Researchers in South Korea have developed a computational fluid dynamics model to help keep autonomous ships on course despite changing weather and tidal patterns.

The rising popularity of autonomous vehicles has spurred significant research interest in the maritime industry, particularly for the development of maritime autonomous surface ships (MASS). An essential requirement of MASS is the ability to follow a pre-determined path at sea, considering factors such as obstacles, water depth and ship manoeuvrability.

Any deviation from this path, say due to adverse weather conditions, poses serious risks such as collision, contact or grounding incidents. It is thus desirable for autonomous ships to have a mechanism in place for effectively resisting deviations.

Current methods for assessing the path-following performance of autonomous ships, however, rely on simplified mathematical ship models. Unfortunately, these models are unable to capture the complicated interactions between the hull, propeller, rudder and external loads of ships, leading to inaccurate estimates of path-following performance.

Furthermore, in response to the International Maritime Organization’s Energy Efficiency Design Index to reduce greenhouse gas emissions, the Marine Environment Protection Committee has provided guidelines to determine the minimum propulsion power required to maintain ship manoeuvrability in adverse weather conditions.

In light of these guidelines and the need for assessing path-following performance, a multinational team of researchers, led by assistant professor Daejeong Kim from the National Korea Maritime & Ocean University (www.kmou.ac.kr), has recently studied the path-following performance of MASS using a free-running computational fluid dynamics (CFD) model combined with a line-of-sight (LoS) guidance system, at low speeds under adverse weather conditions.

“We employed a CFD model based on a fully nonlinear unsteady Reynolds-averaged Navier-Stokes solver that can incorporate viscous and turbulent effects and the free surface resolution critical to path-following problems, enabling a better prediction of path-following performance,” said Kim.

The findings are available online in the journal Ocean Engineering (www.sciencedirect.com/science/article/abs/pii/S0029801823022448).

The team employed the CFD-based analysis on the popular Kriso container ship model equipped with the autonomous LoS guidance system. The adverse weather conditions were modelled as disturbances from the bow, beam and quartering sea waves, and these three cases were studied at three different speeds to identify the effect of forward speeds on the path-following performance.

Simulations revealed the ship experienced oscillatory deviations in all the three cases. In the case of the bow and beam waves, these deviations decreased with an increase in propulsion power. Interestingly, in the case of quartering waves, there was a negligible effect of propulsion power on the deviations. Additionally, the heave and pitch responses of the ship were heavily influenced by the direction of the incident waves. Furthermore, in all three cases, the roll amplitudes were consistently below 1.5 degrees. However, the team could not ascertain the effectiveness of increasing speed in improving path-following performance.

“The proposed CFD-based model can provide a valuable contribution to enhancing the safety of autonomous marine navigation,” said Kim. “Moreover, it can also offer low-cost alternatives to model-scale free-running experiments or full-scale sea trials.”

This study establishes a foundation for analysing the path-following performance of MASS at low speeds in adverse weather conditions and could help in ensuring safer autonomous marine navigation.