Lokukaluge Prasad Perera

Shipping efficiency is a critical factor in reducing fuel consumption and emissions in maritime transport. Enhancing propulsion efficiency in maritime transport not only reduces operating costs for shipping companies but also contributes to global sustainability efforts by minimizing the sector’s carbon footprint. To address these challenges, the industry has been exploring various strategies, including advancements in ship design, adoption of novel technologies for energy saving, alternative fuels, digitalization, and improved logistics management. Regulatory frameworks, such as the IMO emissions targets, further drive the push toward greener and more efficient shipping practices. In TwinShip project, the Engine Propeller Combinatory Diagram (EPCD) is developed based on existing domain knowledge through model-based systems engineering and simulations, i.e. Hydrodynamics and Multi-physics simulation, as the initial step towards Digital Twin models for vessels with existing fuel and technologies. Various pilot vessels, including RoRo and cargo (tanker and bulk carrier) ships, are studied addressing any deviations through refined hydrodynamic calculations to minimize uncertainties. The development of analytical models and prediction of power performance for the above ships is presented and the method for calibration is described, supporting their extension to incorporate Energy Saving Devices for enhanced power performance.

The maritime industry is urged to reduce greenhouse gas emissions and improve the energy efficiency of ships. A potential and relatively inexpensive solution is to implement data analytics to identify improvement areas to optimize ships' performance and fuel consumption. This study investigates barriers to data analytics for maritime organizations intending to utilize data as a means of operational enhancement. This study used the DELPHI–best worst method hybrid approach to identify and rank the barriers to data analytics for energy efficiency. The results revealed a total of 20 sub-barriers grouped into five main barriers. These barriers fall into two overarching categories: organizational barriers, including cultural, managerial, and economic, and technological barriers, comprising data management and data analysis. This study also highlights the most critical barriers within each category, revealing inadequate data governance, multiple suppliers needed to implement a comprehensive system and contracts, and restrictive clauses as the dominant barriers that hamper data analytics adoption in the maritime domain.