Abstract The task of interfacing sub-simulators in a co-simulation often remains difficult, tedious, and prone to error. Here, we describe how this process, and the validation of the resulting interface connections, can be made simpler, faster, and more reliable. Especially when, as is often the case, several individuals or teams collaborate on modeling and simulating a full system. This is achieved by grouping functional mock-up interface (FMI) variables into types close to the relevant engineering domains using semantics formalized as an ontology. Several benefits, we argue, are gained from this: clearer communication, increased validation potential, and reduced number of interface connections to deal with. The validity and the limitations of our approach are demonstrated with a detailed case study of a real maritime system: A dynamic positioning (DP) hardware controller connected to several independent co-simulation models via 156 variables in 78 connections. The proposed solution greatly reduces the complexities and the error potential related to interfacing such systems. All results including reference implementations are openly available through the open simulation platform.

The Norwegian fishing industry is facing increasing pressure to find more sustainable energy solutions. In response to this, a modeling and simulation based case study of a representative Norwegian pelagic trawler/purse seiner is presented to evaluate the emission reduction potential of transitioning from traditional marine gas oil to alternative fuels. The objective is to begin developing a framework for calculating relevant parameters and key performance indicators, to support more detailed analysis of various fishing activities. A data based simulation model of the fishing vessel is presented, and some potential benefits and drawbacks of switching to some alternative fuels are demonstrated, from an energy perspective. While practical challenges related to this transition were not considered in detail, our results indicate that emission reductions are achievable. We also found that the simulation results aligned closely with real-world measurements, supporting the validity of our models. The study highlights the need for continued research and development of sustainable solutions for the Norwegian fishing industry.

Understanding current environmental conditions is essential for autonomous ships, among which real-time estimation of sea conditions is a key aspect. Considering the ship as a large wave buoy, the sea state can be estimated from motion responses without extra sensors installed. This task is challenging since the relationship between the wave and the ship motion is hard to model. Existing methods include a wave buoy analogy (WBA) method, which assumes linearity between wave and ship motion, and a machine learning (ML) approach. Since the data collected from a vessel in the real world are typically limited to a small range of sea states, the ML method might fail when the encountered sea state is not in the training dataset. This article proposes a hybrid approach that combines the above two methods. The ML method is compensated by the WBA method based on the uncertainty of estimation results, and thus, the failure can be avoided. Real-world historical data from the Research Vessel Gunnerus are applied to validate the approach. Results indicate that the hybrid approach improves the estimation accuracy.

Situation awareness is of great importance for autonomous ships. One key aspect is to estimate the sea state in a real-time manner. Considering the ship as a large wave buoy, the sea state can be estimated from motion responses without extra sensors installed. However, it is difficult to associate waves with ship motion through an explicit model since the hydrodynamic effect is hard to model. In this paper, a data-driven model is developed to estimate the sea state based on ship motion data. The ship motion response is analyzed through statistical, temporal, spectral, and wavelet analysis. Features from multi-domain are constructed and an ensemble machine learning model is established. Real-world data is collected from a research vessel operating on the west coast of Norway. Through the validation with the real-world data, the model shows promising performance in terms of significant wave height and peak period.