The alarming rate of climate change accentuates the need to reduce greenhouse gas (GHG) emissions produced from anthropogenic activities and consequently the consumption of fossil fuels. The transportation sector is one of the most energy-demanding activities, consisting around 27% of the global primary energy demand and one of the major contributors of GHG emissions to the atmosphere, while shipping transportation accounts for nearly 12% of its CO2 emissions. Decarbonization is vital for emission mitigation using innovative technologies, policies, and incentives at a local and international level. In this context, the presented Integrated Ship Energy Flowchart (ISEF), aims to create a digital twin of a ship and carry out deterministic calculations, such as engine power requirements and by extension fuel consumption and emissions, by modelling the various components of a ship’s energy flow. Most modeling approaches depend on tracking data from automatic identification systems (AIS) and commercial vessel databases, accompanied with prohibitive costs for many, as well as missing vessel characteristics. ISEF, on the other hand, aims to fill in the gap in case of missing or costly to obtain data while maintaining the flexibility to utilize field data if available. This is done by providing representative vessel characteristics, detailed engine modeling and simulating components such as environmental conditions (sea-state, wind). At the same time, ISEF develops a library of vessel data including ship particulars, engine and route information among others. Thus, it is also suitable for the validation of tracking information and machine learning or other deterministic algorithms. Additionally, this library will enable the development of a statistically representative ship describing the international fleet. This will therefore improve projection algorithms utilized in calculations and aid the evaluation of mitigation options regarding decarbonisation in terms of the overall fleet. Such a model also enables the investigation of alternative fuels and fuel mixtures, route optimization, and inclusion of cold ironing amongst others. The current objectives include the validation of the core modelling implementation via comparisons with available raw data to serve as a reference case and build the necessary libraries. Therefore, a case study of a specific ship utilizing real navigational data will be used to demonstrate the capabilities of the algorithm.

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