Understanding the impact of environmental factors, particularly seaway, on marine units is critical for developing efficient control and decision support systems. To this end, the concept of wave buoy analogy (WBA), which utilizes ships as sailing buoys, has captured practitioners’ attention due to its cost-effectiveness and extensive coverage. Despite extensive research, real-time sea-state estimation (SSE) has remained challenging due to the large observation window needed for statistical inferences. The current study builds on previous work, aiming to propose an AI framework to reduce the estimation time lag between exciting waves and respective estimation by transforming temporal/spectral features into a manipulated scalogram. For that, an adaptive ship response predictor and deep learning model were incorporated to classify seaway while minimizing network complexity through feature engineering. The system’s performance was evaluated using data obtained from an experimental test on a semi-submersible platform, and the results demonstrate the promising functionality of the approach for a fully automated SSE system. For further comparison of features of low- and high-fidelity modeling, the deficits with the feature transformation of the existing SSE models are discussed. This study provides a foundation for improving online SSE and promoting the seaway acquisition for stationary marine units.

Fuel consumption has always been a matter of concern for ships propulsion. In this research we aim to develop computer models of several containership cargo stacking configurations and discuss an optimal configuration at a constant front wind speed. The paper presents the simulation results by using ANSYS CFX for a 1:4 scale Post- Panamax 9000 TEU containership. The ship is modelled in a cubic domain that contains unstructured mesh with details, in such a way that can demonstrate the influence of the container configuration on wind force. Also the numerical results are verified versus wind tunnel test data. An optimal stack configuration led to about 25% reduction in air resistance. It is proposed that in order to reduce the wind drag force and consequently reduce the fuel consumption and pollutant emissions, empty spaces between the cargo containers and unbalanced cargo distribution over the deck should be inhibited. Also, it is advised to make the cargo distribution on the most forward and aftward deck areas more streamlined.