to predict a ship’s fuel consumption: the simplified naval architecture method (SNAM) and the deep neural network (DNN) method. The SNAM relied on limited operational data and employed a simplified technique to estimate a ship’s required power by determining its resistance in calm water. Here, the Holtrop–Mennen technique obtained hydrostatic information for each selected voyage, the added resistance in the encountered natural seaways, and the brake power required for each scenario. Additional characteristics, such as efficiency factors, were derived from literature surveys and from assumed working hypotheses. The DNN method comprised multiple fully connected layers with the nonlinear activation function rectified linear unit (ReLU). This machine-learning-based method was trained on more than 12,000 sample voyages, and the tested data were validated against realistic operational data. Our results demonstrated that, for some ship topologies (general cargo and containerships), the physical models predicted more accurately the realistic data than the machine learning approach despite the lack of relevant operational parameters. Nevertheless, the DNN method was generally capable of yielding reasonably accurate predictions of fuel consumption for oil tankers, bulk carriers, and RoRo ships.

The shipping industry plays a crucial role in global trade, but it also contributes significantly to environmental pollution, particularly in regard to carbon emissions. The Carbon Intensity Indicator (CII) was introduced with the objective of reducing emissions in the shipping sector. The lack of familiarity with the carbon performance is a common issue among vessel operator. To address this aspect, the development of methods that can accurately predict the CII for ships is of paramount importance. This paper presents a novel and simplified approach to predicting the CII for ships, which makes use of data-driven modelling techniques. The proposed method considers a restricted set of parameters, including operational data (draft and speed) and environmental conditions, such as wind speed and direction, to provide an accurate prediction of the CII factor. This approach extends the state of research by applying Deep Neural Networks (DNNs) to provide an accurate CII prediction with a deviation of less than 6% over a considered time frame consisting of different operating states (cruising and maneuvering mode). The result is achieved by using a limited amount of training data, which enables ship owners to obtain a rapid estimation of their yearly rating prior to receiving the annual CII evaluation.

The authors proposed a direct comparison between white- and black-box models to predict the engine brake power of a 15,000 TEU (twenty-foot equivalent unit) containership. A Simplified Naval Architecture Method (SNAM), based on limited operational data, was highly enhanced by including specific operational parameters. An OAT (one-at-a-time) sensitivity analysis was performed to recognize the influences of the most relevant parameters in the white-box model. The black-box method relied on a DNN (deep neural network) composed of two fully connected layers with 4092 and 8192 units. The network consisted of a feed-forward network, and it was fed by more than 12,000 samples of data, encompassing twenty-three input features. The test data were validated against realistic operational data obtained during specific operational windows. Our results agreed favorably with the results obtained for the DNN, which relied on sufficiently observed data for the physical model.

This study presents an experimentally validated physics- and data-based approach to optimize piston-type actuator amplitudes in shipboard pools for active control of sloshing-induced wave elevations under resonance conditions. The piston actuators, installed at both ends of a pool, are driven periodically to counteract waves generated by the pool's motion. Building upon our previously validated linear numerical method that accurately predicts non-resonant wave dynamics using actuators, we established an efficient framework to determine optimal actuator thresholds. Experimental investigations in two scaled pool configurations at the sloshing laboratory of the University of Duisburg-Essen confirmed the numerical method's effectiveness in identifying critical actuator amplitudes for resonance mitigation, despite inherent limitations in modeling nonlinear wave phenomena. To address nonlinear characteristics in resonant regimes, we implemented a data-driven Kolmogorov-Arnold Network trained on experimental wave profile data. This hybrid method demonstrated exceptional computational efficiency, enabling rapid optimization of wave mitigation systems using piston-type actuators. Experimental results validated both the reliability and the practical feasibility of the proposed hybrid method to optimize piston-type actuator motions for resonance control in confined waters. This work established a baseline method for active sloshing control system design, combining computational efficiency with experimental verification. The proposed approach bridged linear system analysis with nonlinear dynamic responses through synergistic physics-data integration, suggesting pathways for enhanced nonlinear coupling analyses in future research developments.